jive volume 3
TRANSCRIPT
Journal ofInformaticsand VirtualEducation
ISSN 1821 - 7087
UDOMAcademicJournals Published by
THE UNIVERSITY OF DODOMA
Volume 3 Number 1December, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 i
The Journal of Informatics
and Virtual Education
Volume 3 Number 1 (2015)
The University of Dodoma
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
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
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
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
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
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)
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
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.
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 6
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 7
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.
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Calculations for GSM Communication Systems", Journal
of Engineering and Development, Vol.15, No.2, 2011,
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[2] A. Mousa, ―Electromagnetic Radiation Measurements and
Safety Issues of some Cellular Base Stations‖ Nablus
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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‖
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International Workshop
Crete, Greece, 2006, pp.758-765.
[5] Electromagnetic Communications Committee (ECC,
2003), Revised ECC Recommendation (02) 04.
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(9KHz-300GHz).
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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
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[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
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[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
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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.
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 9
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 10
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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Copyright ©2015 JIVE, ISSN 1821-7087 11
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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Copyright ©2015 JIVE, ISSN 1821-7087 12
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 13
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.
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[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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[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.
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 15
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 16
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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Copyright ©2015 JIVE, ISSN 1821-7087 17
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 18
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 19
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.
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER, 2015
Copyright ©2015 JIVE, ISSN 1821-7087 20
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.
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Dr. Majuto C. Manyilizu is a Lecturer at College of
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Tanzania. His email address is [email protected].
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 22
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 23
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 24
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 25
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, DECEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 26
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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Copyright ©2015 JIVE, ISSN 1821-7087 27
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]).
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 28
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 29
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 30
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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Copyright ©2015 JIVE, ISSN 1821-7087 31
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 32
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 33
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 34
of Dodoma. His research interests include mobile
communications, wireless sensor networks, mobile sensing
systems and Machine to Machine communications for Smart
grids.
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 35
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 36
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.
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 38
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 39
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
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.
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 40
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 41
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 42
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
JOURNAL OF INFORMATICS AND VIRTUAL EDUCATION, VOL. 3, No. 01, NOVEMBER 2015
Copyright ©2015 JIVE, ISSN 1821-7087 43
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.
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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.