Practical Positioning and Traffic Estimation

Practical Positioning and Traffic Estimation Techniques using Relative Received Signal Strength RRSS in GSM mode Case Study Using Android Smart Phone in Egypt Roads

*

Mohamed H. Abdel Meniem*, Ahmed M. Hamad**, Eman Shaaban* Computer Systems Department, Faculty of Computers & Information Sciences, Ain Shams University, Cairo, Egypt ** Professor of Computer Systems at British University in Cairo

[email protected], [email protected], [email protected] Abstract— Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. In this paper, we consider problem of positioning and traffic estimation of vehicles. A range of cellular positioning technologies have been researched all over the world, using different technologies like GPS, UMTS, WiFi and GSM. The Database Correlation Method (DCM) is a positioning technology which has shown superior in terms of accuracy. DCM is based on a premeasured database of a location dependent variable such as Received Signal Strength (RSS) or Link Quality Identifier (LQI). Although the technique has good potential, the difficulty appears in forming the database (fingerprints) using field measurements. Especially, when implementing this DB in large, dynamic networks. Traffic management systems also is hot topic now days, it relies of different types of concepts, software and hardware. Velocity estimation techniques and floating speed sensors are main components at these systems. We produce a robust technique for both positioning and ensign for traffic estimation. Its main idea is using Relative Received Signal Strength (RRSS) of GSM instead of using absolute value. This study has been tested and analyzed in Egypt 1 roads using realistic data and commercial android smart phone. In general, all performance evaluation results were good. Mean positioning accuracy was about 29 m in urban areas and velocity estimation was about 2.1 km/h in rural areas. Keywords— 3G, Cell-ID, COO, Fingerprint, GDOP, Geo, GIS, GPRS, GPS, GSM, ITS, MCC, MNC, NMEA, RSS, UMTS, WiFi

I. INTRODUCTION Obtaining the position of a mobile unit is called position estimation or simply positioning. Several location estimation systems are reviewed in [1][15]. A vast majority of applications of location estimation use the GPS satellite system [2] which provides location estimates with an accuracy of a few meters. Alternatives to satellite-based systems are developed to avoid problems, such as lack of coverage between high buildings and indoors. These techniques use signals between the mobile unit and terrestrial transmitters or receivers. The transmitters can be either dedicated for this purpose or be a 1

This work is part of EgTNS [19], Egypt Traffic and Navigation System.

part of a communication system, such as a cellular telephone network. Cellular positioning techniques can be categorized as terminal-based techniques and network-based techniques. In terminal-based techniques, user equipment (UE) receive beacons and do all necessary processing in to order to calculate its position. In contrast, network-based techniques receive measurements from UE, and calculate its position silently. One of the alternatives is Wireless-Fidelity or WiFi. WiFi has been used for localization in many researches [5][6][7] and also commercial applications [17]. Moreover, WiFi technology has been used as Floating Car Data of (FCD) for traffic prediction systems. VTrack [31] proved that it is feasible to accurately estimate road travel times using a sequence of inaccurate WiFi-based position samples. WiFi-based localization techniques might raise privacy concerns, especially when apply the scan process or “War-Driving” [16]. Therefore, GSM-based positioning techniques appeared again with different implementations, using different techniques like CellID [10][11], Time Advance (TA) [12]. Other techniques are used also like (Angle of Arrival) AOA, Observing Time Difference (OTD) and Time Difference of Arrival (TDoA) as in [1][12]. GSM-based phones are also used in traffic monitoring services as dynamic probes or Floating Car Data (FCD) as in [18]. In a default scenario when Mobile Entity (ME) moves around in the GSM network, it is unavoidable to change between different geographical areas. To know which cell the ME should communicate with, the ME constantly listens to the signals sent out from the different (Base Transceiver Station) BTSs. The signals are measured and at certain threshold values of the signal strengths the ME will decide it needs to change its serving cell, and initiate a so-called handover (HO) procedure. To measure the different signals, the ME stores information about the currently serving cell, as well as up to six other cells of which it has received the strongest signals, called neighbour cells. All this information is stored in Network Measurements Report (NMR) which sent to core network every 480 millisecond in active (in call or data plan) mode. However, core network can receive same NMR in idle mode by sending paging message to UE, as in [1]. In this

