3G Mobile Map Service Publishing and Data Flow Testing

Zhirong Chen, Tianhe Yin College of Science, Ningbo University of Technology

Ningbo, China chenzr29@gmail.com

Ting Liu Institute of Remote Sensing and Earth Science, Hangzhou

Normal University Hangzhou, China

tingliu21@gmail.com

Abstract—The 3G technology brings a great increase of wireless data transmission capacity, makes it possible for mobile map services. The map service publishing method and data transmission rate directly affect the user experience effect. This paper has released different types and sizes of maps based on three environments, the simulator, TD-SCDMA and Wi-Fi, tested the time required for mobile map transmission, and used the Grubbs method for denoising. By the further analysis of map data transmission under different circumstances, it provides a reliable theoretical reference for efficiency improvement and further development of mobile map services.

Keywords-3G; Mobile map service; Data flow testing; Grubbs

I. INTRODUCTION With the development of geographic information services,

there was an acceleration trend of the integration of IT technology, mobile communication technology and geographic information service technology. The 3G technology, which means a generation of standards for mobile phones and mobile telecommunication services fulfilling the International Mobile Telecommunications-2000 specifications by the International Telecommunication Union [1], brings a great increase of wireless data transmission capacity, makes it possible for mobile map services. The rise of mobile geographic information service technology, mark the geographic information service technology orient from the department application, enterprise application, turn to the large-scale social service. Mobile map service, as a basis for the mobile geographic information service, is not only the indispensable part, but also the key factor for a more efficient service. Therefore publish an efficient and high quality mobile map service plays a very important role for a better experience of mobile geographic information service.

Wireless data communications are an essential component of mobile computing. The various available technologies differ in local availability, coverage range and performance [2], and in some circumstances, users must be able to employ multiple connection types and switch between them. In order to test and evaluate the ability of the 3g network to run a mobile map service, TD-SCDMA, Wi-Fi and simulator environments are used for test. TD-SCDMA, with China's own intellectual property rights, is the representative of the 3G mobile communication network technology [3]. Wi-Fi is a popular

technology that allows an electronic device to exchange data wirelessly (using radio waves) over a computer network, including high-speed Internet connections [4]. For a better contrast, simulator is also used as one of the test environment.

II. TEST METHOD Esri's ArcGIS is a geographic information system (GIS) for

working with maps and geographic information. It is used for: creating and using maps; compiling geographic data; analyzing mapped information; sharing and discovering geographic information; using maps and geographic information in a range of applications; and managing geographic information in a database. ArcGIS Server is the core server geographic information system (GIS) software made by Esri. ArcGIS Server is used for creating and managing GIS Web services, applications, and data. ArcGIS Server is typically deployed on-premises within the organization’s service-oriented architecture (SOA) or off-premises in a cloud computing environment [5].

This paper uses ArcGIS Server 9.3 to release two mobile map services. The map data source configuration information is shown in Tab.1. Meanwhile, in order to meet the test requirements, a simple mobile phone application has been developed. Four time points in the service call have been recorded by the program, the time of request send, server respond, connection established and transmission over. Test results stored as text in the log.txt, for subsequent analysis and statistics. As Fig.1, three time periods during the service call can be calculated from four time points, the response time (t1), the connect time (t2), and the loading time (t3). Through a detailed comparative analysis of the three time periods, the quality and capacity of current mobile map service can be objectively evaluated, which provides data support and theoretical basis for the development of mobile map services. The test times of different map service in three kinds of environments, including simulator, TD-SCDMA network and Wi-Fi network are shown in Tab. 1.

Figure 1. Three time periods during service call

This effort is sponsored by the National Natural Science Foundation of China (NSFC) No. 40901241 and Natural Science Foundation of Zhejiang province No. Y5090377. The author gratefully acknowledges the support of K.C.Wong Education, Hong Kong.

