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Journal of Informatics and Virtual Education ISSN 1821 - 7087 UDOM Academic Journals Published by THE UNIVERSITY OF DODOMA Volume 3 Number 1 December, 2015

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Page 1: jive volume 3

Journal ofInformaticsand VirtualEducation

ISSN 1821 - 7087

UDOMAcademicJournals Published by

THE UNIVERSITY OF DODOMA

Volume 3 Number 1December, 2015

Page 2: jive volume 3

Copyright ©2015 JIVE, ISSN 1821-7087 i

The Journal of Informatics

and Virtual Education

Volume 3 Number 1 (2015)

The University of Dodoma

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Copyright ©2015 JIVE, ISSN 1821-7087 ii

Journal of Informatics and Virtual Education

Chief-Editor

Prof. L. Mselle University of Dodoma, Tanzania

Associate Editor

Dr. M. C. Manyilizu University of Dodoma, Tanzania

Editorial Board:

Dr. Fredrick Mtenzi Dublin Institute of Technology, Ireland

Prof. Christopher M. Collins University of Ontario Institute of Technology, Canada

Dr. David Moffat University of Glasgow, Scotland

Prof. Nerey H. Mvungi University of Dodoma, Tanzania

Prof. Aloys Mvuma University of Dodoma, Tanzania

Prof. Justinian Anatory University of Dodoma, Tanzania

Dr. Salehe Mrutu University of Dodoma, Tanzania

Published bi-annually by the College of Informatics and Virtual Education, The University of

Dodoma

ISSN 1821-7087

Volume 3, Number 1 (2015)

Orders to:

The Editor

Journal of Informatics and Virtual Education

The University of Dodoma

P. O. Box 259

Dodoma

Tanzania

Website: www.udom.ac.tz

© Journal of Informatics and Virtual Education

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Copyright ©2015 JIVE, ISSN 1821-7087 iii

Editorial Note

Welcome to Journal of Informatics and Virtual

Education (JIVE) in 2015 for Volume III, Number I.

The journal consists of well reviewed papers in the

fields of Computer Sciences and its applications. On

behalf of the Editorial Board, the editorial team

members, Prof. Leonard Mselle and Dr. Majuto

Manyilizu, thank the authors and anonymous

reviewers for their invaluable contribution to the

success of the journal.

In this issue, the work of Edward Elias and

Justinian Anatory entitled Investigation of public

exposure to radiated electromagnetic energy from

cellular base stations: a case of Dodoma urban

contend that there has been a proliferation of base

station towers in recent years due to the expansion of

mobile telephone networks caused by the increase of

number of mobile subscribers. This has been

accompanied by an increase in the level of

community concern about possible health effects

from the radio frequency (RF) radiation emissions

from antennae mounted on the base station towers.

The paper is a product of the study conducted in

order to measure RF electromagnetic energy (EME)

levels in Dodoma Urban in comparison with the

maximum permitted limit for general public exposure

given by International Commission on Non-Ionizing

Radiation Protection (ICNIRP). The study used

questionnaires and field measurement methods to

obtain both qualitative and quantitative data. The

questionnaire results revealed that there is a great

concern from the public on the EMF radiations health

effects caused by living near cellular mobile phones.

Then measurements of RF EME emission levels from

cellular Base stations were conducted in six sensitive

Dodoma urban locations using the frequency

selective Narda 3006 equipment. The results

indicated clearly that the RF EME emissions from

cellular base stations are several orders of magnitude

below the maximum permitted limit, contrary to the

public perception. The maximum electric field

strength obtained, for example, was 4.0210V/m or

6.59 % of the limit for the general public exposure

given by ICNIRP guidelines. This level shows that

the people living close to BTS are safe from health

hazards which may be caused by EMF exposure.

Alex Mongi and Aloys Mvuma maintain that

mobile industry has experienced a rapid development

of mobile radio access technologies from 1st

generation to 4th

generation. Such technology has

transformed mobile networks into most available and

cheap last mile solution for broadband and internet

connection problems. These findings have been

discussed in their work entitled Exploring major

factors affecting QoE in mobile broadband networks:

A case of mobile subscribers in Dodoma City,

Tanzania. The authors caution that the issue of users’

satisfaction has always been a permanent question

during planning, building and operational phases of

commercial mobile networks. Previous studies

suggest that QoE does not depend on network QoS

only and there is a gap in this area to identify major

factors which can affect QoE in mobile broadband

networks. This study investigates the potential

variables and identifies the major ones which affect

user’s QoE in mobile broadband networks. The

survey approach and factor analysis method was

opted to collect and analyze data from potential

mobile broadband subscribers. They have explored

them by following the service delivery chain of

mobile networks which flows from users, device, and

network, content and business factors. In general, the

authors have realized that issues which are out of

control of a user tend to highly influence users’

satisfaction.

In his work entitled Modelling approach towards

a better understanding of sea surface salinity off the

East Africa coast, Majuto Manyilizu asserts that the

western Indian Ocean, off East Africa, contributes

significantly to socio-economic development of the

countries in the region, and its sea surface salinity

(SSS) shows seasonality. Although the SSS plays a

crucial role on thermodynamic processes and marine

ecosystem in the region, its in-depth and systematic

numerical study on the mean state and seasonal cycle

does not exist due to temporal and spatial sparse

observations as well as lack of clear satellite salinity

measurements. This study applies a regional model to

simulate the mean state and annual cycle of the SSS

as well as the force behind these patterns in the ocean

off East Africa. The model is forced with the monthly

mean Comprehensive Ocean and Atmosphere Data

Sets winds and heat fluxes, and being validated by

the World Ocean Atlas 2009 (WOA2009) data. The

model outputs and that from WOA2009 are in good

agreement. Both latent heat flux and shortwave

radiations appear to contribute significantly on the

SSS patterns in the region. The region with high SSS

to the north reflects strong shortwave radiations

which can be related to strong winds which sweep

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Copyright ©2015 JIVE, ISSN 1821-7087 iv

away clouds leaving the sky clearer and thus,

allowing high shortwave radiation penetration in

addition to strong evaporation. In addition to the

present theoretical and observational analysis in the

region, this understanding using a modeling approach

has facilitated a better understanding of the SSS

patterns and the forcing behind them. Such

knowledge is important for the marine ecosystem, as

well as marine and coastal shipping activities in the

region.

Leyla Liana and Lucian Ngeze in their work

entitled Online teachers communities of practice: A

proposed model to increase professional development

in Tanzania argue that Communities of Practice

(CoP) have been deployed and proved to be very

successful in sharing knowledge in many settings.

The authors contend that in today’s world, people

from different organizations and sectors need to work

together in many ways to achieve a common goal. In

the education sector, CoP have played a key role in

sharing some pedagogical skills among teachers at

different levels. They conclude that the use of

Information and Communication Technology (ICT)

in these CoP has not been deployed to facilitate the

teacher’s professional development.

Nyaura Kibinda, Aloys Mvuma and Anthony

Faustine present the paper entitled Handover

Algorithm for Machine Type Communication in LTE

Network. In this paper, Machine Type

Communication (MTC) which is a new type of data

communication between machines and devices

without human interactions has been discussed. The

Long Term Evolution (LTE) is a recent third

Generation Partnership Program (3GPP) cellular

standard and is a promising technology to support

future MTC data traffic. This paper evaluates two

existing handover algorithms namely A2-A4-RSRQ

and A3-RSRP. Based on the analysis of the optimal

settings of both algorithms, the performances of the

selected algorithms were compared and the results

proved that A2-A4-RSRQ performs better than A3-

RSRP. A2-A4-RSRQ handover algorithm is able to

maintain acceptable throughput and handover delay

as per 3GPP specification.

Paul Loisulie and Leonard Mselle posit that since

adoption and application of ICTs in governance

entails loss of power to the powerful while somehow

empowering the weak, it will always be somehow

resisted. In their research entitled Investigating

barriers to use ICT as a tool for governance in higher

learning institutions (HLIs) in Tanzania, the authors

discuss the depth and breadth of various barriers to

adoption of ICTs in HLIs in Tanzania. They detail on

how fear for loss of power and other challenges are

responsible for the lukewarm attitude towards

adoption of ICTs in HLIs governance.

Carina Titus and Leonard Mselle report that

most active users of Online Social Networks (OSNs)

are individuals aged between 21 and 30 years. In

their research entitled Investigating the viability of

using online social networks as e-learning platforms

in Tanzanian universities, these researchers assert

that the most preferred site is Facebook followed by

Google. The authors maintain that most students have

the required experiences, skills and drive for

effectively using OSNs as educational platforms.

They determined that the type of course that a student

is pursuing has influence on students’ perceptions,

attitudes as well as experiences towards the use of

OSNs as e-learning platforms.

The main objective of JIVE is to involve papers in

diversity areas covering the field of computer

sciences and its applications. With this journal issue,

the editorial team members anticipate that the papers

will significantly contribute to academic research

progress and policy makers in the country and region.

Prof. Leonard Mselle

Chief Editor

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Copyright ©2015 JIVE, ISSN 1821-7087 v

Table of Contents

1. Investigation of Public Exposure to Radiated Electromagnetic Energy From Cellular Base

Stations: A Case Of Dodoma Urban………………………………………………………1

Edward Elias and Justinian Anatory

2. Exploring Factors Affecting QoE in Mobile Broadband Networks: A Case of Mobile

Subscribers in Dodoma City, Tanzania…………………………………………………9

Alex Mongi and Aloys Mvuma

3. Modelling Approach Towards a Better Understanding of Sea Surface Salinity off the East

Africa Coast……………………………………………………………………………...15

Majuto C. Manyilizu

4. Online Teacher Communities of Practice: A Proposed Model to Increase Professional

Development in Tanzania………………………………………………………………..22

Leyla Liana and Lucian Ngeze

5. Handover Algorithm for Machine Type Communication in LTE Network……………28

Nyaura Kibinda, Aloys Mvuma and Anthony Faustine

6. Investigating Barriers to use ICT as a Tool for Governance in Higher Learning

Institutions (HLIs) in Tanzania………………………………………………………......35

Paul Loisulie and Leonard Mselle

7. Investigating the Viability of Using Online Social Networks as E-Learning Platforms in

Tanzania..………………………………………………………………………………...40

Carina Titus and Leonard J. Mselle

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Copyright ©2015 JIVE, ISSN 1821-7087 1

Abstract— There has been a proliferation of base station

towers in recent years due to an expansion of mobile

telephone networks caused by the increase of number mobile

subscribers. This has been accompanied by an increase in the

level of community concern about possible health effects from

the radio frequency (RF) radiation emissions from antennae

mounted on the base station towers. This paper aimed at

measuring RF electromagnetic energy (EME) levels in

Dodoma Urban and make comparison with the maximum

permitted limit for general public exposure given by

International Commission on Non-Ionizing Radiation

Protection (ICNIRP). The study used questionnaires and field

measurement methods to obtain both qualitative and

quantitative data. The questionnaire results revealed that there

is much of concern from the public on the EMF radiations

health effect caused by living near cellular mobile phones.

Then measurements of RF EME emission levels from cellular

base stations in six sensitive Dodoma urban locations using

the frequency selective Narda 3006 equipment were

conducted. Results indicate that the RF EME emissions from

cellular base stations are several orders of magnitude below

the maximum permitted limit contrary to the public

perception. For example the maximum Electric field strength

obtained was 4.0210V/m or 6.59 % of the limit for the general

public exposure given by ICNIRP guidelines. This level shows

that people living closer to Base Transceiver Station (BTS) are

safe from health hazards which may be caused by EMF

exposure.

Keywords: Cellular Base station, EMF radiation, Public

exposure.

1. INTRODUCTION

URING the second half of the 20th

century the world

underwent an electromagnetic revolution and many

frequencies were used for radio and Television (TV)

broadcasting, radar, mobile phones and for a variety of

wireless devices. The use of communication equipment such

as cellular phone, TV, and Radio has grown to the extent that

a number of people cannot imagine a world without it

anymore.

Mobile cellular networks operations in Tanzania started in

1994 when MIC (Tanzania) Ltd popularly known as Mobitel

by then (currently Tigo) operated a cellular mobile network in

few regions namely Dar-es-Salaam, Zanzibar, Arusha, and

Mwanza. The defunct Tritel-Tanzania Ltd launched its cellular

mobile network in 1995 in two regions namely Dar-es-Salaam

and Zanzibar, with possible extension to other regions by then.

In 1998, Zantel started to serve the purpose on Zanzibar side.

In July 1999, the Tanzanian Government through Tanzania

Communications Commission (TCC) approved an application

by South Africa's Vodacom to operate a mobile phone

network in Tanzania. Vodacom started to operate in 2000

followed by Celtel (currently known as Airtel) in 2001 and

making Tanzania to have five mobile phone operators. Zantel

moved onto the Tanzania mainland in July 2005 and improved

her coverage by entering into a national roaming agreement

with Vodacom Tanzania.

The demand for mobile telephony and Internet since then

has been constantly increasing. It is estimated that the number

of mobile subscription in Tanzania currently is more than

27,428,903, and there are more than 4,000 base stations spread

all over the country to serve the population [19]. Currently,

number of mobile operators in Tanzania is seven namely,

Airtel, Vodacom, Tigo, Zantel, TTCL, Sasatel and Benson

Informatics Limited [13].

Along with this, number of FM radio and TV stations has

also remarkably increased. Tanzania has migrated from analog

to digital system, one of the reason being to create more room

for other mobile, FM and TV operators to get frequency band

to operate. According to [19], there are approximately 87

radio stations and 26 TV stations registered by TCRA up to

date.

The increasing number of cellular telephony, TV, AM and

FM radio users has led to an expansion of communication

networks, with the installation of more base stations. In

addition, many countries now have two or more independent

cellular networks operating within the same geographic

region. The most visible aspect of such networks is the

available of many base station antennas which have been

constructed to provide radio coverage almost everywhere.

They are frequently found near or on shops, homes, schools,

daycare centers, and hospitals. The number of cellular base

stations in a country depends on several factors including the

number of subscribers, the number of networks providers and

the topography [10]. Radio frequency (RF) electromagnetic

radiation from these base stations is regarded as being low

power; however, their output is continuous. This raises the

question as to whether the health of people residing or

working in close proximity to base stations is at any risk [22].

In 1998 the International Commission for Non-Ionizing

Radiation Protection (ICNIRP) published ―Guidelines on

Investigation of Public Exposure to Radiated Electromagnetic Energy From

Cellular Base Stations: A Case Of Dodoma Urban

D

Elias Eduard and Justinian Anatory

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Copyright ©2015 JIVE, ISSN 1821-7087 2

Limiting Exposure to Non-Ionizing Radiation‖ [8]. These

guidelines include frequency-dependent limit values in the

forms: a higher one for protection in the work environment

(occupation) and a lower one for the general public. The

higher values are applied in controlled areas where safety

measures have been taken. These are only accessible to

specially trained personnel. This applies, for example, to

mobile phone antennae sites, for which operators have to

define safe distances. Many countries, however, have their

own national standards, most of which are based on the

ICNIRP limit values, although some specify lower levels for

the limit values.

Some studies have been performed in different countries in

recent years for investigation of the level of EMF radiations.

Example include; Palestine [2], Malaysia [11] and Greece [4].

Very few studies on investigation on EMF radiation levels in

Tanzania have been conducted and have left the public being

unaware of the level of EMF radiations in their living places.

2. METHODOLOGY

2.1 Research Design

This study used a mixed methods design which combined

both quantitative and qualitative approaches for data collection

and data analysis [12]. The qualitative approach involves data

collection using questionnaires. The questionnaires were

provided to residents who live much closer to cellular base

stations in Dodoma urban. Areas where respondents were

involved to attempt questionnaires are: Nkuhungu, Area A,

Uzunguni, Swaswa, Kisasa, Kikuyu, Mnadani, and Idara ya

Maji. The questionnaires were hand delivered and collected to

save time and increase the rate of response.

The quantitative approach involved was field measurement of

EMF radiations from cellular base stations in selected areas

with possibly high level of radiation and very closer to BTS 's.

After the measurement, recording and analysis of data being

done it was followed by data evaluation on whether the levels

of EMF‘s metrics in given areas comply with international

public EMF exposure levels provided in [7].

2.2 Sampling Population and Procedure

In order to conduct field measurement, six places in Dodoma

urban were selected. These areas were elected in accordance

to [21]:

i. Areas with many people who live very close to the

cellular base stations.

ii. Areas with possibility of high levels of radiation due to

availability of several base stations mounted with

multiple antennas.

iii. Sensitive areas like schools, college, and hospital built

close to BTS‘s.

iv. Areas with many respondents complaining high EMF's

radiation

Based on the above criteria, the following places were selected

for field measurement: Nkuhungu two locations ITEGA and

REVOLA, Kizota primary school, Nyerere Square, UDOM at

the school of Social sciences and Kikuyu.

2.3 Frequency selection

In this study the EMF radiation were measured in those

frequency bands, many cellular base stations closer to the

habitants operate as indicated Table 1.

Table 1.Cellular Frequency Bands

S/NO Frequency Band (MHz) Application

1 930 – 960 GSM 900 Down link

2 1805 – 1880 GSM 1800 Down Link

3 2110 – 2170 WCDMA

The device that was applied in the measurement of EMF

electric field from communication towers is frequency

selective State of the Art Narda SRM 3006 equipment. This

equipment is connected with isotropic (probe) antenna, which

is capable of receiving signal in all directions. The antenna

used has a range of 27 MHz to 2.9 GHz. The Narda SRM-

3006 is a system designed to measure the individual

contributions of multiple emitters and to generate a tabular or

spectrum view of the total exposure over the 930 MHz to 2.17

GHz frequency range. The instrument is opted in this study

due to firstly, its availability. Secondly, frequency selective

measurements means that only a narrow part of the spectrum

is measured at each time. Thirdly, in order to perform the

precise compliance, evaluation requires frequency-specific

data so as to weigh the contribution of each source at different

frequencies before summing them.

Figure 1: State of the Art Narda SRM 3006

2.4 Measurement Procedures

The only real-life quantities that can be measured for

assessment of base station emissions are the free-field electric

and magnetic field strengths (E and H). Standards, therefore,

provide derived limits, given in terms of power flux density S

(W/m2) or in terms of E (V/m) and H (A/m).

