A Novel Hybrid Approach to Enhance the Localization Accuracy of Vehicular Adhoc Network (Vanet) Using RFID and GPS

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1 Volume 2, Issue 6, November December 13 ISSN A Novel Hybrid Approach to Enhance the Localization Accuracy of Vehicular Adhoc Network (Vanet) Using RFID and GPS Sunita S.Shinde 1, Ravi M. Yadahalli 2 1 Department of Electronics & Telecommunication Engineering, Annasaheb Dange College of Engineering & Technology, Ashta (India) 2 Department of Electronics and Communication Engineering, PES Institute of Technology & Management, Shivamogga (India), Abstract- The demand for seamless positioning has been significantly high in vehicular applications, such as navigation, intelligent transportation systems, collision avoidance, etc. Most seamless positioning techniques are based on integrated methods. In order to provide seamless positioning, this paper proposes GPS, RFID Hybrid approach in three different scenarios. Hybrid approach, also allows vehicles without GPS to compute their position by contacting GPS equipped neighbors. The performance of the proposed localization system is evaluated using MatLab. Index Terms- Vehicular, localization, DGPS, Mapmatching 1. INTRODUCTION The demand for seamless positioning has significantly increased since the introduction of ubiquitous computing in the late 198s. Positional information has become more and more important seeing as it is needed everywhere all the time. Consequently, the method of providing seamless positioning has attracted a great interest towards various integrated techniques such as GPS/INS, GPS/MEMS, GPS/DR etc. This paper introduces three different RFID-GPS scenarios for achieving localization accuracy as RFID tags at road side, RFID tags at center of lane & RFID tags at divider. Rest of the paper is organized as follows. Next section shows implementation of three different RFID-GPS Scenarios. Results are discussed in detail in section III. Section IV provides our conclusion. 2. DEFAULT SCENARIO FOR PROPOSED SYSTEM Table 1 shows default values of scenario parameters and simulation is carried out using MatLab. Table1. Scenario parameters with Default value Scenario Parameter Default Value No. of vehicles 1 % of Non-GPS vehicles 5% Speed range of vehicles -3m/s Interval of stationary RFID tag 5 km By using above mentioned default parameters, the implementation of proposed scenario is carried out and the effect of the scenario parameters on accuracy is observed. The default VANET scenario developed for simulation is as shown in Fig. 1. Road length of 5 Km is considered for simulation. Blue line indicates road out line. Width of the lane is in between m to 9m. Two green dotted lines indicate lane separation at 3m & 6m. GPS vehicles are represented by red dot & Non-GPS vehicles are represented by black dot and the vehicles get Pink colour circles have got its accurate position. RFID tags on the road side are shown by green square box with black outline at 5Km distance from each other. Green colour circle around each RFID tag indicate 4m range. Identification of vehicles in 4m RFID range is done in next step. After that, movement is provided to the vehicles. Then calculations for GPS & Non-GPS vehicles position are calculated. By varying scenario parameters, graphs are plotted. Default VANET Scenario : Fig.1: Default VANET Scenario Three Different Cases of Scenario Following are the three different cases of RF-GPS scenario. RFID at road side Here, RFID tags are installed on road side at 5Km distance from each other. Hence, RFID Y-coordinates will be m, which is shown in Fig.2. Volume 2, Issue 6 November December 13 Page 83