TABLE I. MAP SERVICE TEST TIMES

Name Map cash size Simulator TD-SCDMA Wi-Fi

Map_1 0.61M 700 100 400

Map_2 15.57M 100 50 100

III. TEST RESULTS PROCESSING

A. Grubbs method The following three kinds of statistical methods are often

used to remove outliers: the early Williams chauven method, the modern Dixon and Grubbs [6]. Among them, the best adaptability of Grubbs' criterion in the treatment of abnormal data, namely the target data sorting, has been proved through a lot of experimental data. So we use this method to remove the noise in the test data. Usually in the Grubbs' rule instances, for system measurement data sequence, we can assume that the approximation by the Gauss distribution, which is practical in engineering. The specific process and the derivation formula are as follows [7]:

1) Calculate the sample mean:

1

1( ) ( )N

ii

Y t Y tN =

= ∑ (1)

2) Calculate the sample standard deviation, when measure in a limited number of times, the formula is:

∑=

−−

=N

ii tYtY

NNS

1

2)()(

)1(1

1

1 ( ) ( ) ( ) ( )( 1)

N T

i ii

Y t Y t Y t Y tN N =

⎡ ⎤ ⎡ ⎤= − −⎣ ⎦ ⎣ ⎦− ∑ (2)

3) Using the Grubbs' rule to eliminate outliers: First calculate the Grubbs criterion T ：

SYY

SV

T i −== (3)

Then according to the measured value number N and level of significanceα , compare T with the look-up table (Tab. 2) result ( , )T N α . α means the system error judged probabilities. It is usually taken as 0.05, 0.025, or 0.01 to avoid the system undetected probability increases. If the comparison result satisfied (4), it is considered that the observed value contains errors, should be defined as the outliers to discard.

( , )T T N α≥ (4)

4) Repeat steps 1), 2), 3), until all of the measurements are completed the judgment of the Grubbs' rule.

5) Finally, the filtered data will be used as measurement sequence for further processing.

TABLE II. THE GRUBBS CRITERION ( , )T N α

α N

3 5 6 8 10 13 20 25 30 35 40 50

0.05 1.15 1.67 1.82 2.03 2.18 2.33 2.56 2.66 2.75 2.82 2.87 2.96

0.025 1.15 1.71 1.89 2.13 2.29 2.46 2.71 2.82 2.91 2.98 3.09 3.13

0.01 1.15 1.75 1.94 2.22 2.41 2.61 2.88 3.01 3.10 3.18 3.29 3.24

B. Sample denoising Before data denoising, the distribution map of sample data

are put forward for an overall understanding. Through the R software drawing function, the Q-Q charts and histograms of t1, t2 and t3 are plotted. Fig. 2 shows the test results of Map_2 under the Wi-Fi environment.

As Fig. 2, the overall value of t3 is large, scattered distribution and no noise. The value of t1 and t2 is significantly smaller than t3, and centralized distribution with a few obvious noises. According to above, use the Grubbs method to denoise the t1 sample. The sample mean of t1 is 507.13, the standard deviation is 41.73. The experimental sample number is 100, take as 0.05, then the Grubbs number

can be found as 3.207 in the look-up table, for comparing with the calculated Grubbs. The value of the twelfth data was 8.072, greater than the value in the look-up table, so rejected. Remain 99 data were calculated and distinguished in the same way, and no abnormal value found.

Use the same method to t2 and t3 for noise inspection and elimination. Finally, the Q-Q charts and histograms of t1, t2 and t3 are plotted again (Fig. 3). As shown in the Fig. 3, the whole process has obvious effect on t1 and t2, which tend to be smooth and concentrated, in addition to t3, without any change.

Repeat the above steps, using Grubbs for all test data denoising, obtain the final sample data.

Figure 2. The Q-Q chart and histogram of t1, t2, t3 before denoising

Figure 3. The Q-Q chart and histogram of t1, t2, t3 after denoising

IV. TRANSMISSION RATE AND STABILITY ANALYSIS

A. Analysis of response time (t1) The denoised t1 data will be calculated to obtain the mean

and standard deviation, and results are as follows:

TABLE III. THE STATISTICAL DATA OF T1(UNIT: MS)

Type Mean Standard deviation

Map_1 Map_2 Map_1 Map_2

Simulator 955.03 495.84 59.347 26.710

TD-SCDMA 245.52 147.87 5.069 65.195

Wi-Fi 987.16 504.03 44.574 24.016

According to the mean value in Tab. 3, the difference of response time (t1) in Map_1 and Map_2 is very apparent, and the efficiency of Map_2 is significantly better than Map_1 in all environments. In the three test environment horizontal comparison, TD-SCDMA is obviously superior to the simulator and Wi-Fi environment.