At the operating frequencies of mobile communications base

stations, the far-field region starts at a quite short distance

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Copyright ©2015 JIVE, ISSN 1821-7087 3

from antenna and, therefore, the measurement of the electric

field strength is, generally, sufficient.

The measurement protocol took into account the objective of

the research which was compliance verification [18], [23]. No

prior knowledge was available regarding sources emissions or

the environment. The general scheme for compliance

verification was not completely followed, since no simulation

was made prior to measurements and no measurement with

broadband field probe was prior made. The measurements

were executed by means of a frequency-selective device, in

locations that theoretically, in ideal propagation conditions,

should fit the maximum field-level prediction. The choice of

measurement points (location and number of points) was in

accordance with the general considerations in [17] and [5].

Processing of measured data and final report followed.

2.4.1 Measurement points

At each site, an initial investigation was conducted using a

spectrum analyzer mode around the BTS to identify points

with maximum exposure according to the procedure defined in

[6]. After identifying the point, the instrument was set ready

for measurement using three spatial points technique defined

in [16]. At least nine locations with different distances from

base station were identified on every site. The point that gave

maximum electric field strength was used in data analysis. The

purposes of initial site survey are:

To ensure that RF field emission from each site does

not exceed the public limits of 2 W/m (as stated in

the International Guidelines) in any area to which the

general public has access outside the site boundary.

To find the location of the Maximum Peak Point

(MPP) around the site in areas accessible by the

general public.

2.4.2 Instrument Settings

Both the Safety Evaluation and Spectrum analysis modes

were applied during field measurement [14]. The ―Safety

Evaluation‖ mode was mostly preferred since the frequency

bands for measurements were already identified.

2.4.2.1 Setting the measurement range (MR)

In most cases, the automatic ―MR Search‖ function is the

quickest way to set the measurement range [14]. However in

this study, the MR was set to 1.8 V/m. This was used as the

low the MR the higher the sensitivity in measurement.

2.4.2.2 Resolution Bandwidth (RBW)

The Resolution Bandwidth (RBW) used was 200 KHz when

measurement was done for a single frequency band e.g.

GSM900 or GSM1800. But measuring three bands needs the

spectrum becomes clearly separated from the noise signal

hence RBW was set to 5MHz. Using the ―Highest Peak‖

marker function, the field strength of the highest spectral line

peak is read off.

2.4.2.3 Setting the frequency range (Span)

It is sensible to restrict the frequency range to the

frequencies of interest to achieve good screen resolution and

fast measurement speeds. The upper and lower frequency

limits were always set to ―Fmin =930MHz /

Fmax=2170MHz‖ to cover the three bands of interest i.e.

GSM 900, GSM1800 and UMTS.

2.4.2.4 Measurement Time

The instrument was set to measure average time of 2-6

minutes as according to [7].

2.4.3 Spatial Averaging

In this study three measurement point technique was

adopted. The antenna of the Narda SRM 3006 was fixed to

1.1m height from the ground and the measurement taken for

an average time of 6 minutes then raised to the 1.5m height

from the ground and likewise data was recorded for the

average time of 6minutes and finally the antenna was raised

and fixed to the height of 1.7m from the ground and took the

reading. At every height before taking new measurement the

instrument was restarted to make sure no other signal

interference from previous reading get into next reading.

The maximum electric fields (V/m) for each of the three

measuring points of each location was recorded and saved in

the Narda SRM 3006 internal memory. The data was later

recorded to calculate the Spatial Average E-Field (V/m).

The formula used to obtain the spatial average of electric field

is as follows:

n2

tot i

i=1

E = E (3.1)

From ICNIRP guideline the reference level for general

public exposure is given by the formula 1.375*√ f in

frequency range 400MHz to 2000MHz. Example, the center

frequency for GSM 900 (range 930 – 960 MHz) is 945 MHz,

and then the reference E-field strength is given as:

V=1.375 945 (3.2)

V = 42.2 V/m (3.3)

To calculate the percentage comparison of measured E-field to

the reference E-field strength (ICNIRP %) the formula below

is applied.

Average spatialx100

ICNIRP Ref

(3.4)

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Copyright ©2015 JIVE, ISSN 1821-7087 4

2.4.4 SRM-3006 TS PC software

This is the software which is used to transfer measured data

from Narda SRM 3006 to computer. The software was

installed to a computer running window 7. The screen shots

which were saved to the memory of Narda equipment was

transferred to the computer for data analysis using the SRM

3006 PC Software following the procedures below;

Save the screen shots under Display on the SRM unit.

Connect the unit to the PC.

Start SRM-3006 Tools.

Click the button to upload the SRM unit‘s memory.

Select the screen shot and save it on the hard disk.

3. RESULTS AND ANALYSIS

The process of field measurement of EMF radiations from

various selected locations was preceded by providing

questionnaires to people living near to BTS. The survey was

to gather information from the community around the base

stations about their perception on the health effect of EMF 's.

Data was collected through questionnaires. Thereafter, the

process of field measurement of EMF was conducted. This

section will explain and discuss in details the results and

analysis of both the qualitative and quantitative data collected

in this study.

3.1. Questionnaire Results

The researchers first wanted to know the level of education

of the participants. This was very important in order for

researchers to assess the level of literacy of respondents. The

distribution of the level of education of the people who

responded to questionnaires is analyzed and represented in

Figure 2.

The researcher also wanted to know the perception of the

respondents on the health effect caused by living near the

mobile base stations. This was the key question in the

questionnaire since the main objective of using this tool was

mainly to get the public concern on the EMF radiations and

the health effect to the people who reside very close to the

mobile base stations. Results on public perception are

indicated in Figure 4. The question was ―if there is any health

effect caused by EMF radiations caused by BTS‖. The

respondents were to choose ―YES‖, ―NO‖ or ―I don‘t know‖.

From data analyzed, 74% chose YES i.e. they believed that

there are exist health effects, 18% chose I don‘t know and 8%

chose NO believing that there is no health effect cause by

EMF radiation from cellular base stations.

Figure 2: Distribution of Education level of public attempted

questionnaire.

Apart from knowing the education level of the respondents

also the researcher wanted to know for how long respondents

have been living in those areas. The period which the

respondents have been living in their respective areas were

distributed as those who have lived for less than 6 months,

between 6 months and 1 year, 1 to 5 years and above 5 years.

The results were analyzed using Microsoft excel and

represented in a bar graph as depicted in Figure 3.

Figure 3: Periods the Participants have lived near the BTS.

From results indicated in Figure 4, it is concluded that the

public perception is positive that the EMF radiation from BTS

cause health effects.

3.2. Field Measurement Results

The data measured from field locations were stored as

screen shots for future analysis. This section provides and

discusses data measured using the Narda 3006 equipment.

Data represents the maximum E-fields that were measured as

depicted in Figure 5, 6, 7 and 8.

Figure 4: Perception on the health effects caused by living

closer to BTS.

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Copyright ©2015 JIVE, ISSN 1821-7087 5

Figure 5: Spectrum analyser screen showing the measurement

performed at Nkuhungu-Revola (930 MHz to 960 MHz).

Figure 5 shows the spectrum analyser screen of the EMF of

GSM900 (frequency range from 930 MHz to 960 MHz)

measurement performed in one of the points at Nkuhungu-

Revola. The results indicate maximum value is 1.013 V/m

approximately 2.4% of the provided ICNIRP limit.

Figure 6: Spectrum analyzer screen showing the measurement

performed at Nyerere Square (930MHz to 2170MHz).

Figure 7: Safety evaluation screen showing the measurement

performed at Nyerere Square in three frequency bands (GSM

900, GSM 1800, UMTS).

Figures 6 and 7 show the screen shot of the spectrum analyser

and Safety Evaluation data respectively measured at Nyerere

Square. The maximum electric field value was 3.101 V/m in

GSM1800 frequency. This value is far below the ICNIRP

safety value of 56.9 V/m.

Figure 8: Safety evaluation screen showing the measurement

performed at UDOM in three frequency bands (GSM 900,

GSM 1800, UMTS).

Measurement that was done at UDOM hostels of School of

Social Sciences provided the maximum value of 4.241 V/m in

UMTS frequency band. However, this value is far below the

safety margin provided in the ICNIRP guidelines of 61V/m

for UMTS.

The data represented in Figure 9 (a) and (b), Figure 10(a)

and (b) and Figure 11 (a) and (b) compare the values which

were measured at six locations with the safety margin

provided by ICNIRP guidelines. The GSM900 is represented

in Figure 9, GSM1800 in Figure 10 and UMTS in Figure 11.

After calculation, spatial averaging of the three points in a

given location as explained in section 3.3 was calculated.

Figure 12 (a) shows comparison of maximum E-field

measured and Figure 12 (b) compares these values with

ICNIRP safety limit value.

3.3. Data Interpretation

The existing data show that the exposure levels were well

below the limits in all examined cases. The highest electric

field strength was 4.0210V/m in UMTS frequency band or

6.59 % of the limit for the general public exposure of the

ICNIRP guidelines. The maximum sum of all the levels in the

GSM 900 band was 1.915V/m or 4.67% of the ICNIRP limits.

The highest level found during these measurements in the

GSM1800 band was 2.5718 corresponded to 4.52 % of the

limit.

As mentioned earlier, the measurements were performed in

six locations and more than 100 readings were taken.

However, only 18 readings with maximum values were

compiled in this study. The objective was to evaluate the

compliance of the measured data to the safety limits thus the

maximum values were appropriate for this.

It can be seen from the results that the exposure levels are

well below the limits of the ICNIRP guidelines in the

examined cases. Apart from this, exposure levels are varying

by orders of magnitude in different locations examined. The

exposure level depends on several factors like the input power

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of the antenna, the type of the antenna, the location of the

examined position in respect to the antenna, several

environmental factors and the distance to a base station.

Figure 9: (a) Maximum E-field measured in different

locations at GSM 900 frequency band, (b) Comparison

between E-fields in (a) with the ICNIRP limit.

Figure 10: (a) Maximum E-field measured in different

locations at GSM 1800 frequency band, (b) Comparison of E-

fields in (a) to the ICNIRP limit.

Figure 11: (a) Maximum E-field measured in different

locations at UMTS frequency band, (b) Comparison of E-

fields in (a) with the ICNIRP limit.

Figure 12: Maximum E-fields measured in six locations at

GSM 900, GSM 1800 and UMTS frequency bands,

(b)Comparison of E-fields in (a) with the ICNIRP limit.

3.4. Analysis

In most cases measurement positions were located in the

living area of people concerned about possible health

consequences due to the exposure next to mobile phone base

stations. Other areas were those where many people spend

most of their time, areas like schools, colleges, and gardens.

The RF exposure levels, at the living area of general public

were collected and evaluated according to the ICNIRP

reference levels. According to the results of the average spatial

point‘s measurements in six locations, it was found, that in

most cases the exposure levels from the base stations were far

below the ICNIRP exposure level for the general public. For

example the maximum Electric field strength obtained was

4.0210V/m or 6.59 % of the limit for the general public

exposure given by ICNIRP guidelines

4. CONCLUSION

This paper focused on investigating the level of EMF

radiation through field measurement for compliance purposes

with ICNIRP guidelines for general public exposure. The

whole process intended to see whether or not people living

near the cellular mobile towers are at any risk.

The field measurements were preceded by providing

questionnaires to the public living in close vicinity of mobile

towers. The results from questionnaires showed that the public

are more concern on the health effect caused by living near the

BTS. They strongly suggested that there are health effects to

the people who live near the BTS‘s.

After the measurement were conducted, the data analysis

shown that the maximum E-field strength recorded at the area

under investigation is 4.0210 V/m which is very low

compared to the ICNIRP reference level for general public

exposure. The percentage exposure at this area is about

6.5918% of the reference level. Other measurements were

very low compared to the ICNIRP guidelines. These results

are contrary to the perceptions of the public on the health

effects to the people living nearby BTS 's. Therefore with this

measurement results obtained in this study, the people living

around BTS 's are safe from health hazards which may be

caused by EMF exposure. In future, it is recommended that

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this study be carried to measure in much detail the areas with

multiple antennas.

Coming up with solid conclusion that there is no health

effect it needs to conduct a long term research that will

involve different groups of objects exposed to different

environments. The methodology of using six minutes as

exposure time is still questionable while people are exposed to

radiations for much longer time. Lastly, recently there have

been large imports of Chinese mobile phones and rapid

growth of mobile usage in many African countries. These

mobile phones some are from fake companies which does not

adhere to international safety standards. People are using them

without knowing there long term health effect. The study

should be done to investigate the health effect of using cellular

mobile phones especially those with no safety standards.

REFERENCES

[1] A. H. Sallomi ―Safety Distance and power Density

Calculations for GSM Communication Systems", Journal

of Engineering and Development, Vol.15, No.2, 2011,

pp.63-73.

[2] A. Mousa, ―Electromagnetic Radiation Measurements and

Safety Issues of some Cellular Base Stations‖ Nablus

Journal of Engineering Science and Technology, 2011,

pp. 35-42.

[3] C. Olivier; and L. Martens, ―Optimal Settings for Narrow-

Band Signal Measurements Used for the Exposure

Assessment Around GSM Base Stations‖, IEEE Trans.

Instrum. Meas. vol. 54, no.1, 2005, pp. 311-317.

[4] C. Sammut, "Non-ionising electromagnetic field exposure

monitoring and measurement campaigns in Malta‖

Biological Effects of EMFs, 4th

International Workshop

Crete, Greece, 2006, pp.758-765.

[5] Electromagnetic Communications Committee (ECC,

2003), Revised ECC Recommendation (02) 04.

Measuring Non-Ionizing Electromagnetic Radiation

(9KHz-300GHz).

[http://www.erodocdb.dk/docs/doc98/Official/Pdf/ECCRe

c 0204.pdf], site visited on 06/04/2013.

[6] G. Atanasova.; G. Angelova; N. Atanasov, "Results of

power density measuring for frequencies between 800

MHz and 3000 MHz in urban area," TELECOM'2008,

Varna, Bulgaria, 2008.

[7] ICNIRP ―Guidelines for limiting exposure to time-

varying electric, magnetic and electromagnetic fields (up

to 300GHz)‖ Health Phys. Soc., vol. 74, no. 4, 1998, pp.

494-522.

[8] ICNIRP ―Guidelines on Limiting Exposure to Non-

Ionizing Radiation‖ International Commission on Non-

Ionizing Radiation Protection, ISBN 3-9804789-6-3,

1999.

[9] ICNIRP “ICNIRP statement on the “Guidelines for

limiting exposure to time varying electric, magnetic, and

electromagnetic fields (up to 300 GHz)” Health Physics

Soc., 97(3): 2009, pp257-258.

[10] I. Gabriela and T.A. Nikolai, ―An investigation of emf

power density distribution from GSM/UMTS base

stations in urban area‖ Higher State School College of

Telecommunications and Post, Sofia, 1700, 1 Academic

Stefan Mladenov, Bulgaria, 2009

[11] I. Rafiqul; O. K. Othman; A. Liakot; A. Amir; Z. Mohd.

―Radiation Measurement from Mobile Base Stations‖,

University Campus in Malaysia. American Journal of

Applied Sciences 3 (4): 2006, pp.1781-1784

[12] J. W. Creswell, Research Design: Qualitative,

Quantitative, and Mixed Methods Approaches, 3rd

Edition, SAGE Publishers, London, 2008

[13] M. Mary and D. Bitrina, ―Tanzania ICT Sector

Performance Review 2009/2010 Towards Evidence-based

ICT Policy and Regulation‖ Volume Two, Policy Paper

11, 2011.

[14] Narda (2011), [Online]. Available: https:

//www.narda.com

[15] Recommendation ITU-T K.52 ―Guidance on complying

with limits for human exposure to electromagnetic

fields‖, 2000.

[16] Recommendation ITU-T K.61 ―Guidance on

measurement and numerical prediction of electromagnetic

fields for compliance with human exposure limits for

telecommunication installations‖, 2008.

[17] Recommendation ITU-T K.83,‖Monitoring field

strengths of electromagnetic fields‖, 2008.

[18] S.M. Mann; T.G. Cooper; S.G. Allen; R.P. Blackwell;

A.J. Lowe, ―Exposure to Radio Waves near Mobile Phone

Base Stations‖, Chilton, Natl. Rad. Protect, Board Report

321, 2000.

[19] TCRA Report for Assessment of EMF Radiation levels in

Tanzania, 2012.

[20] T. S. Rappaport, Wireless Communications Principles and

Practice, Second Edition, Prentice Hall, Upper Saddle

River NJ, 2002.

[21] U. Bergqvist, G. Friedrich, Y. Hamnerius, L. Martens, G.

Neubauer, G.Thuroczy, E. Vogel, and J. Wiart, (2001).

―Mobile Telecommunication Base Stations-Exposure to

Electromagnetic Fields‖, Report of a Short Term Mission

within COST244bis, [Online]. Available:

http://www.cost281 .org/activities/ Short term

mission.pdf.

[22] V.G. Khurana; C. Teo; M. Kundi; L. Hardell; M. Carlberg

―Cellphones and brain tumors: A review including the

long-term epidemiologic data‖. Surg Neurol. 72, 2009

pp205-214.

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[23] W. Müllner; G. Neubauer; H. Haider, ―Add3D, a new

technique for precise power flux density measurements at

mobile communications base stations‖ 10th International

Fachmesse und Kongress für EMV Düsseldorf. 2002, pp.

305-312.

Mr. Elias Eduard is with School of Virtual Education,

College of Informatics and Virtual Education at the University

of Dodoma.

Prof. Justinian Anatory is with School of Informatics,

College of Informatics and Virtual Education at the University

of Dodoma.

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Abstract—Mobile industry has experienced a rapid

development of mobile radio access technologies from the 1st

generation to the 4th

generation, which has transformed mobile

networks into most available and cheap last mile solution of

broadband and internet connection problems. However, the

issue of users‘ satisfaction has always been a permanent

question during planning, building and operational phases of

commercial mobile networks. Previous studies suggest that

Quality of Experience (QoE) does not depend on QoS of

network only, and there is a gap in this area to identify major

factors which can affect QoE in mobile broadband networks.