work, we propose featured positioning technique based on Relative Received Signal Strength or simply RRSS. The rest of paper is organized as follows. Section II gives an insight to other positioning methods. Section III presents detailed methodologies proposed evaluated by authors, followed by trial result and performance evaluation in Section IV. Section V provides a conclusion and suggests possible directions for future research. II. BACKGROUND A. Positioning Methods According to [1], positioning techniques can be classified into fingerprinting techniques and non-fingerprinting techniques or simply ranging techniques. Ranging techniques involve lateration, angulation and dead-reckoning methods [1]. While fingerprinting-based techniques include any technique based on prior training, or require an observation phase like [5][6][20]. Also, it includes database correlation methods (DCM) like [12]. Among the existing techniques of positioning we are interested in fingerprinting. The fingerprinting algorithm does not create a map of estimated tower positions nor does it model radio propagation. Instead, it creates a search index of radio fingerprints to geodetic coordinates. To position a device, the algorithm uses the constructed index and calculates the Euclidean distance in signal strength space between the current fingerprint and all available fingerprints in the index. It then selects k fingerprints with the smallest euclidean distance as potential indicators of the current location. The location of the device is estimated as an average of the latitude and longitude coordinates of the best k matches. The accuracy of the location estimate is highly dependent on the density of the set of collected fingerprints. Fingerprinting algorithms try to increase the accuracy, availability and interoperability. At the same time, they try to decrease the size of DB and reduce the complexity and the running time of matching algorithms. B. Traffic Estimation Techniques Providing real-time road traffic information to road users has become the natural next step in traffic telematic after the proliferation of navigation system equipments. The knowledge of the user location only does not allow navigation equipments to estimate journey durations and calculate the best routes by considering the current road traffic conditions. The concept that a flow of mobile phones can be mapped to a stream of road users led in the last few years to relevant industrial activity. Several methods can be used for collecting mobility/location data from the cellular network. A first high level differentiation can be done between active and passive monitoring systems. 1) Active Monitoring: In active monitoring, the procedure used by the network to gather information about users’ location and/or position1generates additional signalling traffic. A typical procedure used to actively refine user location information is the paging procedure, which is normally used by the network for localizing UE in case of incoming calls/connections. Paging might be used by the network to

localize generic UE, even when no incoming calls are present. On the other hand, the positioning mechanisms defined in the context of location services (LCS) are more complex procedures used for retrieving information from a UE and calculating its geographical position, e.g. Cell-ID-based positioning and OTDOA. A typical example for active monitoring system is NHCRP project [23]. 2) Passive Monitoring: In contrast to active monitoring, with passive techniques signalling is silently collected from one or more points in the network with no impact in the offered network load. Realtime information, such as users’ behaviour, terminal movement history, and terminal positions are then retrieved by processing the collected signalling. The amount/type of the retrieved information depends on the placement of the monitoring points and on the state of the user terminal. CAPITAL [21] and STRIP [22] are good examples for exploiting traffic information from cellular network in passive mode. 3) Hybrid Techniques: Some projects like [18] proposed featured system which leverage advantages of both active and passive monitoring system. Several limitations and concerns appear when applying active and passive monitoring, especially when they are applied using terminal-based positioning techniques. User reveals his position and sends it periodically, one of the most features project proposed solution to privacy concerns and provide evaluation metrics for accuracy and privacy is Virtual Trip Lines or simply VTL [29]. VTL addresses two main challenges: privacy and integrity (The system should not allow adversaries to insert spoofed data, which would reduce the data quality of traffic information. This is especially challenging because it conflicts with the desire for anonymity). III. RELATIVE RECEIVED SIGNAL STRENGTH (RRSS) APPROACH In this section, we describe our RRSS system for GSM phone localization. We start by an overview of the system followed by some of Haversine functions used. Then, details of offline and online phases, and finally envisage of the traffic model. A. Overview RRSS system works in two phases: an offline phase and online phase. During the offline phase the data base of fingerprint is built where RRSS for each antenna at a particular position/sample. During the online or matching phase the fingerprint is used to calculate the most probable sample which has maximum correlation between rules. B. Haversine Formula It is important to distinguish between euclidean distance and Haversine distance. The first is well known and applied for planar systems, however almost all of positioning systems apply it to evaluate their results. Ecludian distance cannot be used due to the curvature of the Earth’s surface. Haversine distance gives the great-circle distance between two points on a sphere from their longitudes and latitudes. We have found