From the aspect of standard deviation, the request time of Map_2 in simulator and Wi-Fi environment are more stable than Map_1, but the result is just the opposite in TD-SCDMA environment, Map_1 is more stable. In transverse observation, Map_1 is known more stable in TD-SCDMA than simulator and Wi-Fi, but for Map_2, TD-SCDMA environment gradually becomes unstable.

B. Analysis of connect time (t2) The denoised t2 data will be calculated to obtain the mean

and standard deviation, and results are as follows:

TABLE IV. THE STATISTICAL DATA OF T2(UNIT: MS)

Type Mean Standard deviation

Map_1 Map_2 Map_1 Map_2

Simulator 1100.81 970.50 47.964 39.728

TD-SCDMA 193.90 234.00 26.588 46.968

Wi-Fi 1115.55 972.89 44.585 36.990

According to the mean value in Tab. 4, the response time (t2) of Map_2 is significantly more efficient than Map_1 in simulator and Wi-Fi environment, and gets the opposite result in TD-SCDMA environment. In the three test environment horizontal comparison, either for Map_1 or for Map_2, TD-SCDMA is obviously superior to the simulator and Wi-Fi environment.

From the aspect of standard deviation, the request time of Map_2 in simulator and Wi-Fi environment are slightly better than Map_1, but slightly worse in TD-SCDMA environment. In transverse observation, the situation is same to t1, Map_1 is known more stable in TD-SCDMA than simulator and Wi-Fi, but for Map_2, TD-SCDMA environment gradually becomes unstable.

Overall, the stability difference of connect time in the three environments is small.

C. Analysis of loading time (t3) The denoised t3 data will be calculated to obtain the mean

and standard deviation, and results are as follows:

TABLE V. THE STATISTICAL DATA OF T3(UNIT: MS)

Type Mean Standard deviation

Map_1 Map_2 Map_1 Map_2

Simulator 15501.20 84156.38 1440.847 43553.353

TD-SCDMA 55476.47 814756.00 19675.292 872671.677

Wi-Fi 14346.83 148090.93 3829.243 110499.610

According to the mean value in Tab. 5, the loading time (t3) in simulator and Wi-Fi is significantly shorter than TD-SCDMA, and the gap is large. From the aspect of standard deviation, the stability of Map_1 is much better than Map_2, and the loading time of Map_2 is considerable instability. In transverse observation, simulator is the best, followed by Wi-Fi, and TD-SCDMA is the worst.

As loading time is closely related to cache size, while the size of Map_2 is much bigger than Map_1, so the data need to further processing in order to conduct a longitudinal comparison. By units the mean part in Tab. 5, the time required to transmit 1M map cache in different environment is shown in Tab. 6:

TABLE VI. THE VALUE AFTER UNITISED OF T3(UNIT: MS)

Type Mean Value after Unitised

Map_1 Map_2 Map_1 Map_2

Simulator 15501.20 84156.38 25411.80 5405.03

TD-SCDMA 55476.47 814756.00 90945.03 52328.58

Wi-Fi 14346.83 148090.93 23519.39 9511.30

In the three test environment horizontal comparison, it will take a bit long time to load data for a smaller map, and the loading rate has improved greatly for a larger map, this phenomenon is especially reflected in the simulator and Wi-Fi environment.

The above analysis of three periods shows that, in TD-SCDMA environment, both response time and connect time are better and more stable than the other two environments. But overall it seems the main time cost in the third step. Loading time in TD-SCDMA environment is required significantly more than simulator and Wi-Fi, and its stability also has big difference. In contrast, the loading rate advantage is obvious in Wi-Fi environment, and also more stable.

V. CONCLUSION The huge increase of data transmission capacity from 2G to

3G enables geographic information services’ wireless network transmission to take place. However, the characteristics of mobile terminals, such as weak hardware resources, low capacity, and frequent disconnection, have reduced its overall efficiency. To overcome the mobile terminal’s resource constraints and optimize in light of 3G network advantages, the balance strategy between the validity of resource using and the complexity of implementation in 3G network should be considered carefully.

ACKNOWLEDGMENT We would like to thank the anonymous reviewers for their

constructive suggestions and advice on improving the quality and presentation of this paper. This effort is sponsored by the National Natural Science Foundation of China (NSFC) No. 40901241 and Natural Science Foundation of Zhejiang province No. Y5090377. The author gratefully acknowledges the support of K.C.Wong Education, Hong Kong.

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