This study investigates the potential variables and identify the

major ones which affect user‘s QoE in mobile broadband

networks. The survey approach and factor analysis method

was opted to collect and analyze data from potential mobile

broadband subscribers. We have explored them by following

service delivery chain of mobile networks which flows from

users, device, and network, content and business factors. In

general, we learnt that issues which are out of control of a user

tend to highly influence users‘ satisfaction.

Keywords— factor, mobile broadband, prediction, Quality of

Experience

1. INTRODUCTION

1.1 Concept of Quality of Experience

UALITY of experience (QoE) has emerged as a preferred

quality measure of ICT infrastructure and service

performance. It is a reflection of users‘ satisfaction

towards the quality of service delivery process.

Numerous definitions of QoE have been formulated in an

attempt to describe it and its associated factors in an

appropriate context. According to [1], QoE is how a user

perceives the usability of a service when in use, how satisfied

he/she is with a service in terms of usability, accessibility,

retainability and integrity. In view of that, QoE can be

described by variables which fall under four factors namely

usability, accessibility, retainability and integrity. This

definition seems to limit the consideration of other factors

which may significantly affect QoE. In [2], International

Telecommunications Union Recommendation E.800 (ITU

Rec.800) define QoE as the overall acceptability of an

application or particular service as perceived subjectively by

the end users. Both definitions proposed by [1] and ITU-T

Rec.E.800 emphasize the fact that users are central in quality

evaluation of communication networks.

The European Network of quality assessment, Qualinet,

define QoE as the degree of delight or annoyance of the user

of an application or service which is resulted from fulfillment

of his/her expectations with respect to the utility and/or

enjoyment of the application or service in the light of users‘

personality and current state [3]. This definition elaborates

points such as fulfilment and expectation which were not

discussed by [1] and ITU –T Rec. E.800. This implies that

service or application performance is evaluated in terms of

fulfilling users‘ expectation.

European Telecommunications Standards Institute (ETSI)

also defined QoE as a measure of user performance based on

both objective and subjective psychological measures of using

an ICT service or product. This definition suggests that there

are factors which influence users QoE which are divided into

objective and subjective ones. The objective factors are

described by quantifiable network variables whereas

subjective factors are described by non-measurable variables.

From the reviewed QoE definitions, it is observed that

QoE is influenced by many factors in communication

ecosystem. Researchers from both academic and

telecommunications organizations went further with the topic,

trying to find out what are those factors affecting QoE in order

to design its measurement and management mechanisms.

A research conducted by [4] studied factors affecting QoE

in Next Generation Networks (NGN). They argued that, QoE

is a multidimensional concept, which is difficult to be defined

or measured in a simple unified manner, because there are

many factors affecting it. Furthermore, identified QoE

influencing factors as technology performance, usability,

subjective evaluation, expectation and context. They

concluded by stressing the fact that identified factors are not

exhaustive due to the fact that QoE is not only technology

centric but also user centric.

In [5], Noor and Khorsandroo proposed a framework of

QoE in mobile networks. The key factors affecting QoE

proposed in their study are economical issues, Grade of

Service (GOS), Quality of Resilience (QoR), type of user,

device, and Quality of Service (QoS) which is divided into

Application QoS and Network QoS. Another study was also

done by Nguyen to model user QoE of Web services in data

networks, a case of Ethernet technology [6]. The focus of their

study was to investigate on network parameters and user mind

Exploring Factors Affecting QoE in Mobile Broadband Networks: A Case of

Mobile Subscribers in Dodoma City, Tanzania

Alex F. Mongi and Aloys N. Mvuma.

Q

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effects toward QoE. However, it was concluded that more

factors should be considered in their future work.

A study conducted by Laghari and Connelly grouped

factors influencing QoE into two categories i.e. qualitative and

quantitative [7]. Their study focused on factors affecting QoE

in wireless environment based on multimedia applications.

The major work done was to evaluate the influence of network

parameters and content characteristics over user perceived

quality. Three studies presented in by [8] and [9] grouped QoE

influencing factors in three categories namely human, system

and context. The study focused on three categories which tried

to accommodate many parameters which might influence

users‘ QoE. The parameters grouped under human influencing

factors are demographic, socio-economic background,

physical and mental construction or the user‘s emotion state.

System factors refer to properties and characteristics that

determine the technically produced quality of an application or

service and they have been described by media, network and

device parameters while the context influencing factors are the

parameters that embrace any situational property to describe

users‘ environment. On another side, Mitra discussed several

factors which might affect QoE [10]. In their discussion,

categories of factors considered fall into device, application,

network and users.

Literature review shows the current research trend and

activities of both academia and industry with regard to QoE

concept. Immersive efforts by some researchers to define and

describe the concept have been observed, with a little focus on

identifying QoE influencing factors along with definitions.

Moreover, further studies significantly contributed in

identifying factors affecting QoE in different contexts. Three

key issues have been observed in the reviewed works. Firstly,

QoE is affected by many factors and the work done was not

able to exhaust all. Secondly, no researcher has identified

major factors among of the proposed factors affecting QoE

since it is not possible that all factors have equal influence to

QoE. For that exploratory study in this concept is inevitable.

Thirdly, many researchers did not give attention to the factors

affecting QoE in mobile broadband networks. This technology

has significantly grown and become the most accessible,

widespread and affordable last mile solution for broadband

connections to many people.

Therefore, this paper aimed to explore the major factors

which affect QoE of mobile broadband applications through

survey approach of active mobile broadband subscribers in

Dodoma City, Tanzania.

1.2 Mobile broadband networks and services

Cellular network history goes as far as 1980s when the first

analogue cellular network was launched. It was designed to

transmit only analogue voice signals. The coverage area was

divided into small cells to enable reuse of the same frequency

in different areas without causing interference in the network.

This kind of technology was famously known as 1st

Generation (1G). The demand for better services and efficient

system were the catalyst for development of higher systems

[11].

The Second Generation systems (2G) were introduced in the

market in 2000s through different technologies. In this system,

voice and text message were possible with a limitation in data

services due to low throughput of 9.6kbps. Afterwards,

improvement to 2G resulted in 2.5G through High Speed

Circuit Switched Data (HSCSD) technology. It could give a

data speed up to four times that of 2G. Its biggest challenge

was inability to proper use of resources due to circuit-switched

technique and throughput could not accommodate real time

applications [12]. Followed 2.5G was an improvement called

2.75G which is called enhanced rate for global evolution

(EDGE). This support data with a maximum speed of 384kbps

and uses packet-switched network famously acknowledged for

its efficiency in resource utilization.

Further evolution of cellular network continued and resulted

into 3rd

Generation networks (3G).With this technology users

can have access to both voice and other broadband services at

the same time. At mobility state, network throughput varies

between 144kbps and 384kbps, while at stationary and good

network coverage, users can experience a speed up to

2.048Mbps. This system was intended to provide a global

mobility with a wide range of services including telephony,

paging, messaging, internet and broadband data [13]. The 3G

have been implemented in most places using wide code

division multiple access (WCDMA) and CDMA2000

technologies. Further researches are still going on to test 4G

and 5G. However, in many developing countries particularly

Tanzania, 2.5, 2.75G and 3G are the mostly used solutions for

broadband and internet connections [14].

Statistics suggest that mobile broadband networks grow

every year. In a global perspective, according to ITU-statistics

of the year 2013, the global mobile broadband penetration was

about 30.7% of world population [15]. Apart from that, a

research done by Erickson projects that by the end of 2019,

about 5.9 billion smartphones will be connected to mobile

broadband networks and generate multimedia traffic which

will account 61% of the total mobile network traffics. Similar

study done by CISCO projects multimedia applications traffic

growth in mobile broadband networks to reach two-third of

the total traffic by the end of 2017.

In Tanzania perspective, statistics show the growth of

mobile broadband network due to the number of service

providers, tele-density ratio and market shares. In the year

2013, the tele-density was recorded at 61% while in year 2014

it has risen to 67%. According to Tanzania Communications

Regulatory Authority (TCRA) statistics [16], the average

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growth rate was recorded to be 1.8% in December 2014,

compared to 1.5% rate of September 2014.

From those facts, it is vivid that, mobile broadband plays a

great role as a solution to the last mile problem for broadband

and internet connection of many people. For that matter,

investigating the major factors affecting QoE in this network

type is a topic of interest to telecom industry, regulatory

authority as well as consumers. According to researcher‘s

understanding, the study to identify major factors affecting

QoE in mobile broadband networks has received limited

attention by previous researchers.

2. METHODOLOGY

This study employed a survey research technique to isolate

the major factors affecting quality of experience of broadband

users. According to [17], survey method is an appropriate

research design for describing and interpreting the conditions

and relationships that exist, practices that prevail, processes

that are going on, influences that are being felt and trends that

are developing. For that matter, survey method is a suitable

study design for the isolation of factors influencing QoE in

mobile broadband networks.

2.1 Generating QoE influencing variables

Investigation of variables affecting QoE has been done by

other researchers focusing on different themes such as media,

application, and communication network used to conduct the

study [18] [19]. A deductive approach was used for generating

and reviewing variables which affect users‘ QoE. This was

achieved by using an extensive literature review and

experience in telecommunication field, which is a common

method for determining variables for testing [20] [21]. In this

study, about 30 variables were generated as shown in Table 1.

Table 1: List of variables affecting QoE

S/N Variable SN Variable

1 Server availability 16 Information loss

2 Content availability 17 Bandwidth

3 Data rate 18 Screen size

4 User mood 19 User location

5 User preferences 20 Expectation

6 Delay variation 21 Battery life

7 Content type 22 Service cost

8 User occupation 23 Environment effects

9 Ease of use 24 Social influence

10 Content quality 25 Information Delay

11 Gender 26 Cultural effects

12 Age 27 Prior experiences

13 Network availably 28 Service promotion

14 Device brand 29 Application availability

15 Device processing

power

30 Current experience

2.2 Study Area

The study was conducted in Dodoma town targeting

students and academic staff members who are the potential

users of mobile broadband networks. This fact can be verified

by various special data bundles designed by mobile operators

for University communities [22].

2.3 Sample Size

According to a rule of thumb for doing factor analysis, the

ratio between attribute to be tested to sample size should be

1:5, which means sample size should be greater than number

of variables by five times [23]. In this study there were 30

quality variables generated from literature reviews and field

experience. Therefore, the sample size required should be

greater than 150. To account for reliability of returning

respondents, a total of 250 questionnaires were distributed to

mobile broadband subscribers of colleges and universities in

Dodoma city. Out of 250 questionnaires, about 211 were

returned for analysis which is about 84.4% success returning

rate.

2.4 Data Collection

Data collection was done by using a questionnaire which

contained structured questions. The first part of the

questionnaire recorded respondents‘ information such as age,

gender, education level, experience of using mobile broadband

networks and applications normally accessed. The second part

of the questionnaire aimed to understand the importance of

each outlined variable towards users‘ QoE on the course of

using mobile networks. During data collection process, scaling

is very important procedure to be observed. Scaling explains

the procedure of assigning numbers to various degree of

opinion, attitude and other concept. In QoE studies, the ITU

proposed measurement scale called Mean Opinion Score

(MOS) which explains user satisfaction of an accessed

telecommunication service [24]. This scale is similar to Likert

scale which is a five-point scale indicating user response. In

this study, the scaling followed five point scale to represent

users‘ opinion ranging from 1 to 5 indicating Very low to

Very high response score respectively, while a neutral point

being centered.

2.5 Data Analysis

In this study, descriptive statistics was performed in order to

investigate the distribution and features of people who

participated in the study with respect to the targeted

population. Also inferential analysis was performed using

factor analysis method in order to investigate the common

variation among proposed QoE variables and grouping them

in common factors. Reliability test of measurement instrument

was done using Cronbach alpha score, while sampling

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adequacy was tested using Kaiser-Mayer-Olkin (KMO) and

Bartlett‘s test of sphericity.

The factor analysis was done using Principle Component

analysis (PCA) method while the rotation technique used was

varimax with factor score with loading smaller than 0.4 were

dropped.

3. RESULTS

3.1 Descriptive Analysis

The respondents participated into the study were 211

mobile broadband customers whereby 38% were female and

62% male and their age group between 18-23 making 52.6%,

20-29 representing 32.7% of responded customers. About

74.9% of customers have experience of over 1 year of using

mobile broadband networks. Applications which found

interests by many customers using mobile broadband networks

are web-browsing having 66.4%, file downloading making

21.8% and the remaining percentage for other applications.

Most of contacted customers use smartphones to get

connected to broadband networks, making 52.6% and those

using laptops making 37.4% and the remaining 10% for other

devices like desktop, tablets etc.

3.2 Factor Analysis

The analysis was done through a statistical package

software called Statistical Package for the Social Sciences

(SPSS). The alpha score which indicates reliability of a

measurement scale was 0.804, which exceed a minimum value

of 0.7. For that reason our results are reliable [25] [26]. The

KMO score was 0.748 while Bartlett‘s test of sphericity was 0

indicating that the data set is sufficient for factor analysis

respectively [27] [28]. Extraction method applied was

Principal component analysis, with Varimax rotation, while

variable extraction based on the criteria of eigenvalues greater

than or equal to 1 while maximum iteration was 25. Apart

from eigenvalues, the threshold score for variables considered

in the analysis was 0.5.

Results summary as indicated in Table 2 shows that, out of

31 tested variables, only 20 of them obtained enough weight

and interpreted as major influencing variables. It has been

Table 2: Rotated matrix component

1 2 3 4 5 6 7 8 9 10 11

Application Availability 0.66

Packet loss 0.58

Jitter 0.54

Devise Processing Power 0.51

Perceptual gender effects 0.80

Perceptual age effects 0.63

Behavioral effects 0.52

Screen Size

Expectation 0.71

Mood 0.70

Easy of Use

Location 0.78

Content Accessibilty 0.60

Bandwidth

Service Promotions 0.70

Application server status

Content Quality

Perceptual environmental effects 0.81

Content Type

Perceptual cultural effects

Experience 0.72

Occupation 0.56

Prio Experience 0.51

Device Brand 0.71

Data rate

Battery Life

Delay 0.81

Service Cost 0.72

Network Availability

Social Influence 0.80

Factor loading

Variable

QoE

User

Device

Age

Business

Network

Content/Application

Processing power

Social influence

Occupation

Prior Experience

Mood

Expectation

Behavior

Gender

Delay

Packet loss

Device type(brand)

Content Quality

Accessibility

Availability

Delay variation

Service cost

Customer location

Business environment

Promotions

Throughput

Figure 1: Factors and variable affecting QoE

noted that, users rated less on the influence of network

availability, data rate and bandwidth towards their QoE. This

can be explained in the sense that the issue of bandwidth and

data rate go synonymous and they cause data loss and delay.

The issue of delay and data loss in broadband application

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causes a noticeable effect.

Application server status, content type and content quality

are hypothetically considered to influence QoE. However, in

this study they have received low score ratings meaning that

they are not strong variables to influence QoE of mobile

broadband users. The users‘ opinions suggest that users with

limited technical background cannot differentiate between

server availability and application availability. The effect is

only felt when the application/content of choice is not

available. Content quality and content type have also not been

considered as significant variables affecting users QoE due to

the fact that users are in control of their choices with regard to

contents and they are not bound to their choices. Secondly

most of streaming contents accessed through internet are free

of charge, which does not raise users‘ concern about content

quality, since they do not pay for them.

Screen size, device battery life and ease of use are among

the variables with low score. This is due to the wide choices of

device brands at an affordable prices. Users can switch from

one device to another depending on the function intended.

Lastly but not least, cultural effect on accessed media was

said to affect QoE of accessed media over broadband. Due to

wide spread of internet connectivity, the world has become

like one village which creates intercultural interference across

different countries. For that matter, cultural influence is very

minimal in affecting QoE of broadband subscribers, even

when the accessed content does not reflect the culture of users

4. DISCUSSION

The communication ecosystem of broadband networks shows

the interdependency between key players which are users,

devices, network, applications and customer support. Since

QoE is an end-to-end quality, it is affected by a number of

issues falling from technical and non- technical variables.

Modeling QoE requires the consideration of key variables

from each player which seem to have higher weight than

others. The factored variables serve as key input for field

study to understand statistical relationship between user

satisfaction and influencing factors.

5. CONCLUSION

There are many factors which influence QoE in

communication networks. This paper has focused on

identifying major QoE influencing factors in mobile

broadband networks. In this work, a survey study to active

users has been conducted using a questionnaire which tested

about thirty one variables, derived from literature review as

well as field experience. Variables reduction technique

employed is called factor analysis, which reduced thirty one

variables into twenty variables grouped into user, device,

network, content and business factors.

It has been found that out of the five factors, network and

content factors seems affect user‘ QoE severely since their

variation have no direct control of users meanwhile user,

device and business factors seems to be indirectly affecting

factors since users have a role to play towards their

satisfaction.

Future work will try to model the response of users‘

satisfaction from the variation of network and content

variations as the input to the model, so as to have a

mathematical model to describe the relationship.

REFERENCES

[1] D. Soldani. ―Means and methods for collecting and

analyzing QoE measurements in wireless networks‖. In

Proceedings of International Symposium on a World of

Wireless, Mobile and Multimedia Networks, June, 2006,

pp. 531–535,

[2] ITU-T Rec.800: ―Methods for subjective determination of

transmission quality‖. International Telecommunication

Union-Telecommunication Standardization Sector (ITU-

T), (2007).

[3] P. Le Callet, S. Moller and A. Perkis. ―Qualinet white

paper on definitions of quality of experience‖, 2013

[4] S. Baraković J and H. Bajrić. ―QoE dimensions and QoE

measurement of NGN services‖. In Proceedings of the

18th Telecommunications Forum.

[5] R. M Noor and S. Khorsandroo S, ―Quality of experience

key metric framework for networks mobility user‖.