that the best implementation of other great-circle functions by Ed Williams in [24]. For example to calculate bearing between two points lat1, lon1 and lat2, lon2 we used formula in Equation 1. 𝜃𝜃 = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 2[𝑠𝑠𝑠𝑠𝑠𝑠(∆𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙). 𝑐𝑐𝑐𝑐𝑐𝑐(𝑙𝑙𝑙𝑙𝑙𝑙2 ), 𝑐𝑐𝑐𝑐𝑐𝑐(𝑙𝑙𝑙𝑙𝑙𝑙1 ). 𝑠𝑠𝑠𝑠𝑠𝑠(𝑙𝑙𝑙𝑙𝑙𝑙2 ) − 𝑠𝑠𝑠𝑠𝑠𝑠(𝑙𝑙𝑙𝑙𝑡𝑡1 ). 𝑐𝑐𝑐𝑐𝑐𝑐(𝑙𝑙𝑙𝑙𝑙𝑙2 ) . 𝑐𝑐𝑐𝑐𝑐𝑐(∆𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙)] (1)

Where ∆𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 = 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙2 − 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙1

To calculate great-circle distance between two points lat1, lon1 and lat2, lon2, we used formulas in Equation 2, 𝑎𝑎 = 𝑠𝑠𝑠𝑠𝑠𝑠2 �

∆𝑙𝑙𝑙𝑙𝑙𝑙 2

� + 𝑐𝑐𝑐𝑐𝑐𝑐(𝑙𝑙𝑙𝑙𝑙𝑙1 ). 𝑐𝑐𝑐𝑐𝑐𝑐(𝑙𝑙𝑙𝑙𝑙𝑙2 ). 𝑠𝑠𝑠𝑠𝑠𝑠2 �

𝑐𝑐 = 2. 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎2 �√𝑎𝑎, �(1 − 𝑎𝑎)� 𝑑𝑑 = 𝑅𝑅. 𝑐𝑐

∆𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 2

�

(2)

Where R is Earth radius, ∆𝑙𝑙𝑙𝑙𝑙𝑙 = 𝑙𝑙𝑙𝑙𝑙𝑙2 − 𝑙𝑙𝑙𝑙𝑙𝑙1 , ∆𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 = 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙2 − 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙1

C. Offline Phase This phase aims to construct the relative signal strength histogram for the RSS received from each cell tower at each location in the fingerprint. Since we target a practical technique, we collected all fingerprint on-the-go. Some other systems require stopping at certain points to collect signal strength. We believe that collecting fingerprint at normal speed of roads is more practical than stopping at each training point. Each training point has n(n-1)/2 rules where n is number of valid detected cells, that means maximum number of rules is 21 when six neighbours (plus serving cell) detected successfully. Rule pattern for GSM is shown below. RSS1 (LAC1, CID1) {>, = 3). DS value acts as balancing factor between size of fingerprint DB and accuracy. Figure 2 (a) illustrates relation between DS and average of localization error for two testbeds. We chose 160 m as optimized DS value in order to reduce size of fingerprint DB. In order to improve accuracy, we tried to minimize DS as possible, we chose 15 m for urban and 35 m for rural as illustrated in Figure 2(b).

Mean Positioning Error [m]

32 Km

Rural

Testbed

600

Rural Urban

500 400 300 200 100 0

20 40 60 80 100 120 140 160 180 200 250 300 400 Distance between Samples (DS) [m] a. Long Distances

Mean Positioning Error [m]

Urban

No. of Testing Samples 1203

250

Rural Urban

200 150 100 50 0

15, 29

35, 55

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 Distance between Samples (DS) [m] b. Short Distances

Figure 2. Effect of Changing Distance between Samples (DS) on Positioning Error.

It is important to mention that we could not reach smaller DS value, due to limitation of hardware (Smartphone). It can save only 1 sample per second. When vehicle speed is 60 km/h (at urban) it passes about 15 m each second and about 35 m when speed is 120 km/h (at rural). Mean accuracy was about 29 m in urban when DS = 15 m, and 55 m in rural when DS = 35 m. Figure 3 shows Cumulative Distribution Function (CDF) for two DS values at two testbeds. CDF of Localization Error

TABLE I SUMMARY OF TWO TESTBEDS

1 0.8 0.6

Urban, DS = 15 m Rural, DS = 35 m Urban, DS = 160 m Rural, DS = 160 m

0.4 0.2 0

0

50 100 150 200 250 300 350 400 450 500 550 600 Localization Error [m]

Figure 3. CDF of Mean Localization Error using different DS values.