International journal of the Physical Sciences, Vol 6, Dec

2012, pp.6521-6528

[6] L.T. Nguyen. ―Analysisi & Modeling of QoE for Web

based Services‖.Maasey University, PhD Thesis, 2013

[7] K. U. R., Laghari and K. Connelly, ―Towards total quality

of experience: A QoE model in a communication

ecosystem‖. Communications Magazine, IEEE, 50(4),

Dec 2012, pp. 58-65

[8] W. Song, D.W. Tjondronegoro and M. Docherty.

―Understanding user experience of mobile video‖

framework, measurement, and optimization, 2012, pp.3-

30.

[9] U. Reiter, K. Brunnström. K. De Moor, M.C Larabi, M.

Pereira, A. Pinheiro and A. Zgank. ―Factors influencing

quality of experience‖. In Quality of Experience 2014, pp

55-72.

[10] K. Mitra, A. Zaslavsky and C. Åhlund, ―QoE Modelling

Measurement and Prediction‖, A Review. arXiv preprint

arXiv:1410.6952, June, 2014

[11] J. Korhonen, ―Introduction to 3G mobile

communications‖. Artech House., 2003

[12] D. Soldani, M. Li, and R. Cuny, ―QoS and QoE

management in UMTS cellular systems‖. John Wiley &

Sons., 2007

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Copyright ©2015 JIVE, ISSN 1821-7087 14

[13] P. Le Callet, S. Moller, and A. Perkis ―Qualinet white

paper on definitions of quality of experience‖ , 2013

[14] TCRA, ―Quarterly Statistics report‖, Available at

http://www.tcra.go.tz, accessed on 12th May 2015

[15] ITU-T SG 12. ―Performance, QoS and QoE‖, Available at

http://www.itu.int,accessed on 13th December 2013.

[16] M. Jarschel, D. Schlosser, S. Scheuring, and T. Hoßfeld,

―An evaluation of QoE in cloud gaming based on

subjective tests‖. In Innovative Mobile and Internet

Services in Ubiquitous Computing (IMIS),Fifth

International Conference on IEEE, June 2011,pp. 330-

335

[17] C. R. Kothari, ―Research methodology‖, Methods and

techniques, New Age International ,2011

[18] P. Reichl, S. Egger, R. Schatz and A. D'Alconzo, ―The

logarithmic nature of QoE and the role of the Weber-

Fechner law in QoE assessment‖. In Communications

(ICC),IEEE International Conference on, May 2010, pp.

1-5

[19] L.T. Nguyen, ―Analysis & Modeling of QoE for Web

based Services‖. Massey University, PhD Thesis.

Available at http://muir.massey.ac.nz, accessed on 5th

March 2014

[20] S. Barackovic and S.L Kapov, ―Survey and Challenges of

QoE Management Issues in Wireless Networks‖. Journal

of Computer Networks and Communications, 2013

[21] Singh. Y. K, ―Fundamental of research methodology and

statistics‖. New Age International Publisher Ltd, 2006

[22] Vodacom special package for University students :

Available on https://www.vodacom.co.tz/internetservices,

accessed on 26th April 2015

[23] K. U. R Laghari, R. Gupta, S. Arndt, S. Moller and T. H

Falk.,― Neurophysiological experimental facility for

Quality of Experience assessment‖, IFIP/IEEE

International Symposium on May 2013,pp. 1300-1305

[24] ITU-T Rec 800, ―Methods for subjective determination of

transmission quality.‖ International Telecommunication

Union Telecommunication Standardization Sector, 2007.

[25] A.S. Gaur and S.S. Gaur, ―Statistical Methods for practice

and research‖, A Guide to data analysis using

SPSS.SAGE publications, New Delhi, 2014

[26] P. Reichl, S. Egger, R. Schatz and A. D'Alconzo, A. ―The

logarithmic nature of QoE and the role of the Weber-

Fechner law in QoE assessment‖. In Communications

(ICC),IEEE International Conference on May 2010, pp. 1-

5

[27] K. U. R. Laghari, and K. Connelly ―Towards total quality

of experience‖: A QoE model in a communication

ecosystem. Communications Magazine, IEEE, 50(4),

April 2012, pp.58-65.

[28] Y.K. Singh, ―Fundamental of research methodology and

statistics‖. New Age International Publishers Ltd, 2006

Alex Mongi is with University of Dodoma, College of

Informatics and Virtual Education, where he teaches

Telecommunications Engineering programs. He is also a PhD

candidate researching on modeling of Quality of Experience in

mobile broadband networks. He has been a trainee Engineer at

Tanzania Telecommunications Company TTCL since 2010,

supervised by Engineers Registration Board (ERB).

Prof. Aloys Mvuma is with School of Informatics of the

University of Dodoma where he serve a Principle of the

College of Informatics and Virtual Education. Apart from

administrative duties, he has been actively involved in

teaching as well doing research work. His area of interests lies

within Telecommunication signals and systems analysis,

digital communications as well as broadband communication

networks.

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Modelling Approach Towards a Better Understanding of Sea Surface Salinity off the

East Africa Coast

Majuto C. Manyilizu

Abstract— The western Indian Ocean, off East Africa,

contributes significantly to socio-economic development of

the countries in the region, and its sea surface salinity (SSS)

shows seasonality. Although the SSS plays a crucial role on

thermodynamic processes and marine ecosystem in the

region, its in-depth and systematic numerical study on the

mean state and seasonal cycle does not exist due to temporal

and spatial sparse observations as well as lack of clear satellite

salinity measurements. This study applies a regional model to

simulate the mean state and annual cycle of the SSS as well as

the forcing behind these patterns in the ocean off East Africa.

The model is forced with the monthly mean Comprehensive

Ocean and Atmosphere Data Sets winds and heat fluxes, and

being validated by the World Ocean Atlas 2009 (WOA2009)

data. The model outputs and that from WOA2009 are in good

agreement. Both latent heat flux and shortwave radiations

appear to contribute significantly on the SSS patterns in the

region. The region with high SSS to the north reflects strong

shortwave radiations which can be related to strong winds

which sweep away clouds leaving the sky clearer and thus,

allowing high shortwave radiation penetration in addition to

strong evaporation. In addition to the present theoretical and

observational analysis in the region, using a modeling

approach has facilitated a better understanding of the SSS

patterns and the forcing behind them. Such knowledge is

important for the marine ecosystem, as well as marine and

coastal shipping activities in the region.

Keywords— model, sea surface salinity, East Africa.

1. INTRODUCTION

HE western Indian Ocean off the East Africa region plays

a critical role in the socio-economic development of

Tanzania and its neighbouring countries. The Tanzanian

shelf region, for example, accommodates four major ports,

namely; Dar es Salaam, Zanzibar, Tanga and Mtwara which

provide transport services to local communities. Furthermore,

the ports serve for transit goods to land-locked countries such

as Uganda, Rwanda, Burundi, Democratic Republic of Congo,

Zambia and Malawi. Consequently, these ports contribute

significantly to the country‘s trade and income. In addition to

that, the current discoveries of oil and gas below the ocean off

Mtwara in the southern Tanzanian shelf are expected to boost

the country‘s economy in conjunction with tourism and

recreation. Furthermore, [1] reports that fishing which is the

main food source and commercial activity in the coastal

communities contributes to the gross domestic product (GDP)

of Tanzania by about 2.1-5.0% for Mainland Tanzania and

2.2-10.4% for Zanzibar. However, those socio-economic

activities are influenced by variability of ocean circulation and

properties which further affect the thermodynamic processes

and ecological distribution in the region. Thus, understanding

of ocean variability off East Africa is very important for the

development of Tanzania and the neighbouring countries.

Ocean variability off the East African region is dominated by

strong seasonality which affects the upper ocean circulation

and physical properties. The surface monsoon winds blow

generally from the tropical western Indian Ocean (southwest)

in austral winter (June - September) and from the northeast to

the tropical western Indian Ocean in austral summer

(December - March). The austral winter and summer are

correspondingly named the South West monsoon and the

North East monsoon. The two transition periods occur in

April/May for austral autumn and austral spring in

October/November. The upper ocean circulation has been

reversed to the north of 12oS in response to the seasonal

reversals in surface winds [2] as portrayed in a schematic

diagram in Figure 1a. The East African coastal current

(EACC) which dominates the flow in the region is weakened

during the North East monsoon to about 0.2 ms-1

([3] & [4]).

However, during the South West monsoon, the trade winds

along the East African coast strengthen the EACC to a

velocity of up to 2 ms-1

from April through October ([3], [4]).

Similar speeds of about 2.0 ms-1

were reported in the Indian

Ocean Experiment (INDEX-1976-1979) during April and May

of 1979 [5].

As in global scale, salinity variability in the ocean off East

Africa has not been widely studied since salinity observations

are sparse, both temporally and spatially, and up to now there

are no clear satellite salinity measurements [6]. However, a

few authors have conducted studies on variability of salinity in

the East African coastal ocean relying on sparse observational

data (e.g. [7], [8], [9]). The authors suggest that salinity

distribution in this region is influenced by upper ocean

mixing, upwelling, river discharge, precipitation and runoff.

The upper ocean mixing, river discharge and runoff are

sources of phosphorus and nitrate over the coastal waters off

Tanzania and southern Kenya while mixing and upwelling are

sources off northern Kenya and Somalia [10].

T

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The annual cycle of the sea surface salinity in the coastal

ocean off Tanzania is shown by [11] in Figure 6 for 1956 and

1975. Minimum salinity occurs at the onset of the South West

monsoon when river discharge, cloud cover and precipitation

are high along the coast of East Africa. During this time,

freshwater discharge from major rivers such

Figure 1: Schematic diagram of near-surface circulations in the tropical western Indian Ocean (in blue) and the subsurface return

flow of the super gyre (in magenta) during (a) the South-West Monsoon and (b) the North-East Monsoon, adopted from [2]. The

currents are the South Equatorial Current (SEC), South Equatorial Counter-Current (SECC), North-East and South-East

Madagascar currents (NEMC and SEMC), East African Coastal Current (EACC), Somali Current (SC), Southern Gyre (SG),

Great Whirl (GW), and adopted from [12] South-West and North-East Monsoon currents (SMC and NMC).

as the Ruvuma, Mbemkulu, Rufiji, Ruvu and Pangani, after

the March-April-May (MAM) rainy season plays a great role

in lowering the salinity of the coastal ocean. The Rufiji river,

which is one of the largest rivers in Africa, is noted to

discharge about 2000-3000m3/s into the Tanzanian coastal

ocean [4]. The influence of discharge of the major rivers such

as the Tana and Sabaki on coastal waters off northern Kenya is

also noted shortly after the inland rainy season in both

seasons, MAM and October-November-December (OND). In

contrast to this, the highest salinity in the coastal waters off

East Africa occurs during the North East monsoon when air

temperatures and solar insolation are high, and the rainfall and

river discharges are low. [9] found 34 and 35.2 as the typical

sea surface salinity values between April and June on the East

African coastal waters off Tanzania.

Up to now, there is no in-depth and systematic numerical

study of the mean state and seasonal cycle of the sea surface

salinity in the western Indian Ocean off East Africa. Thus, this

study aims at providing numerical studies on the mean and

annual cycle of the sea surface salinity in the region. Such

study provides a fundamental understanding of inter-annual

and longer-term variability in the region. Using the Regional

Ocean Modeling System (ROMS), the study addresses the

following questions: How does the sea surface salinity vary

throughout the year in the tropical western Indian Ocean off

East Africa? How does the latent heat flux influence on the

salinity of the upper ocean in the region? How does the

shortwave radiation contribute to the salinity variability in the

coastal ocean off East Africa? The datasets used and the

methodology applied in this study are detailed in section 2.

The results and discussion of the mean state and annual cycle

of the upper-ocean dynamics are provided in section 3.

Section 4 contains the summary and conclusion.

2. METHODOLOGY

This study uses the Regional Oceanic Modeling System

(ROMS) to understand the mean state and annual cycle of the

sea surface salinity in the western Indian Ocean off East

Africa. The model has been shown to realistically simulate the

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Figure 2: The study domain in the tropical western Indian Ocean and its bathymetry derived from ETOPO2V2C

(www.ngdc.noaa.gov).

tropical western Indian Ocean by [12], the Comoros Basin by

[13] and the Zanzibar Channel by [14]. Thus, the model is a

well known and established tool for simulating ocean

circulation in this region. It is a free-surface, terrain-following

ocean model which solves the three dimensional hydrostatic

primitive equations ([15], [16]. The vertical structure is

discretized in stretched, terrain-following coordinates, and

orthogonal curvilinear coordinates are applied in the

horizontal on a staggered Arakawa C-grid. The K-Profile

Parameterization (KPP) provides the model vertical mixing

[17]. This research uses the IRD version of the code (ROMS

AGRIF), available from the Website

"http://www.romsagrif.org" [18].

The ROMS model is configured in the tropical western Indian

Ocean for the domain 37.5-60oE and 4.85

oN-18

oS with its

bathymetry derived from ETOPO2V2C (see

www.ngdc.noaa.gov, Figure 2). It consists of 40 vertical levels

with 1/6o horizontal resolution and time steps of 1800 seconds.

The model is forced with the monthly mean Comprehensive

Ocean and Atmosphere Data Sets (COADS) winds and heat

fluxes [19] for 10 years with a three-year spin-up time. The

initial and lateral boundary conditions for this simulation are

extracted from the World Ocean Atlas 2001 global dataset

with monthly climatology at 1o resolution, WOA2001 [20]. A

heat-flux correction resulting in a restoring term on surface

temperature is applied in this experiment. The model outputs

of the experiment are averaged every two model days which in

turn are processed to calculate mean state and climatological

data. The model is validated by comparing the model sea

surface salinity with that from WOA2009. The WOA2009

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datasets consist of the global monthly climatology at 1o grid

resolution and interpolated to standard depth levels on both 1o

and 5o grids ([21], see www.nodc.noaa.gov).

Figure 3: Annual mean sea surface salinity in the tropical western Indian Ocean for (a) model (b) World Ocean Atlas 2009

(WOA2009), (c) heat fresh water flux (net evaporation minus net precipitation) with winds and (d)shortwave radiations.

3. RESULTS AND DISCUSION

3.1 Mean State

The spatial patterns of the mean state of the sea surface

salinity (SSS) from the model simulations are compared with

that from WOA2009 with respect to winds and shortwave

radiation patterns over the same domain (Figure 3). The winds

which cause the latent heat-flux, and the shortwave radiations

are used to investigate the forcing of the salinity patterns in the

region. The mean spatial patterns of the model sea surface

salinity compare reasonably well with that from WOA2009.

The western Indian Ocean off the East African region shows

north-south distribution patterns of the SSS in the ROMS

model simulations and WOA2009. High SSS (>35.00) appears

to the north of 7oS, and relatively low SSS (<35.00 ) occurs to

the South with the highest values being confined to further

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offshore (Figure 3a & b). The region with high SSS matches

with strong evaporation greater than 0.2 cm/day associated

with strong winds over that region (Figure 3c). Furthermore,

the region with high SSS reflects strong shortwave radiations

which can be related to strong winds which sweep away

clouds leaving the sky clearer and thus, allowing high

shortwave radiation penetration. The opposite seems to exist

to the South. Thus, strong evaporation and high shortwave

radiation may be major contributors to the high SSS to the

north of the region.

The model reproduces the mean SSS spatial patterns in the

region, fairly well compared to the WOA2009 SSS data. Since

the tropical western Indian Ocean experiences strong

seasonality, it is useful to discuss sea surface salinity in its

annual cycle to gain more understanding for each season. The

discussion for the annual cycle is provided hereafter and it

starts with May, the monsoon transition season, then July,

midway through the South West monsoon, followed by the

seasonal monsoon transition (November) and then January,

midway through the North East monsoon.

3.2 Annual Cycle

The annual cycle of the model SSS in the tropical western

Indian Ocean off East Africa is compared with that from the

WOA2009 data (Figure 4, 1st

& 2nd

columns). In general, the

annual cycle of the SSS appears to be comparable to that of

WOA2009. All SSS data display seasonality. The study of the

annual cycle of the SSS patterns in the region is performed in

conjunction with the net freshwater fluxes (net evaporation

minus net precipitation) and shortwave radiation as the major

contributors for this distribution (Figure 4, 3rd

and 4th

columns).

The spatial distribution of the SSS and its corresponding net

freshwater flux as well as shortwave radiations during the

transition period to and during the South West monsoon (May

and July) is illustrated in Figure 4 (1st and 2

nd rows). In May,

there is relatively salty water (> 35.00) to North East African

coast ocean reflecting strong net evaporation fluxes of more

than 0.3 cmday-1

and shortwave radiation that ranges from 220

to 250 Wm-2

(Figure 4a). This region is characterized by

strong winds in this period which might be sweeping away the

cloudy sky leaving the region clearer for strong shortwave

radiations. Thus, latent heat flux associated with strong winds

which clear the sky for shortwave radiation can be associated

with high SSS to the north. Relative fresh water of about 34.6-

34.8 occurs in the Tanzanian ocean to the south of 10oS in

agreement with [9]. The net precipitation flux near the

Tanzanian coast between 0-10oS reflects the March-April-May

rainfall season (long rains) over East Africa. Such patterns

could be the reasons for relatively fresh waters off Tanzania

shelf region. Further south of 10oS, the strong evaporation

seems to be the main contributor for the low SSS as the winds

and shortwave radiations are low. In this period, the salty

waters (~35.6 ) which appear to the north of the region in the

model and WOA2009 are maintained in July. The net

evaporative flux is slightly enhanced in July resulting in

relatively high SSS to the north of the domain (Figure 4b).

Figure 4 (3rd

and 4th

rows) displays the spatial patterns of

the SSS, net freshwater fluxes and shortwave radiations during

the transition period to and during the North East monsoon

(November and January) in the region. The highest SSS in the

region occurs in the transition period to the North East

monsoon (November) as indicated in Figure 4c. In this period,

relatively high SSS in the Tanzanian shelf region can be

associated with high net precipitation and shortwave radiations

in November (Figure 4c). This is a period with very weak

winds that create shallow mixed layers especially to the

northeast of 10oS where it matches with high SSS ranging

between 35.4 to 35.8 (Figure 4c). High SSS is maintained to

the northwest of the domain during January to the north of

10oS, matching with a strong net evaporation driven by cold

dry north-easterly winds (~0.5 cmday-1

). Such high SSS from

November to January agrees with that suggested by [8] and

[7]. This cooler salty zone matches with strong net

evaporation to the north, and the warmer fresh zone matches

with strong net precipitation and low shortwave radiation to

the south.

4. CONCLUSION

The tropical western Indian Ocean shows seasonality which

has been demonstrated by the model applied in the western

Indian Ocean off East Africa. The simulated SSS in the

tropical western Indian Ocean off East Africa reasonably

agrees well with that from the WOA2009 data. Generally,

fresh and salty waters occur in the south and north of the

domain, respectively. In the mean state, the western Indian

Ocean off the East African region shows north-south

distribution patterns of the SSS in the ROMS model

simulations and WOA2009. High SSS associated with strong

evaporation, strong winds and shortwave radiations occur to

the north during transitions to and in South West monsoon.

However, the mid of Tanzanian shelf shows minimum SSS in

May which can be related to weak winds and shortwave

radiation in conjunction with precipitation of March-April-

May rains. However, the highest SSS occur to the north in

transition to and during the North East monsoon as shown in

November and January.

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Figure 4: Annual cycle of sea surface salinity for model (1st column), WOA2009 (2

nd column), freshwater flux (net evaporation

minus net precipitation) and winds (3rd

column) and shortwave radiation (4th

column) over the tropical western Indian Ocean off

East Africa in (a) May, (b) July, (c) November and (d) January.

Owing to marine and coastal contributions to the socio-

economic development of Tanzania and its neighbouring

countries, a better understanding of SSS is crucial for

thermodynamic processes and regional ecological systems.

Such understanding will improve planning and management of

activities in the East African marine ecosystem region as well

as marine and coastal shipping activities in the Tanzanian

shelf region. Using a modeling approach, this study has

facilitated a better understanding of the upper ocean salinity in

the region, in conjunction with the present theoretical and

observational analysis. Such understanding of the upper-ocean

dynamics in the region is important, both for scientific

progress and for marine, coastal and port management.

Acknowledgments: Special thanks to the Carnegie-IAS

Regional Initiative in Science and Education (RISE) for

funding this research.

REFERENCES

[1] N. S. Jiddawi and M. C. Öhman, "Marine fisheries in

Tanzania", Ambio, , 2002, 31: 518-527

[2] F. A. Schott, S. P. Xie, and J. P. McCreary, Jr., "Indian

Ocean circulation and climate variability", Reviews of

Page 27: jive volume 3

JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER 2015

Copyright ©2015 JIVE, ISSN 1821-7087 21

Geophysics, 47, RG1002, doi: 10.1029/2007RG000245,

2009.

[3] B. S. Newell, "A preliminary survey of the hydrography

of the British East African Coastal waters", Fishery

Publications Number 9. Her Majesty‘s Stationery Office,

London, 1957, 21 pp.

[4] A. M. Dubi, "Coastal erosion. In: Ngusaru, A.S. (Ed). The

present state of knowledge of marine science in Tanzania:

synthesis report. Tanzania Coastal Management

Partnership", Science and Technical Working Group,

2000.

[5] F. A. Schott and J. P. McCreary, "The monsoon

circulation of the Indian Ocean", Progress in

Oceanography, 51, 1-123, 2001.

[6] B. Subrahmanyam, V. S. N. Murty, and D.M. Heffner,

"Sea Surface Variability in the Tropical Indian Ocean",

Remote Sense Environment, 115, 3, 944-956, 2011.

[7] B. S. Newell, "The hydrography of British East African

coastal waters", II. Fishery Publications, London, 12, 1-

18, 1959.

[8] I. Bryceson, "Seasonality of oceanographic conditions

and phytoplankton in Dar es Salaam waters. University

Science Journal (Dar es Salaam University), 8, 66-76,

1982.

[9] N. Nyandwi and A. Dubi, "Episodic atmospheric changes

and their impact on the hydrography of coastal waters in

Tanzania", Climate Research, 18, 157 – 162, 2001.

[10] S. L. Smith and L. A. Codispoti, "Southwest monsoon of

1979: chemical and biological response of Somali coastal

waters", Science, 209, 597-600, 1980.

[11] T. R. McClanahan, "Seasonality in East Africa‘s coastal

waters", Marine Ecology-Progress Series, 44, 191-199,

1988.

[12] M. Manyilizu, F. Dufois, P. Penven, and C. Reason, Inter-

annual variability of sea surface temperature and

circulation in the tropical western Indian Ocean, African

Journal of Marine Science, 36:2, 233-252,

DOI:10.2989/1814232X.2014.928651, 2014.

[13] C. Collins, J. C. Hermes and C. J. C. Reason, "Mesoscale

activity in the Comoros Basin from satellite altimetry and

a high-resolution ocean circulation model", Journal of

Geophysical Research: Oceans, 119, doi:

10.1002/2014JC010008, 2014.

[14] C. G. Mayorga-Adame, "Ocean circulation of the

Zanzibar Channel: A Modeling Approach, 2007,

Technical report, Thesis Research, La Jolla, California,

USA. http://www.theissresearch.org/zanzibar/

[15] A. Shchepetkin and J. McWilliams, "A method for

computing horizontal pressure-gradient force in an ocean

model with a non-aligned vertical coordinate", Journal

of Geophysical Research, 108, 35.1-35.34, 2003.

[16] A. Shchepetkin and J. McWilliams, "The Regional

Oceanic Modelling system (ROMS): A split-explicit,

free-surface, topography following coordinate oceanic

model", Ocean modelling, 9, 347-404, 2005.

[17] W. G. Large, J. C. McWilliams, and S. C. Doney,

"Oceanic vertical mixing: a review and a model with a

non-local boundary layer parameterization", Reviews of

Geophysics, 32, 363-403, 1994.

[18] L. Debreu, P. Marchesiello, P. Penven, and G. Cambon,

Two-way nesting in split-explicit ocean models:

algorithms, implementation and validation, Ocean

Modelling, 49-50, 1-21, 2012.

[19] A. M. Da SFilva, C. C. Young, and S. Levitus, "Atlas of

Surface Marine Data 1994, Volume 1: Algorithms and

Procedures", Technical report 6, United States

Department of Commerce, NOAA, NESDIS, 1994.

[20] M. E. Conkright, R. A. Locarnini, H. E. Garcia, T. D.

O‘Brien, T. P. Boyer, C. Stephens, and J. I. Antonov,

World Ocean Atlas 2001: Objective analysis, data

statistics, and figures, CD-ROM documentation,

Technical report, National Oceanographic Data Center,

Silver Spring, MD, 2002.

[21] J. I. Antonov, D. Seidov, T. P. Boyer, R. A. Locarnini, A.

V. Mishonov, H. E. Garcia, O. K. Baranova, M. M.

Zweng, and D. R. Johnson, World Ocean Atlas 2009,

Volume 2: Salinity. S. Levitus, Ed. NOAA Atlas

NESDIS 69, U.S. Government Printing Office,

Washington, D.C., 184 pp, 2010.

Dr. Majuto C. Manyilizu is a Lecturer at College of

Informatics and Virtual Education, University of Dodoma in

Tanzania. His email address is [email protected].

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Abstract— Communities of Practice (CoPs) have been

deployed in many settings and proven to be very successful in

sharing knowledge in many settings. In today‘s world, people

from different organizations and sectors need to work together

in many ways to achieve a common goal. In the education

sector, CoPs have played a key role in sharing some

pedagogical skills among teachers at different levels.

However, Information and Communication Technology (ICT)

in these CoPs has not been fully deployed to help in the

teacher‘s professional development. This paper aimed at

designing a CoP using ICT to increase collaboration among

teachers, assist in their professional development and improve

pedagogy. Data were collected through an online

questionnaire. Sixty teachers from 11 different Secondary

School Inspectorate Zones participated and responded. The

results showed that the use of CoPs can help accelerate

teachers‘ professional development. The study proposed a

model of an effective CoP for teachers using information and

communication technologies. It consists of a website linked to

social networks which give alerts to other teachers elsewhere

using mobile phones or computers. The suggested model once

implemented will help teachers share skills and knowledge in

different subjects of their interest.

Keywords—Communities of Practice (CoP), ICT,

Pedagogy, Online Social Networks.

1. INTRODUCTION

N most developing countries like Tanzania, improving the

quality of education requires adequate teacher preparation

and professional development. Teachers are the most

important factor in the education system to improve the

quality of education at all levels [6]. High-quality teaching

yields successful learners who perform better on exams and in

life in general [3]. In-service training of teachers is the

primary form of professional development to raise teaching

quality. This type of continuous learning helps teachers refresh

their skills and cope with the rapid changes in their

specializations.

In the last few years, the Government of Tanzania has worked

to ensure high-quality teaching by providing teachers with

professional development activities. One way to improve

teachers‘ professional development is to provide opportunities

for teachers to share their knowledge with each other. This can

be achieved by creating groups of people who have common

interests, concerns, or problems, and giving them a forum to

share knowledge and skills in that area on a continuous basis.

Such groups of people with common interests and goals are

professionally called CoP [4]. Communities of practice have

appeared in different forms: face-to-face communication,

meetings, phone calls and electronic communications [9].

Within the communities, individuals maintain the community

by learning the practice [5].

With recent advances in mobile phone and internet

technologies, organization of communities of practice is

becoming convenient—teachers can access them anywhere

and anytime. This paper aims at proposing a model for

creating learning and knowledge communities to improve

teachers‘ professional development using information and

communication technologies (ICTs) such as mobile phones

and online social networks. This is one way to improve

teaching by integrating these new technologies into

professional development.

Following the education development plans on primary and

secondary levels in Tanzania, there has been an increase in

enrollment in public schools. Enrollment in secondary school

Forms One to Four increased from 432,599 students in 2004 to

1,466,402 in 2009 [7]. This drastic increase in enrollment has

led to a high demand for teachers, which in turn has led the

government to employ undertrained teachers [8]. However,

these undertrained teachers generally do not have access to

continuous professional development training.

The teaching workforce in primary and secondary schools

in Tanzania is huge in number and scattered geographically.

Provision of in-service training to such scattered numbers of

teachers requires strategic planning to allocate resources and

address the training needs of individual teachers [8].

Leveraging the widely used ICTs in Tanzania for in-service

training is of paramount importance.

In 2008, the Education Sector Development Program (ESDP)

put in place strategies to improve continued in-service teacher

education and professional development, divided into two sub-

strategies: to provide regular in-service training courses

according to teachers‘ professional needs; and to establish

mentoring services for teacher professional support,

Online Teacher Communities of Practice: A Proposed Model to Increase

Professional Development in Tanzania

Leyla H. Liana and Lucian V. Ngeze

I

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development for primary and secondary school teachers, and

tutors [1]. This can be easily facilitated by using ICTs since

they can reach a large population of teachers at various

geographical locations.

2. METHODOLOGY

An online questionnaire was used to collect the responses

from 60 teachers that came from 11 different Secondary

School Inspectorate Zones. The first part of the questionnaire

consisted of demographic information including gender, age

range and school/zone name. The second part was aimed at

determining ICT skills and competence of the teachers.

Teachers were questioned about the type of phone they

possessed and their usage. They were also asked about their

computer knowledge and usage of social networks. The third

part of the questionnaire consisted of questions about

knowledge and usage of CoPs.

3. RESULTS AND ANALYSIS

The results obtained from the questionnaire showed that

51.2% of the respondents were males, while 48.8% were

females. Of the total participants, 4.2% were teachers in the

age range of 25-30 years, 18.8% were teachers in the age

range 31-36 years. About 27.1%, 33.3%, 10.4%, and 8.3%

were the percentage of the teachers in the age ranges 37-42

years, 43-48 years, 49-54 years and above 55 years,

respectively.

3.1. About Phone Type and Services

Teachers were asked what type of phones they own. Most

teachers (about 75.5%) possessed smart phones which could

perform different activities, while 24.5% have basic cellular

phones. Figure 1 depicts the information about the phone

types. Teachers were also asked about the type of services

they use with their phones and their frequency of usage. All

teachers surveyed use Short Message Service (SMS, also

known as text messaging): About 75% of them indicated that

they always use SMS, and about 25% only sometimes use for

SMS. Most teachers also use their phones for voice calls: 75%

always use their phones for making voice calls, while 12.5%

sometimes use their phones for voice calls. A small number of

participants (12.5%) do not use their phones for voice calls.

3.2. Computer Knowledge

Teachers were asked to self-assess their computer knowledge.

The majority of them (77.1%) were moderate computer users

who have skills to operate and use computers. A smaller

number (12.5%) of the teachers were advanced computer

users, while 6.3% of the participants were experienced

computer users (experts) and only 4.2% were beginners.

Figure 1: Type of Mobile phones used

3.3. Use of Smart phones for Educational Purposes

Participants were asked about the services they accessed and

used from their smart phones. Three services were compared:

educational purposes, Internet browsing and document

management. The first question aimed at getting the

frequency of mobile phone usage for educational purposes

such as searching for materials, reading notes, downloading

materials as it was shown that 47.6% always used their mobile

phones for educational purposes, whereas with another 47.6%

accessing educational contents on rare basis. In this regard,

only 4.8% never used their phones for educational purposes.

3.4. Commonly Used OSNs

The commonly used social networks were surveyed in the

proposed model for the CoP. Five OSNs were listed to be

evaluated by the participants, whereby only three were seen to

be frequently used by many of the participants frequently.

WhatsApp, Facebook and Instagram led the list of social

networks being used by most teachers as shown in Figure 2. In

terms of the frequency of use, WhatsApp led the survey with

most of the teachers always using it. Facebook social network

also showed a big trend in its use whereby 35.7% always used

the network with 42.9% rarely using it while only 21.4% did

not use Facebook.

3.5. CoP Concepts

Two questions intended to get the response of the participants

whether they knew what CoPs were, and whether they had

been using them to share skills, ideas and different subject

contents using their mobile phones and computers to enhance

their professional development. The responses showed that

18.8% always shared educational resources, while 66.7%

rarely used the devices to share resources and knowledge on

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some contents. A small minority (7%) never used their devices

to share educational resources.

Figure 2: Commonly used Social Networks and their frequency

When asked whether online CoP would help in their

professional development, 80.9% of the participants strongly

agreed, while only 14.9% said it would somehow help in their

professional development (Figure 3). Only 4.3% were not sure

whether it could help.

3.6. Readiness to Leverage OSNs for CoP

The last question intended to determine teachers‘ readiness

to leverage the use of social networks and mobile technology

for the creation of a CoP. Figure 4 shows the distribution in

percentages of the participants. About three quarters of the

participants are ready to participate in a CoP whereas 22.4%

indicated they need some time to understand the concept of

CoPs. The rest of the participants were not sure whether it

could be possible to use social networks and mobile

technology for a teachers‘ CoP.

Figure 3: CoP and the Professional Development

3.7. Proposed System Architecture

The teachers‘ community consists of several ICT

components and actors: the Online Social Networks (OSNs),

teachers‘ mobile phones, a website, teachers, and an overall

administrator. Figure 5 demonstrates the various components

of the teachers‘ CoP.

Figure 4: Readiness to leveratge Social Networks for CoP

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Figure 5: The Teachers CoP Components

Components of the Teachers CoP

Website

This is a central control structure that collects information

from the OSNs, teachers, and administrator. If there is a post

from the OSNs, the website then sends alerts to teachers‘

mobile phone. The website use case is shown on Figure 6.

Online Social Networks (OSNs)

These includes teachers‘ CoP pages from various

social networks such as Facebook, Instagram,

LinkedIn, Twitter and WhatsApp teachers group

conversations. The website is going to automatically

collect feeds from these OSN groups and display

them.

Mobile Phone

Teachers will use their own mobile phones to

subscribe to alerts from the website. These alerts will

be text messages (SMS) so that any mobile phone

will be able to receive them. The alerts will notify the

teacher of posts that they may be interested in and

encourage them to visit the website for details.

Overall Administrator

This is the person in charge of the website, the OSNs

pages and the WhatsApp teachers‘ group pedagogical

conversations. He/she will have full rights for

posting, updating, allowing and barring others

content in the website and the OSNs pages.

Subject Teacher

This is any teacher that has registered to the

community. They will be able to upload/download

subject content, participate in discussions, and obtain

alerts through their mobile phones.

3.8. Evaluation of the proposed model

The evaluation of any information system is an important task

performed before the information system model is kept into a

working solution. According to [2], evaluation is done on the

objective quantitative performance measures such as items

produced satisfaction surveys, and clients‘ feedback. The

evaluation process of this study was to obtain clients‘

feedback on the different component uses of the CoP and the

system entirely.

4. CONCLUSION

Communities of Practice (CoP) are learning platforms that can

facilitate professional development of teachers by helping

them share knowledge, skills and resources. A CoP

encourages teachers to express ideas, solve problems, and ask

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other teachers about challenges they‘re having. Among other

things, communities create a social fabric of learning to

improve professional development in areas with a large influx

of new teachers like that seen in Tanzania in recent years.

Here, we have suggested a model for an online community of

practice for Tanzanian teachers. This community will use

common social networks to connect teachers to each other,

featuring a central webpage with live feeds of social content.

Such a learning community would provide synchronous and

asynchronous settings to interact with and learn from other

teachers. The suggested model leverages online social

networks that many teachers already use to encourage them to

engage with each other on a regular basis. The central website,

combined with text message alerts, will help teachers monitor

discussions relevant to the subject(s) they teach. This model

will allow knowledge to be easily shared among teachers.

Pilot studies can be done to assess the effectiveness of our

model on a small scale using one social network as a test

sample.

ACKNOWLEDGEMENT

This paper has been completed based on the responses to

questionnaire conducted to Secondary School teachers and

School Inspectors from eleven different zones. Special thanks

for their inputs and ideas.

Figure 6: The Website Use Case Diagram

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REFERENCES

[1] A. L. Lukanga, ―Teacher Education in Tanzania: The

Experience of Pre-Service and In-Service Teacher

Preparation for Quality Education‖, 2008.