C. Compare with other Positioning Techniques In this section, we compare RRSS GSM localization system (using small DS values) with similar systems derived from CellSense [25] experiments, since they have been tested in very similar environments in Egypt using Android smart phone too. Also we compared RRSS accuracy with DCM system likes [12][27].Table III summarizes comparisons.

TABLE III COMPARISON BETWEEN DIFFERENT TECHNIQUES AND RRSS ALGORITHM

Algorithm / Testbed

Urban

Rural

Error [m]

Mean

Median

RRSS Database Correlation Method (DCM) [12] CellSense [25]

29

12

67th Percentile 20

-

-

44

-

30

Database Correlation Method Nearest Neighbours (DCM-NN [27]

101

Gaussian Process [25] Deterministic [25]

-

31

67th Percentile 40

95th Percentile 143

-

-

-

-

-

-

105

165

-

112

276

243

-

221

700

-

-

-

270 131

-

-

Mean

Median

55

90

65

56 89

Average Length of Sample [m]

D. Evaluation of Traffic Estimation Model Evaluating traffic estimation models have several parameters and concerns like positioning and velocity accuracy, availability, reliability, interoperability, privacy, cost. At this research we focused on velocity accuracy, bearing detection, sampling technique and some of privacy concerns. 1) Velocity Accuracy: velocity accuracy is always better than positioning accuracy, taking into consideration map matching concerns. Since our approach is on-road positioning, then no need for map matching algorithm and velocity mean error will be less than 2.1 km/h for both urban and rural. 2) Bearing Accuracy: We define bearing accuracy is detecting vehicle position at right direction, i.e. avoid shifting vehicles to the opposite road-direction. Detecting right direction in rural is very simple and accurate. In urban, as shown in Figure 5, it is quite difficult. Based on visual observations, we assumed best difference between origin sample direction and matched sample direction is 90°. Our RRSS positioning technique could successfully detect 77% for urban and 83% for rural and mean bearing error was < 90°. 3) Sampling Technique: Sampling means when and where each vehicle should update its position. It is a trade-off between accuracy and privacy [29]. We provide sampling technique based on rules. Initially each vehicle will update all detected rules. Then, only modified rules will be updates. To test this sampling technique we measured actual average length of our proposed Rule-based sampling technique when UE have initially 1, 3, 6 or 10 rules. Figure 4 illustrates these results. 300.00 250.00

Rural Urban

278

200.00 150.00 100.00 50.00 0.00

1

3 6 Initial No. of Rules

95th Percentile 108

10

Figure 4. Actual length of Rule-Based Sampling Technique.

Average of maximum sample length was 278 m as illustrated in Figure 4. According to [32], this sample length able to provide speed accuracy less than 2.1 km/h. 4) Privacy Concerns: In general, privacy concerns appear in terminal-based mode only. In case of network-based mode data will be collected silently. We assume that updating only one rule is not sufficient for tracking vehicles. E. Error Sources To understand positioning accuracy, it is important to figure out error sources. First of these errors is GPS (Ground Truth), GPS accuracy should be evaluated using Geometric Dilution of Precision (GDOP) [2] value. We used free livecrowdsourced website Cellumap [28] to illustrate GPS accuracy of our smart phone. Although GPS accuracy should be within 5 ~ 20 m, it reached 50 m at dense urban areas. Also, limited number of valid detected cells which leads to limited number of rules. Also, fingerprints might be useless if configuration of the sense has been changed. Finally, rounding errors and calculation might increase error. V. CONCLUSIONS We proposed a positioning technique and traffic estimation model based on relative received signal strength RRSS. Overall performance metrics was good. Results were compared with other fingerprint positioning techniques, especially those done in similar environments. Currently, we are testing the effect of changing devices and testing stability of rules over time. Also, studying the ability to use RRSS indoors as a standalone technique or integrate it with other technologies like UMTS and WiFi. In order to increase number of valid detected cells we are working on combining multiple NMR in spatiotemporal manner. As part of EgTNS, this technique will be combined with other passive positioning technique and HO-based velocity estimation approaches. ACKNOWLEDGMENT We would like to thank Prof. Mustafa Yousef, Dr. Tarek Attia, Abeer A. Ghandar, Haytham A. AbuelFutuh, and R&D department in Vodafone Egypt.

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Figure 5. Bearing of Different Samples (Ground Truth), Urban on Right, Rural on Left.