[2] E. Wenger, R. McDermott, and W. M. Snyder,

Cultivating Communities of Practice. Massachussets:

Harvard Business School Press, 2002.

[3] H. Saint-Once and D. Wallace, Leveraging Communities

of Practice for Strategic Advantage. USA:

Butterworth-Heinemann, 2003.

[4] K. Peffers, ―The Design Science Research Process: A

model for Producing and Presenting Information

System Research,‖ 2006.

[5] M. J. Keppell, Instructional Design: Case Studies in

Communities of Practice. USA: Information Science

Publishing, 2007.

[6] MoEVT, ―In-service Education and Training Strategy

for Primary School Teachers 2009-2013,‖ 2010.

[7] MoEVT, ―In-Service Teacher Training-INSET-

Strategy_2009-2013,‖ Dar-es-Salaam, Tanzania, 2010.

[8] R. W. Chediel, ―Teacher Education ( Preset and Inset )

Tanzania,‖ in 6th Quality Education Conference,

2013.

[9] URT, ―The Teacher Education Development And

Management Strategy- 2007/08 to 2010/11,‖ 2007.

Leyla H. Liana is with the School of Virtual Education,

The University of Dodoma. P.O. Box 490, Dodoma Tanzania.

e-mail: leylaliana86@ gmail.com).

Lucian V. Ngeze is with the School of Virtual Education,

The University of Dodoma, P.O. Box 490, Dodoma, Tanzania.

(e-mail: [email protected]).

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Abstract— Machine Type Communication (MTC) is a new

type of data communication between machines and devices

without human interactions. Long Term Evolution (LTE) is a

recent third Generation Partnership Program (3GPP) cellular

standard and is a promising technology to support future MTC

data traffic. This paper evaluates two existing handover

algorithms namely A2-A4-RSRQ and A3-RSRP. Based on the

analysis of the optimal settings of both algorithms, the

performances of the selected algorithms were compared and

the results proved that A2-A4-RSRQ performs better than A3-

RSRP. A2-A4-RSRQ handover algorithm is able to maintain

acceptable throughput and handover delay as per 3GPP

specification.

Keywords—Handover, Handover algorithm, LTE, MTC,

performance.

1. INTRODUCTION

HE world is developing into a networked society where

all kinds of devices interact and share information.

Advancements in cellular communication have resulted in

the emergence of Machine Type Communication (MTC) due

to the wide range coverage provision, low costs and high

mobility support of cellular networks. MTC (or Machine to

Machine (M2M) communications) is a form of data

communication among devices and/or from devices to a set of

servers that do not necessarily require human interaction.

MTC provides back-end connectivity anywhere and anytime

and consequently enabling creation of the so-called Internet-

of-Things (IoT) [12].

Long Term Evolution (LTE) is part of the 3GPP Release 8

specifications. It is an emerging technology that is designed to

deliver fixed, and more recently, mobile broadband

connectivity with higher peak data rates, greater flexibility for

heterogeneous networks and flatter network architecture. LTE

supports MTC/M2M applications and provides mobility

through fast and seamless handover mechanism for UE/MTC

device movement within the range of network coverage from

one base station to another in similar or different network.

LTE network can be used to create a rich set of M2M

applications, e.g., Smart Grid, Healthcare and Intelligent

Traffic System (ITS) [5], [11].

Several researchers have suggested LTE as a candidate to

support MTC. The use of cellular based MTC communication

has increased rapidly over the years. This is because cellular

communication systems are more adequate for majority of the

MTC applications as they are encompassing a wide range of

requirements including mobility, ease of deployment and

coverage extension. The concept of mobility offer several

advantages to MTC devices. They can stay connected by

handovers to the cells closer by as they move in the network

while maintaining their services. Seamless mobility anywhere

and anytime type of service provision has always been key

design principle for legacy cellular networks. Mobility is also

the requirement of several MTC applications such as ITS,

transportation and logistics and e-health for regular monitoring

[8], [6].

Since originally LTE has been designed to support Human

Type Communication (HTC) traffic, existing LTE handover

algorithms do not consider MTC data traffic features on

regular LTE traffic [6]. Most of handover algorithms were

optimized to support the requirements of HTC, thus it is vital

to present a comprehensive study on LTE and awareness of its

mobility capabilities in MTC devices especially in the motion

state so as handover will not compromise with the network

Quality of Service (QoS).

In cellular telecommunications, the term handover or

handoff refers to the process of transferring an ongoing call or

data session from one channel connected to the core network

to another channel [3]. A handover algorithm is used for

making a handover decision. A handover will be triggered if

several conditions specified by a handover algorithm are

satisfied.

There are many well known algorithms to carry out the

handover from source cell to target cell. In this paper we

provide a brief description of A3-RSRP (Strongest Cell

Handover Algorithm) and A2-A4-RSRQ handover algorithms.

Performance analysis is carried out using these two algorithms

due to the fact that they are basic algorithms in LTE network.

The A3-RSRP handover algorithm is also known as the

strongest cell handover algorithm. This algorithm is based on

Reference Signal Received Power (RSRP) measurements and

event A3 (neighbor cell‘s RSRP becomes better than serving

cell‘s RSRP). The idea is to provide each UE with the best

possible RSRP. This is done by performing a handover as

soon as a better cell (i.e. with stronger RSRP) is detected.

Event A3 is chosen to realize this concept [10].

A2-A4-RSRQ handover algorithm utilizes the Reference

Signal Received Quality (RSRQ) measurements acquired from

Handover Algorithm for Machine Type Communication in LTE Network

Nyaura Kibinda, Aloys N. Mvuma, and Anthony Faustine

T

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event A2 (serving cell‘s RSRQ becomes worse than threshold)

and Event A4 (neighbor cell‘s RSRQ becomes better than

threshold) [10]. Thus, the algorithm will add two measurement

configurations to the corresponding eNodeB (eNB) Radio

Resource Controller (RRC) instance. Their intended uses are

described as follows; Event A2 is leveraged to indicate that

the UE is experiencing poor signal quality and may benefit

from a handover. Event A4 is used to detect neighboring cells

and acquire their corresponding RSRQ from every attached

UE, which are then stored internally by the algorithm. By

default, the algorithm configures Event A4 with a very low

threshold, so that the trigger criteria are always true.

Many studies have been done concerning problems related

to handover algorithms for handover performance

optimization and evaluation. In [4], a handover algorithm

known as LTE Hard Handover Algorithm with Average RSRP

Constraint (LHHAARC) in order to minimize number of

handovers and the system delay as well as maximize the

system throughput was proposed. In [3] the performance of

LTE Hard Handover Algorithm (LHHA) and LHHAARC

taking the QoS parameters such as throughput, handover delay

of multimedia services such as audio, video through

simulation using JAVA platform was evaluated and compared.

The results obtained from simulation proved that LHHAARC

performs better than LHHA algorithm evaluated under

different circumstances. In [9] two kinds of handover

algorithms was introduced that adjust the handover parameters

of LTE eNB to improve the overall network performance. In

[7] the performance of LTE handover based on Power Budget

Handover Algorithm (PBHA) was investigated.

Due to the fact that MTC and LTE are emerging

technologies, many researchers have been devoted to conduct

studies in their performance optimization. However few

studies have been undertaken in evaluating the performance of

LTE handover algorithms in HTC and there is no proposed

handover algorithm for MTC among existing algorithms.

MTC promises huge market growth with expected 50 billion

connected devices by 2020 [15]. Support for such a massive

number of MTC devices has deep implications on the cellular

network performance. This research paper will propose a

suitable handover algorithm to support MTC in LTE network.

2. METHODOLOGY

2.1 Application Model

In order to analyze handover algorithm performance it is

often necessary to generate traffic. This is often accomplished

by using a packet generator specifically designed to generate a

specific pattern of traffic (for example, to match a measured

traffic source). In this research, a traffic model has been

designed to match with the behavior of a real generated traffic

using stochastic process. The major parameters for MTC

traffic model are the message size transmitted by the MTC

device and inter-send time i.e. time between transmissions of

two consecutive MTC messages. The message/data size varies

according to the MTC applications. According to [14], in

MTC applications such as ITS, devices transmit 64 Bytes of

data.

In the context of MTC applications, network has to face

increased load as well as possible surges of MTC traffic due to

massive concurrent data and signaling transmission, in which

case the inter-arrival time distribution of MTC devices may

follow Beta distribution over time [13]. Memory-less data

packet arrivals per MTC device are assumed, in order to

accurately model the traffic behavior of MTC applications

such as ITS, where data are triggered by random events.

Traffic inter-send time were modeled by researchers using

Beta distribution with two statistical parameters which are α

and β expressing the inter-send time. Based on an assigned

inter-send time, random traffic patterns obeying the Beta

distribution are easily generated for each MTC device utilizing

random number generators.

According to [13], numerical analysis showed that if the

arrival pattern follows Beta distribution, it could potentially

increase the mean sojourn time, mean waiting time of the

system and decrease server utilization. Beyond 3GPPs

proposals, if we want to choose an appropriate Beta

distribution for different MTC applications, it is better to let α

< β. Beta (2, 3) have been used to model the traffic inter-send

time of this research.

2.2 Network Simulation and Simulation Parameters

The research was done using a dynamic system level

simulator called Network Simulator-3 (NS-3). NS-3 is a

discrete event network simulation tool available for research

and educational purposes and is maintained, developed and

distributed by the NS-3 open source project [10]. Within the

scope of this work, NS-3 version 3.19 was used as the

modeling framework. This version of NS-3 contains

contributed modules for simulating LTE radio network nodes.

The handover scenarios were studied in simulated

environment using selected handover algorithms. In LTE the

triggering of handover is usually based on measurement of

link quality and some other parameters in order to improve the

performance. The parameters which have been used in this

study are threshold, offset, hysteresis (HO margin) and Time

to Trigger (TTT). These parameters will affect directly the

handover initiations and hence they can be tuned according to

certain design goals.

A2-A4-RSRQ and A3-RSRP handover algorithms with

optimal values have been used to evaluate the handover

performance of each algorithm. Also 0 km/h, 30 km/h, 60

km/h and 120 km/h device speed have been used with beta

distribution traffic model.

The system is modeled and simulated in the dynamic system

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level simulator NS-3. A radio network consisting of 7 cells of

5 MHz bandwidth with 25 resource blocks and 2.1 GHz

carrier frequency is built. These values were selected because

they provide the favorable cell peak throughput as per 3GPP

specifications [1], [2]. Figure 1 is the cellular layout of the

simulation.

A fixed number of MTC devices were randomly distributed

over the area with random initialized positions and they were

moving at different speeds in random directions. The most

relevant simulation parameters are listed in the Table 1.

3. RESULTS AND ANALYSIS

Performance metrics used in this study are; number of

handover, throughput and handover delay.

3.1 A2-A4-RSRQ Handover Algorithm Results

According to Figure 2, the average handovers per MTC

device represents the number of handovers that occur during a

simulation. From the results, number of handovers increases

as threshold increases in respect with increase of speed. A2-

A4-RSRQ handover algorithm with 30 dB threshold generates

the highest number of handovers.

Figure 1: Cell Network Layout

Figure 2: Number of Handover/MTC Device versus Speed

(A2-A4-RSRQ)

Figure 3: Throughput versus Speed (A2-A4-RSRQ)

Figure 3 demonstrates that the A2-A4-RSRQ handover

algorithm with 30 dB threshold has the highest throughput.

Also, in each speed setup there is increase in throughput as

threshold increases. However there is an anomalous

observation when the speed is at 30 km/h there is increase in

TABLE I

SIMULATION PARAMETERS

Parameter Value

Cellular layout 7 three-sectored sites in hexagonal layout

(21 cells in total)

Channel model Typical urban

Number of MTC

devices (UE)

100 MTC devices randomly distributed

around the sites

Inter-site distance 500m

Antenna type Parabolic antenna

Antenna parameters

(eNB)

Beam width: 70 dB Maximum attenuation:

20dB

UL Bandwidth 5 MHz

Carrier frequency 2.1 GHz

eNB Tx power per

sector 46 dBm

Traffic type MTC data traffic

Traffic data size 64 Bytes

Data inter-send time Beta (2, 3)

Data rate 250 kbps

Mobility Model Steady state Random way Point (RWP)

MTC devices position Randomly distribution

MTC device speed 0, 30, 60, 120, 180 [km/h]

Path loss model LOS, hybrid buildings propagation model

Simulation duration 50 s

Run number 5

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throughput for the all thresholds values.

Figure 4 depicts the results for handover delay in A2-A4-

RSRQ handover algorithm in five speed scenarios. As speed

increases the handover is more likely to occur which results in

an increasing handover delay under all settings. However

peculiar behaviors for some thresholds (30 dB, 20 dB and 10

dB) are observed at 120 km/h, in which there is decrease in

handover delay while the speed is high. Moreover there is a

slight decrease of handover delay at 180 km/h for 5 dB

threshold when the speed is at 60 km/h. However A2-A4-

RSRQ with 30 dB threshold has minimum average delay.

Figure 4: Handover Delay versus Speed (A2-A4-RSRQ)

The performance of all threshold settings as shown in Figure

2, 3 and 4 respectively, demonstrate that increasing the

threshold value results in increase in number of handovers,

increase throughput and decrease handover delay. However,

the threshold of 30 dB gave the optimal results for A2-A4-

RSRQ handover algorithm as compared with other threshold

settings based on several performance metrics. Table 2

summarizes the results for A2-A4-RSRQ handover algorithm.

3.2 A3-RSRP Handover Algorithm Results

Figure 5 depicts that, A3-RSRP handover algorithm with

settings of 9 dB hysteresis and 64 ms TTT gives minimum

number of handovers when compared with the 3 dB hysteresis

and 64 ms TTT. The same tendency is observed for 9 dB

hysteresis and 2560 ms TTT and 3 dB hysteresis and 2560 ms

respectively. A3-RSRP handover algorithm with 3 dB

hysteresis and 256ms TTT triggers more handovers than other

settings for 120 km/h and 180 km/h.

Figure 6 illustrates that A3-RSRP handover algorithm with

3 dB hysteresis and 64ms TTT and 3 dB hysteresis and 256

ms TTT settings generates higher throughput when compared

with 9 dB hysteresis and 64 ms TTT and 9 dB hysteresis and

256 ms TTT and 9 dB hysteresis and 2560 ms settings

respectively as speed increases.

Figure 5: Number of Handover/MTC Device versus Speed

(A3-RSRP)

Figure 6: Throughput versus Speed (A3-RSRP)

Figure 7: Handover Delay versus Speed (A3-RSRP)

TABLE 2

RESULTS SUMMARY FOR A2-A4-RSRQ HANDOVER

ALGORITHM

Performance Metric Threshold

Minimum number of handovers 5 dB

Maximum throughput 30 dB

Minimum handover delay 30 dB

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Figure 7 shows that 9 dB hysteresis and 64 ms TTT and 9

dB hysteresis and 2560 ms TTT settings results in higher

handover delay as compared with 3 dB hysteresis and 64 ms

TTT and 3 dB hysteresis and 256 ms TTT as speed increases.

Results in Figure 5, 6, and 7, demonstrate that, increase in

hysteresis lead to decrease in number of handovers, decrease

throughput and increase in delay as speed increases

respectively. UE speed is a very important aspect which has

great influence in handover performance. High speed

UE/MTC device goes through the cells frequently.

Accordingly, they will perform handover frequently, and

obviously it will degrade network performance. Moreover,

TTT has the effect in the performance, although it can mitigate

the ping-pong effect (unnecessary handovers), it also causes

radio link failure due to delayed handover. Observed drop of

throughput and increase in delay at high TTT is due to delayed

handover which causes radio link failure. Table 3 shows the

results summary. From the results, A3-RSRP handover

algorithm with 3 dB hysteresis and 256 ms TTT has optimal

results in all performance metrics compared with the rest of

the settings.

Proposed Algorithm is based on comparison between A2-

A4-RSRQ and A3-RSRP handover algorithms with optimal

settings respectively. Figure 8, 9 and 10 demonstrate the

results of both handover algorithms with optimal settings.

Figure 8: Number of Handover/MTC device versus Speed

(Optimal Settings)

Figure 8 shows that A2-A4-RSRQ handover algorithm with

30 dB threshold generates minimum number of handovers.

Also Figure 9 demonstrates that throughput is as good as A3-

RSRP handover algorithm with 3 dB hysteresis and 256 ms

TTT throughput. However, A2-A4-RSRQ handover algorithm

with 30 dB threshold has slightly higher delay due to lack of

TTT mechanism at all speed scenarios as compared with the

other handover settings as Figure 10 depicts. Table 4 shows

the results summary of A2-A4-RSRQ and A3-RSRP handover

algorithms.

Figure 9: Throughput versus Speed (Optimal Settings)

Figure 10: Handover Delay versus Speed (Optimal Settings)

Finally based on the result data obtained we can conclude

that A2-A4-RSRQ performs better compared to A3-RSRP

TABLE 4

RESULTS SUMMARY FOR A2-A4-RSRQ AND A3-RSRP

HANDOVER ALGORITHMS

Performance Metric Algorithm

Minimum number of handovers A2-A4-RSRQ

Maximum throughput A2-A4-RSRQ and A3-RSRP

Minimum handover delay A3-RSRP

TABLE 3 RESULTS SUMMARY FOR A3-RSRP HANDOVER ALGORITHM

Performance Metric Hysteresis/TTT

Minimum number of handovers 9 dB/2560 ms

Maximum throughput 3 dB/64 ms and 3 dB/256 ms

Minimum handover delay 3 dB/256 ms and 9 dB/256 ms

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handover algorithm. We conclude this by analyzing the

decrease in number of handovers. A2-A4-RSRQ can

effectively reduce the number of handovers per MTC device

compared to A3-RSRP in all speed scenarios. According to

3GPP specifications in properties and requirement of MTC,

A2-A4-RSRQ with 30 dB threshold is the winning handover

algorithm for MTC applications especially in ITS use case.

4. CONCLUSION

The focus of this paper has been on determining the best

handover algorithm for MTC in LTE network. Since the

setting of handover triggers is of primary importance for a

good performance of the handover procedure, different

triggering settings for the selected handover algorithms have

been performed. The performance of both algorithms have

been compared based on performance metrics under different

UE speed scenarios considering the MTC data in ITS. The

performance results prove that A2-A4-RSRQ handover

algorithm performs better than A3-RSRP handover algorithm.

This paper has considered for handover algorithms

performance evaluation simple deployment scenarios due to

software limitations. However, in future research it would be

advisable to investigate the best handover algorithm in LTE

network for MTC considering more complex scenarios such as

larger cells, higher speeds and high loaded systems. Also other

optimization parameters and handover algorithms would be

used. Also unusual behavior in some handover settings should

be investigated e.g., slightly increase of throughput at 30km/h

for A2-A4-RSRQ handover algorithm and variation in number

of handovers for A3-RSRP handover algorithm with 9 dB

hysteresis and 2560 ms TTT settings.

REFERENCES

[1] 3GPP, ―Service Requirements for Machine-Type

Communications‖, Technical report, TR 22.368, 2012.

[2] 3GPP, ―Technical specification group services and system

aspects: Service requirements for Machine-Type

Communications (MTC)‖, release 10, 2010.

[3] B. V. Arun, and D. Jayaramaiah, ―Performance

Evaluation of LTE Hard Handover Algorithm with

Multimedia Data Transmission‖, International Journal of

Innovative Research in Computer and Communication

Engineering (IJIRCCE), vol. 2, no. 4, pp. 3906-3912,

2014.

[4] C. Lin, K. Sandrasegaran, H. A. Ramli, and R. Basukala,

―Optimized performance evaluation of LTE hard

handover algorithm with average RSRP constraint‖,

International Journal of Wireless and Mobile Networks

(IJWMN), vol. 3, no. 2, 2011.

[5] E. Dahlman, ―LTE 3G Long Term Evolution‖, Expert

Radio Access Technologies Ericsson Research, 2007.

[6] I. F. Akyildiz, J. Xie and S. Mohanty, ―A survey of

mobility management in next-generation all-IP-based

wireless systems‖, IEEE Wireless Communications, vol.

11, no. 4, pp. 16-28, 2004.

[7] J. Chavarría, ―LTE Handover Performance Evaluation

based on Power Budget Handover Algorithm‖, M.S

thesis, Univeristy of Politecnica de Catalunya (UPC),

2014.

[8] K. Jun, ―Enabling Massive Machine-to-Machine

Communications in LTE-Advanced‖, in Grid and

Pervasive Computing. vol 7861, J. James, H. Arabnia, C.

Kim, W. Shi and J. Gil, Eds. New York: Springer, 2013,

pp. 563–569.

[9] L. Luan, M. Wu, J. Shen, Y. Junjun and H. Xian,

―Optimization of Handover Algorithms in LTE High-

speed Railway networks‖, International Journal of Digital

Content Technology and its Applications (JDCTA), vol.

6, no. 5, 2012.

[10] NS-3. (2014, May 7). NS-3-model-library [online].

Available: http//www.nsnam.org/docs/models.

[11] T. Ali-yahiya, Understanding LTE and its Performance,

New York, Springer, 2011, ch. 1.

[12] T. Taleb and A. Kunz, ―Machine Type Communications

in 3GPP Networks: Potential, Challenges, and Solutions‖,

IEEE Communication Magazine, vol. 50, no. 3, pp. 178-

184, 2012.

[13] X. Jian, X. Zeng, Y. Jia, L. Zhang and Y. He, ― Beta/M/1

Model for Machine Type Communication‖ , International

journal of Electrical and Electronics (IEEE), vol. 17, no.

3, pp. 584-587, 2013.

[14] Y. Mehmood, ―Machine-to-Machine Data Traffic

Multiplexing in LTE-Advanced Network‖, M.S thesis,

Dept. Electric. Eng, National University of Science and

Technology, Islamabad, Pakistan, 2014.

[15] Y. Morioka, ―LTE for Mobile Consumer Devices‖,

Workshop on Machine to Machine Standardization, ETSI,

2011.

Nyaura Kibinda received the Bsc. degree in

telecommunications engineering from the University of Dar es

Salaam, Tanzania in 2010, the M.Sc. degree in

telecommunications engineering from The University of

Dodoma (UDOM), Tanzania in 2014.

Aloys N. Mvuma |(M‘2003) received the B.Sc degree in

electrical engineering from university of Dar es salaam in

1994, the M.Sc. in information science from Shimame

University, Japan in 2000 and Doctor of Engineering in

Systems Engineering from Hiroshima University, Japan in

2003.

Anthony Faustine is an assistant lecturer of

Telecommunications at the University of Dodoma. He has a

Msc. in Telecommunications engineering from the University

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of Dodoma. His research interests include mobile

communications, wireless sensor networks, mobile sensing

systems and Machine to Machine communications for Smart

grids.

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Abstract—Since adoption and application of ICTs in

governance entails loss of power to the powerful while

somehow empowering the weak, it will always be somehow

resisted. This paper discusses the depth and breadth of various

barriers to adoption of ICTs in Higher learning Institutions

(HLIs) in Tanzania while giving details on how fear for loss of

power and other challenges is responsible for the lukewarm

attitude towards adoption of ICTs in HLIs governance.

Keywords—ICT, Perceived barriers, Governance, loss of

power, vested interests.

1. INTRODUCTION

T is clear that Information and Communication Technology

(ICT) has brought tremendous changes in the way people

live and interact with one another as well as how tasks and

functions are performed in organizations. However, the use of

ICT in governance functions is not a straight-forward process

since it depends on the degree of individuals‘ willingness to

adopt and use it. One would have expected to see ICT being

used in all possible functions of governance in HLIs as they

are knowledge-producing institutions which are expected to

show the way. It is obvious that one may wonder as to why

ICT in Tanzania has received voluntary massive adoption

from users in areas like social networks (Face book, Twitter,

Instargram, LinkedIn, Whats-App, and Viber) and in

electronic money transactions (M-Pesa, Airtel Money, Tigo

Pesa) while in areas related to governance of institutions the

situation is completely different. This manifests a scenario

where the same society portrays two diverging tendencies

towards the same technology.

The study by [5] identifies two technology acceptance

models as; perceived usefulness (PU) and perceived ease of

use (PEOU). Perceived usefulness implies the acceptance of

the technology when it is useful, i.e. facilitates better job

performance and rejection of the technology when it is

believed to be not useful. Perceived ease of use means

acceptance of technology when it does not require too much

effort to use it. These two constructs are inseparable in a sense

that they all depend on one another to determine acceptance of

technology by people. A close scrutiny on Tanzanian reality as

far as acceptance of ICT is concerned reveals a dichotomy; at

intuitional level such as Tanzanian HLIs, the theory is

contradictory while on the private side Davis‘s theory in [5]

holds true.

In the same direction, Rogers in [3] summarizes technology

acceptance in; relative advantage, compatibility,

complexity/simplicity trialability and observability. In

practice, ICT as a technology that was supposed to be

embraced in Tanzanian HLIs passes all tests pertaining to

Rogers requirements. However, on various cases of ICT in

Tanzanian HLIs, acceptance and adoption has failed where

conditions are obviously favorable while it has had

tremendous success when the application is destined to save

outside institutional realm.

The same trend holds when Baguma's dimension

extrapolation is applied [11]. Baguma in [11] identifies four

dimensions with which ICT is applied. These include; e-

Administration, e-Citizen, e-Services and e-Society, whereby

e-Administration refers to application of ICT to share

information and delivery of services by using networked

management information systems. Again, it seems that in

Tanzania, e-Citizen, e-Services and e-Society have, by far,

suppressed e-Administration, despite the fact that investment

in adoption of e-Administration in the form of training and

deployment surpasses the other three by far.

Despite some obvious opportunities that are being missed

due to the lukewarm trend towards adoption of ICT in

Tanzanian HLIs, the barriers have remained unbeatable.

Among the major benefits that are missed due to slowness in

adopting ICT in Tanzanian HLIs include; environmental

degradation caused by unnecessary use of paper where ICT

could have been an alternative. Conducting meetings at all

levels requires a lot of paper which costs large sums of money.

Speed and convenience in governance is compromised in

cases where manual paper functions are still employed instead

of ICT in governance. These are just a few of the obvious

advantages that the use of ICT in governance of HLIs would

bring about. Despite these obvious advantages, the speed of

adopting ICT in HLIs governance is still lukewarm.

It is obvious that ICT has proved its usefulness beyond

contention. It is further evident that it is not difficult to adopt

the use of ICT in various institutions, including HLIs in

Tanzania to a level that may be beneficial [8]. In the same

society, some technologies have been adopted sufficiently fast

Investigating Barriers to use ICT as a Tool for Governance in Higher

Learning Institutions (HLIs) in Tanzania

Paul Loisulie and Leonard Mselle

I

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at the individual realm, while at corporate level, specifically in

HLIs, the case is opposite, despite the obvious benefits. That

ICT has not been fully embraced in Tanzanian HLIs can

somehow be explained by resistance; both conscious and

unconscious, originating from the elite on one part and

operational personnel on the other part.

The main objective of this paper is to unearth the most

salient barriers to adoption of ICT as a tool for corporate

governance in HLIs in Tanzania. The paper investigates the

extent to which fear for loss of power and empowerment has

influenced the trend of adoption of ICT in the governance of

HLIs. Currently, there is no evidence of a thorough study

which has delved on the factors that have hindered

proliferation of ICT in the governance of Tanzanian HLIs.

Two questions guided this study;

1. What are the most salient barriers to the successful

adoption and use of ICT in the governance of HLIs in

Tanzania and how have they negatively affected the

adoption of ICT in Tanzania and Tanzanian HLIs, in

particular.

2. To what extent do these barriers constitute a subtle

―fear for loss of power by the elite‖ and dangers for

vested interests by operational personnel?

2. METHODOLOGY

Documentary and systems review is used to find the general

trend in adoption of ICT in Tanzania and, in particular, in

some HLIs. The analysis of automated systems in three HLIs

is used to reveal the subtle resistance to the adoption of ICT in

some functions in HLIs. Interviews on purposively selected

respondents from two HLIs were carried out to find out the

extent to which résistance against ICT due to fear for loss of

power and vested interests is responsible for the lukewarm

adoption of ICTs in Tanzanian HLIs. The identified

institutions are the University of Dodoma (UDOM) and Saint

John University of Tanzania (SJUT). The respondents were

Management officials, IT experts, legal officers, Human

Resources officer, as well as finance, records management and

procurement and supplies specialists. Other respondents were

students from the College of Informatics and Virtual

Education (CIVE) - UDOM who developed and maintained

two information systems namely; University of Dodoma

Student‘s Records (UDOM-SR) and the University of

Dodoma Students Voting System (USVS).

3. RESULTS AND DISCUSSION

3.1 The legal framework/environment

Documentary review reveals that the Tanzanian legal

environment does not support application of ICT as a tool for

governance. In this case, the HLIs cannot be an exception. For

ICT to be adopted as a tool for governance, proper legal

framework must be established. Among the laws needed to

enable adoption and application of ICT in governance are; the

law of evidence act, Cyber Crime Act, Records Archives Act

and other laws related to ICT applications. The fact that up to

now the Tanzania Law does not accommodate admissibility of

electronic evidence in court proceedings points to very serious

barrier in adoption of ICT in governance. What is more

appalling is that, as serious as this barrier may be, there are no

concerted efforts, so far, to eliminate it. Review of judicial

reasoning against acceptance of electronic evidence in the law

point to some sort of fear. Judge Nyangarika‘s ruling in [7] on

the commercial case in the High Court of Tanzania in 2013

proves categorically that the legal environment is still afraid of

electronic evidence and e-corporate governance, for that

matter. In one of his statements from the ruling, he

emphasizes:

It must be born in mind that electronic evidence must

be authenticated because of the potential for

unauthorized transaction or of the processing of such

evidence. There is also a need to know the history,

source and custody of such kind of evidence.

This contention is fair as far as authenticity of evidence is

concerned. However, the need for such caution seems to foster

acerbic assertion against electronic evidence mainly from

those who find it beneficial to resist electronic information

systems.

Another testimony from the court ruling is found in the

labor dispute by Judge Mipawa in [6]. Justice Mipawa

maintains:

It will be dangerous for the court to receive [the

email document] because it can be made anywhere

by anyone; it is not even signed because the

authentically of the document must be signed, this is

a report which was allegedly prepared; we don’t

know if it was himself who made it because there is

no signature of the maker.

Again, it may be said that the justice is too hostile of

electronic documentation in his statement.

A follow up discussion with 6 legal officers revealed some

kind of individual sympathy towards electronic evidence.

However, there is no evidence of concerted efforts in the

Tanzanian legal realm for adoption of electronic evidence.

Sections 78 and 79 of the Evidence Act of 2002 explain the

procedure for proof and verification of banker‘s book but it is

silent about other electronic documentations and records.

Worse enough, even the written Laws (Miscellaneous

amendments) act No. 2 0f 2006 and No. 15 of 2007 do not

address the barrier of admissibility of e-evidence in the court

proceedings except for criminal proceedings [16].

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3.2 Confidentiality of classified information

Both Public Standing Orders of 2009 [15] and Employment

and Labor Relations Act No. 6 of 2004 [13] are still not user-

friendly for smooth adoption and application of ICT in

governance in Tanzania. Both Laws have restrictions on

electronic delivery of information. They encourage the

traditional motor-an-brick handling of information where a

small minority of elite holds information to its favor, doing all

that is possible to deny its accessibility to the rest of people.

Electronic format of information is not friendly to the sort of

documents labeled ‗confidential‘, ‗secret‘ or ‗top secret‘. The

employment and Labor Relations Act No. 6 of 2004 [13] gives

the true picture of the matter when it dictates that an employer

shall not be obliged to disclose information that is legally

privileged; the employer cannot disclose, without

contravening a law or an order of court, information that is

confidential and; if disclosed, may cause substantial harm to

an employee or the employer, is private personal information

relating to an employee without that employee's consent.

Standing Orders for the Public Service [15] is another clear

testimony that laws are not user-friendly for adoption and

application of ICT in governance. The standing orders put

clearly that;

In case of correspondence made through fax or e-mail, the

originator shall have the responsibility of classifying and

marking such information as ―confidential‖, ―secret‖ or ―top

secret‖ before faxing or e-mailing. For avoidance of leakage

of information, every organization shall have only one official

fax machine placed at the office of the Chief Executive

Officer of the organization concerned. For easy handling of

confidential information received through fax or e-mail there

shall be designated a public servant or public servants to

handle such information.

Confidentiality and secrecy are the necessary elements in

bureaucracy and they must be protected. However, in the case

of Tanzania, as revealed from the discussion, confidentiality

requirements are over-emphasized, mainly to justify fear of

losing power among the elite. A close scrutiny indicates that

defense for confidentiality and secrecy is not made for the

benefit of the corporate. Rather, this is done at the expense of

corporate transparency and fairness.

3.3 Fear of breaking from tradition

The traditional filing and records system is well established

and is largely used throughout the public service. Most of

interviewees were of the opinion that hard copies and physical

filing systems are more secure than electronic system and

therefore they should be maintained. Electronic system,

though not entirely rejected, seems to be skeptically perceived

without concrete reason other than clichés. One of the senior

officers from a human resource department in of the HLIs

contends;

There is a general traditional belief that formal

communication in public sector in particular

becomes formal if, and only if, it is presented on

paper or hard copy. A good example is promotion

letters whereby employees will be comfortable when

they receive hard copies of promotion letters.

The same human resource officer quoted above added;

Although issues like internet problems, low

bandwidths and poor power supply are not strong

barriers to adopt ICT in governance, as compared to

the well established system; when any one or all of

these are experienced, people will quickly abandon

the online system and resort to the system they are

used to.

Some of the interviewees agreed that threat to the status quo

is one of the reasons for lukewarm acceptance of automation

in HLIs, as quoted hereafter;

Use of ICT in governance is a big threat to the

current status quo as it facilitates sharing of

information, transparency and work relations to an

extent of necessitating need for new ways to govern

organizations.

Skepticism towards automation in HLIs is not only confined

to the elite. Through discussion and system review it was

found that some employees prefer paper mode of

communication to online especially where the electronic

system seems to threaten their vested interests. One typical

case concerns the introduction of electronic room allocation

system at UDOM which was strongly supported by the

management (because it would improve revenue collection)

but vehemently resisted by the wardens who otherwise viewed

the system as a threat to their personal powers and a loss of

illicit revenues that was made possible through the manual

system.

4. DISCUSSION AND CONCLUSIONS

Fear for loss of power by the elite is the most hard to

surmount barrier towards adoption of ICT in Tanzanian HLIs.

Partly it is hard to surmount due to fact that it is disguised in

other obvious barriers such as the legal system of the country.

Lukewarm acceptance of ICT for governance in Tanzania is

not particular to such institution because a general scrutiny

shows that the legal system maintains obvious hostility

towards adoption of ICT in governance in Tanzania. These

two trends (legal hostility and lukewarm acceptance) have

established a symbiotic relationship whereby; the elite in HLIs

have used the legal reason to retard automation, whenever it is

convenient; while on the other hand, this retardation has left

the judicial skepticism towards ICT unchallenged. In the case

of the operational personnel the barriers stem from vested

interests disguised as skepticism in breaking from traditional

ways.

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In those cases where the elite power is not threatened and

vested interests are not endangered, automation seems to have

sailed smoothly. As an example, at UDOM, electronic systems

have been successfully proposed, developed and implemented

whenever such systems were judged as positive by both the

elite and the operational personnel. Through systems review, it

was revealed that UDOM-SR, which is a students‘ record

system used in UDOM for all students‘ records ranging from

registration, admission and the whole academic history, has

had a successful discourse. Economically, the system is home-

made, meaning that planning and development costs are

negligible. Consequently, maintenance and running costs have

been negligible. The system has been serving UDOM for 5

years now with absolute satisfaction. Success of this system

has led UDOM administration to freeze software purchases.

Consequently, UDOM has introduced other systems for

students‘ voting and has automated room-allocation. However,

this success has not been exploited to automate much simpler

functions such as general filing functions, staff appraisal and

cash flow operations. The possible explanation would be the

same fact of ―fear for loss of power by the powerful‖ and

vested interests on the part of operation personnel. ICT seems

to be easily accepted for those functions which do not threaten

the exclusive ―information privileges‖ of the elite and vested

interests of the operational personnel. The situation is not

different in the case of the University of Dar es Salaam where

ARIS has wholly been accepted as an automatic system for

students‘ records, while staff evaluation and most of the

financial functions still remain manual.

There exist some slight differences in case of the Open

University of Tanzania (OUT) where at least the filing

functions and internal memos systems have been automated.

In general, it is difficult to label this lukewarm acceptance

of technology as resistance because there is no record of direct

rejection [1], [10]. Rather, the trend seems to have been

facilitated by lack of enthusiasm towards ICT, on the part of

the elite, whenever this technology seems to threaten their

privilege of governance and on the part of operational

personnel whenever automation seems to threaten their status

quo.

This paper investigated the perceived barriers to adoption of

ICT as governance tool in HLIs in Tanzania, as performed by

[9]. It is obvious that ICT adoption in Tanzania has passed

through both Perceived Usefulness and Perceived Ease of Use

as manifested in the investigated cases and in [8]. At

individual level, ICT adoption has been more than a success;

while at corporate level, success has been hindered by tacit

fear of loss of power on the side of the elite while vested

interests are the major hindrances as championed by

operational personnel. At corporate level, where it is

convenient, the elite and the operational personnel have

supported the adoption of ICT in governance. Unless some

mechanisms are devised to address these barriers adoption of

ICT in the governance of HLIs in in Tanzania in particular and

the country in general will remain lukewarm for an

unnecessary long period.

REFERENCES

[1] B. Michiel, ―E-governance and developing countries

introduction and examples‖, The Hague: International

institute for communication and development (IICD),

2001.

[2] D. Sawe, ―How Societies Benefit from Open Access to

ICT‖, 5th International Conference on Open Access, Dar

es Salaam: PO-PSM. 2007.

[3] E. M. Rogers, ―Diffusion of innovations‖ 5th ed. Free

Press, New York. 2003.

[4] E. Lwoga, Making learning and Web 2.0 technologies

work for higher learning institutions in Africa. Campus-

Wide Information Systems, 29(2), 2012, pp. 90–10,

doi:10.1108/10650741211212359.

[5] F. D. Davis, ―Perceived usefulness, perceived ease of

use, and user acceptance of information technology‖, MIS

Quarterly , (1989) 319–340.

[6] J. Mipawa, ―Ruling of the Labor Dispute No. 30 of 2010

between Mwaikenda Ambokile Michael – Complainant

versus Interchick Co. LTD – respondent‖; High Court of

Tanzania – Labor Division, Dar es Salaam, 05/02/2014

and 20/03/2014.

[7] J. .Nyangarika, ―Ruling: Commercial Case No. 29 0f

2011: Exim Bank (T) LTD – Plaintiff versus Kilimanjaro

Coffee Co LTD – Defendant; High Court of Tanzania –

Commercial Division, Dar es Salaam‖, 24.07.2013 and

24.07.2013.

[8] J. S. Mtebe, ―Acceptance and Use of e-Learning Solutions

in Higher Education in East Africa‖, Dissertations in

Interactive Technology, Number 18 Tampere 2014, ISBN

978-951-44-9628-8 (pdf), http://tampub.uta.fi.

[9] J. S. Mtebe, & R. Raisamo ―Investigating Perceived

Barriers to the use of Open Educational Resources in

Higher Education in Tanzania‖, International Review of

Research in Open and Distance Learning, 15(2), 2014,

pp.43–65.

[10] P. M. Wulystan. R. Bernad, C. M. Andrew and R. Sanare,

―Using Mobile Phones for Teaching and Learning

Purposes in Higher Learning Institutions: the Case of

Sokoine University of Agriculture in Tanzania,

Proceedings and report of the 5th UbuntuNet Alliance

annual conference, 2012 pp 118-129.

[11] R. Baguma, ―Affordable e-governance using free and

open source software‖, In V. Baryamureeba, & Williams

(Eds.), Information and Communication Technology for

sustainable development; Measuring computing research

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excellence and vitality, Kampala: Fountain Publisher

2006, pp. 199-208.

[12] Snell and Bohlander, Managing Human Resources, 14th

Ed: New York Thompson Learning Inc., 2007.

[13] URT, Employment and Labor Relations Act, Dar es

Salaam, 2004.

[14] URT (2003), National Information and Communications

Technology Policy: Dar es Salaam, Ministry of

Communications and Transport.

[15] URT, Standing Orders for the Public Service: Pursuant to

S.35 (5) of the Public Service Act, Cap.298, Dar es

Salaam. 2009.

[16] URT, The Evidence Act; CAP. 6 R.E. 200, Dar es

Salaam, Government Printers, 2002.

Prof. Leonard J. Mselle is currently a senior lecturer in the

College of Informatics and Virtual Education (CIVE) of the

University of Dodoma. Dr. Mselle is a renowned researcher in

the field of technology diffusion. He is currently a member of

Elsevier –editorial board. Dr. Mselle can be contacted through

[email protected]

Paul Loisulie is an assistant lecturer in the College of

Education of the University of Dodoma. He is currently

pursuing PhD in the field of technology diffusion and

adoption, specifically in relation to the management of

academic institutions.

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Abstract— This study is to investigate the viability of using

Online Social Networks (OSNs) as e-learning platforms in

Tanzanian Universities. Students from the University of

Dodoma constituted the sample for this study. Observation

and questionnaires were used as a research instruments. The

findings indicate that most active users of OSNs are

individuals aged between 21 and 30 years. The most preferred

site is Facebook followed by Google. Most students have the

required experiences, skills and drive for effectively using

OSNs as educational platforms. It is determined that the type

of course that a student is pursuing has influence on students‘

perceptions, attitudes as well as experiences towards the use of

OSNs as e-learning platforms.

Keywords: E-learning, Online Social Networks, Web 2.0,

University students.

1. INTRODUCTION

ITH the emergence of Web 2.0 technologies, Online

Social Networks (OSNs) have become a popular way

of sharing information and platforms for educational purposes.

OSNs such as Twitter, MySpace, Facebook and LinkedIn have

attracted millions of users and they are used for various

purposes, including education.

Reference [8] contends that OSNs have been exploited as

well-liked platforms for information-sharing, communication

and knowledge dissemination. Users of these networks are

predominantly young college students, and teenagers. Indeed,

the popularity of social networking is highly demonstrable by

the number of people using those OSNs. The integration and

use of social networking technology as an e-learning tool is

promising for distance education.

Recently, there has been an overwhelming interest in the use

of OSNs among university students and the emphasis on

developing 21st century competencies. In practice, higher

education institutions are still primarily relying on traditional

Learning Management Systems (LMS) that do not fully

capitalize on the potential of social media for enabling

participation in global learning networks, collaboration and

social networking [10].

Reference [14] posit that, while Web 2.0 participatory

technologies have become an essential part of young learners‘

daily lives, very few institutions are taking full advantage of

these technologies to support their learning processes. In

Tanzania ICT in education is still underdeveloped. The

learning process in Tanzania is still facing a number of

problems including lack of teachers/instructors and study

materials such as books [5]. Despite the potential offered by

ICT, on-line learning in general is still at its initial stage [7].

Regardless of their reported success, LMSs are relatively

inflexible systems for many students. In LMSs, the standard

organizational unit is the course, and this structure restricts

students to the content designed for a particular course and to

interact only with other participants of the course. Therefore,

students‘ engagement in LMS is lower in comparison to the

engagement in other environments or tools such as mobile

devices, Web 2.0 tools or game consoles. These environments

provide opportunities for customization, communication and a

sense of ownership which is impossible in the current LMSs

[9].

Use of OSNs as platforms where different people meet and

share their experience by publishing their ideas, action and

even events, can create a new learning environment in the

context of different learning settings. These sites can support a

range of applications associated with educational technologies

already in use at the university level such as communication,

participation, interactivity and collaboration. The current study

is aimed at determining the extent of the use of OSNs as e-

learning platforms in Tanzania higher learning context and

predicts the trend on the use of OSNs as educational

platforms.

Along with the development of Web 2.0 technologies, OSNs,

such as Facebook.com, MySpace.com and Twitter, have

become more and more popular. There are millions of people,

using these sites to share their stories, meet new friends, and

catch up with old friends. In addition to the features of any

other websites, OSNs websites are different from old web

which uses Web 1.0 technologies.

Reference [6] contends that, there is a clear separation

between a set of highly popular Web 2.0 sites such as

Facebook and YouTube, and the old web. These separations

are visible when projected onto a variety of axes such as

technological scripting and presentation technologies used to

render the site and allow user interaction); structure (purpose

Investigating the Viability of Using Online Social Networks as E-Learning

Platforms in Tanzanian Universities

W

Carina Titus and Leonard J. Mselle

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and layout of the site); and sociological (notions of friends and

groups). So OSNs enable users to share and exchange their

ideas, event and activities. They allow users to take part in

online social activities.

OSNs are becoming smarter. By memorizing user

behaviors, such as websites browsed, music listened to, friends

talked to and articles read. Social websites will understand the

basic interests of the person. According to [3], next-generation

sites, called Social Networking 3.0, may in fact be perceived

as spooky in the level of accuracy of this, artificial

intelligence. Reference [3] maintains that, social bookmaking

site functionality such as Digg will be married with OSNs

enhanced with self-learning technology.

Despite these potentials, OSNs are hardly mentioned or

discussed as a means for educational delivery in Tanzanian

Higher Learning Institutions (HLIs).

2. METHODOLOGY

The specific pool of population that this research studied were

students of the University of Dodoma (UDOM) amounting to

15,049 enrolled in full- or part-time studies as of May 2014.

Respondents were selected randomly from six different

colleges i.e. College of Humanities and Social Sciences

(CHSS), College of Education (CoED), College of Informatics

and Virtual Education (CIVE), College of Earth Sciences

(CoES), College of Natural and Mathematical Sciences

(CNMS), and the College of Health Sciences (CoHS). This

allowed every individual to have an equal chance to be

selected for the study. To calculate the sample size, a formula

from Yamane [13] was adopted as follows:

n =N/1+N (e²) (4.1)

where n=sample size, N=Population size and e= the level of

precision 5% at 95% level of confidence. Then n

=15,049/1+15049(0.05)². Almost 390 respondents participated

in the study. Proportionate sampling was employed to obtain a

sample size from each college depending on the population

size. The questions were designed to find out the attitudes,

experiences and perceptions of UDOM students towards the

use of OSNs as e-learning platforms. Data was analyzed

using logical analysis approach and the Statistical Package for

the Social Sciences (SPSS) software. The final outcomes from

the questionnaire survey were summarized to directly or

indirectly provide answers to research questions.

3. RESULTS AND DISCUSSION

The results show that of the 388 respondents, 2% were less

than 20 years, 94% were between 21-30 years, and 4% were

31-40 years old.

3.1. Internet Access Modes

78.6% (301) of respondents confirmed that they access the

internet via modem, 63.2% (242) indicated that they relied on

their cell phones, 20.9% (80) relied on the fiber network, 8.4%

(32) relied on internet cafes and 2.9% (11) did not use any of

access methods mentioned.

Figure 1: Internet Access Modes

Internet accessibility by modems was found to be the main

mode for OSNs access, followed by cell phones and fiber

networks were ranked third. Very few did access internet via

internet cafes. The reason given is that, UDOM internet access

point is still very poor so students have to rely on modems and

their cell phones as there is a wider option of mobile internet

with flexible bundles to suit for each one‘s choice.

3.2. The Extent of Use of Internet for Academic Purposes

vs. Other Uses

As depicted in Figure 2, the purpose of internet use by most

of the respondents is mainly for academic purpose which is

almost 99%. The results for academic purposes were

encouraging in a way that discussions pertaining to courses

can take place. That shows to be a widely used platform for

education.

Figure 2: The Extent of Use of Internet for Academic

Purposes vs. Other Uses

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This confirms assertion by [11] who contend that many

students have moved on, with their social lives and using of

online tools that are much more flexible and user centered,

whilst the academic staff are still struggling to work out how

best to make use of older technologies.

3.3. E-learning and Social Networking Ideas

According to the results in Figure 3, 84% of respondents are

e-learning and OSNs. These results imply that most of the

respondents may have enough knowledge about e-learning and

OSNs. They are adherents of OSNs as e-learning platforms.

Figure 3: E-learning and Social Networking Ideas

The results of survey in terms of colleges showed that students

from CIVE (pursues of ICT based courses) are the primary

group in exploiting OSNs for learning, followed by students

from CoED. It is inferred here that the more an individual is

exposed to informatics subjects the most likely he/she is to use

ICTs as a means of education. It is further inferred that

students tend to get interested in the use of a technology when

they see their peers are using it.

3.4. Academic Groups in OSNs Used by UDOM Students

In order to emphasize the use of OSNs in the personal

learning experience, it became necessary to examine if

students are part of any of the academic groups in OSNs. The

results show that OSNs academics groups are not used by the

majority students (57%), but the social collaborative

networking learning through those groups was almost 43% as

Figure 4 depicts.

Figure 4: Subscription in Academic Groups

Meanwhile, 43% of the students responded that they are part

of academic discussion groups (such Computer Science & IT

Students in Facebook) based on their courses and interests. It

seems that almost 57% of the total respondents are not part of

any social discussion groups. This might have been caused by

existence of portals and LMSs and thus no enough

sensitization has been made to use OSNs for educational

issues. The portals that are current in use at the University are

UDOM-SR (for record keeping and retrieval) and Moodle.

Collegewise, it was found that, over 32% of respondents at

CHSS, over 61% of respondents at CIVE, over 47% of

respondents at CoED, over 37% of respondents at CoHS, over

63% of respondents at CNMS and over 47% of respondents at

CoES had joined academic groups in OSNs as depicted in

Figure 5.

Figure 5: Distribution of Academic Group Subscription by

Colleges at UDOM

To sum up, the preceeding discussion regarding OSNs and e-

learning issues indicate that, although interactive educational

options such as Blackboard exist, OSNs were rarely used for

academic purpose [12]. Therefore the researcher believes the

respondents‘ feelings are quite positive, and they were happy

to use OSNs for learning. As they rarely used OSNs for

learning at present, they were willing to use OSNs as e-

learning platforms currently or in the future.

4. CONCLUSION

While students accept OSNs to be the main platform for e-

learning in the institutions and they are ready to use it, there is

a noticeable divergence between the formal technological

direction for e-learning and the informal (reality) use of e-

learning platforms. The formal posture is to use traditional

Local Area Network (LAN) combined with Wide Area

Networks (WAN). This posture is more expensive to

implement and maintain. However use of OSNs is cheaper and

flexible, and that is why this approach is widely used

informally.

Generally, it is important for institutions to be aware of

students‘ current needs and interest related to their learning

environment for better knowledge acquisition and academic

achievement. As they need more interactive learning

environment that allows them to have greater chances to

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manage and control their own learning environment. Students‘

tendencies, inclinations and ability can be used to design and

devise OSNs which may be more preferable to students and

other users. Since data was collected from only one institution,

the future sample for similar study will be drawn from other

institutions to allow for generalization.

REFERENCES

[1] A.Y. Yu, and R. C. Kwok. Can learning be virtually

boosted: An investigation of online social networking

impacts. The Journal of Computers and Education,

55(4), 2010, pp. 1494-1503,.

[2] C. Greenhow and B. Robelia. Old communication, new

literacies: Social network sites as social learning

resources. Journal of Computer-Mediated

Communication, 14, 1130–1161. doi: 10.1111/j.1083-

6101.2009.01484.x, 2009.

[3] C. Schmugar. The future of social networking sites.

McAfee Security Journal, 2008, pp.28-30.

[4] E. Yang Su, Social networking helps students perform

better, professor says, K-12 Daily Report.

California,2011.http://californiawatch.org/dailyreport/soci

al-networking-helps-students-perform-better-professor-

says-12292#.ToASq-_flvc.email.

[5] F. Hassan. Viability Of Implementing Mobile Learning In

Tanzania. Unpublished Report, University of Dodoma,

2013.

[6] G. Cormode and B. Krishnamurthy ―Key differences

between Web1.0 and Web2.0‖,2008.

http://www2.research.att.com/~bala/papers/web1v2.pdf.

[7] H. Seif. Investigation of the challenges facing the

implementation of on-line learning program for higher

learning institutions: Case of Udom, Unpublished Report,

UDOM. , 2012.

[8] J. DiMicco, D.R. Geyer, C. Dugan, B. Brownholtz, and

M. Muller, Motivations for Social Networking at Work.

Computer supported cooperative work, 2008.

[9] L. Liu and C. Wang. E-learning Tools to Improve

Students Learning Experience: a case study, International

Journal of Modern Education and Computer Science

(IJMECS), Vol. 1, No. 1, 2009, pp. 1-9.

[10] N. Dabbagh and A. Kitsantas, Personal learning

environments, social media, and self-regulated learning:

A natural formula for connecting formal and informal

learning. Internet and Higher Education, in press. 2011.

[11] R. B. Johnson and A. J. Onwuegbuzie. Mixed methods

research: A research paradigm whose time has come.

Educational Researcher, Vol. 33, No.7. , 2004.

[12] T. A. Pempek, Y. A. Yermolayeva, and S. L. Calvert.

College students' social networking experiences on

Facebook. Journal of Applied Developmental Psychology,

30(3), 2009,pp. 227-238.

[13] T. Yamane Statistics: An Introductory Analysis, 2nd

Ed.

Newyork: Harper and Row. 1967.

[14] W. Clark., L. Logan, F. Luckin, A. Mee, and M. Oliver.

Beyond Web 2.0: mapping the technology landscapes of

young learners. Journal of Computer Assisted Learning,

vol.25, 2009, pp. 56-69.

Carina Titus is with School of Informatics, College of

Informatics and Virtual Education at the University of

Dodoma.

Prof. Leonard J. Mselle is with School of Virtual

Education, College of Informatics and Virtual Education at the

University of Dodoma.