2 Volume 2, Issue 6, November December 13 ISSN Fig.2: VANET Scenario for Case I RFID tags at center of lane Here, RFID tags are installed in the center of first lane at 5Km distance from each other. Hence RFID Y-coordinates will be 1.5m, which is shown in Fig.3. Fig.3: VANET Scenario for Case II RFID tags between 1st and 2nd lane (at divider) Here, RFID tags are installed in between first lane and second lane (at divider) at 5Km distance from each other. Hence RFID Y-coordinates will be 3m, which is represented in Fig.4. Fig.4: VANET Scenario for Case III 3. RESULTS & DISCUSSION The RF-GPS system designed & implemented using a 3- lane freeway scenario in which all vehicles move in one direction with various speeds. Vehicles have 4m range RFID system and 25m range IEEE radio. Table 1 describes default values of scenario parameters, which uses default values unless explicitly stated. The latency is measured until a vehicle acquires the first accurate position data, which tells us the ability of RFID-GPS integrated system to guarantee accurate positioning in representative traffic conditions. The interval between position acquisitions by Non- GPS vehicles is also measured to assess the ability to support such users. The proposed localization system is evaluated for four different scenarios parameters. Initially, for different traffic volumes on the road. The total number of vehicles on the road is an important factor since the cars interact with each other to find accurate position. Fewer the vehicles, the fewer are the reference points. Thus, if there are not enough vehicles driving on the road, the probability of receiving the differential co-ordinates decreases. This also degrades the possibility that Non-GPS vehicles encounter GPS vehicles having accurate position. Second, the percentage of GPS vehicles on the road is changed. This value is especially critical to Non-GPS vehicles. If the numbers of GPS vehicles decreases, Non- GPS vehicles have less chance to obtain travel data via the single peer localization scheme. Third, the simulation is carried out with different speed ranges. Speed difference between vehicles increases the chance of encountering other vehicles, and thus increases RFID contact rate. Finally, fraction of RFID enabled vehicles on the road is varied. More RFID capability on the road implies more reference vehicles which improve the position accuracy of nearby GPS vehicles. The impact of RFID penetration on localization performance will be evaluated. Effect of RFID Tags Position: Comparison of parameters listed in Table 1 carried out by considering three different cases as given below. 3.1 Traffic Volume 1 C D F g e t t i n g a c c u r a t e p o s i t i o n f o r a l l v e h i c l e vs. CDF of accurate position for all 1 cars cars 3 cars 4 cars Volume 2, Issue 6 November December 13 Page 84

3 Volume 2, Issue 6, November December 13 ISSN C D F g e t t i n g a c c u r a t e p o s i t i o n f o r a l l v e h i c l e vs. CDF of accurate position for all 1 cars cars 3 cars 4 cars C D F g e t t i n g a c c u r a t e p o s i t i o n f o r n o n G P S v e h i c l e vs. CDF of accurate position for Non GPS 1 cars cars 3 cars 4 cars Case-3) RFID tags between 1 st and 2 nd lane (at divider) 1 9 C D F g e t t i n g a c c u r a t e p o s i t i o n f o r a l l v e h i c l e vs. CDF of accurate position for all 1 cars cars 3 cars 4 cars C D F g e t t i n g a c c u r a t e p o s i t i o n f o r n o n G P S v e h i c l e vs. CDF of accurate position for Non GPS 1 cars cars 3 cars 4 cars Fig.5: Comparison of Three Cases for Traffic Volume Parameter The comparison of results shows that the three different cases for traffic volume parameter, for all vehicle (in Fig.5). Time required to get accurate position for all vehicles is 39 sec, 3 sec & 27 sec for case I, II, & III respectively, for vehicles. Hence it is evident that less time is needed for case III. For Non-GPS vehicles, time required to get accurate position is 3 sec, 278 sec & 27 sec for case I, II, & III respectively, for vehicles. Hence best is the case III. 3.2 Fraction of Non-GPS vehicle Mean time to get 1st accurate position Non GPS vehicle vs. Mean time to get 1st accurate position ALL GPS nongps Population of Non GPS vehicle on road[%] Mean time to get 1st accurate position Non GPS vehicle vs. Mean time to get 1st accurate position ALL GPS nongps Population of Non GPS vehicle on road[%] Case-3) RFID tags in between 1 st and 2 nd lane (at divider) Mean time to get 1st accurate position Non GPS vehicle vs. Mean time to get 1st accurate position Population of Non GPS vehicle on road[%] Fig.6: Comparison of Three Cases for Fraction of Non- GPS Vehicle Parameter ALL GPS nongps The comparison of results shows that the fraction of Non- GPS vehicles varied for the three cases (in Fig.6). Mean time to get accurate position for % population of Non- GPS vehicle on the road, for case I, it is around 58 sec, for case II it is around 38 sec & for case III it is around 33 sec. Hence, mean time to get accurate position is less in case III. Volume 2, Issue 6 November December 13 Page 85

4 Volume 2, Issue 6, November December 13 ISSN Impact of speed variables CDF getting accurate position for all vehicle vs. CDF of accurate position for all Range:15-3 Range:-3 Range: Traffic Volume CDF of GPS Vehicles % 4% % 8% 1% CDF getting accurate position for all vehicle vs. CDF of accurate position for all Range:15-3 Range:-3 Range: Case-3) RFID tags between 1 st and 2 nd lane (at divider) CDF getting accurate position for all vehicle vs. CDF of accurate position for all Range:15-3 Range:-3 Range: Fig.7 Results Comparison of Three Cases for Speed Variable Parameter The comparison result shows impact of speed variables for three different cases (in Fig.7). Consider 15-3 m/s range in the three cases. for case I is sec, for case II is 18 sec, & for case III is 15 sec. So simulation time is less in case III. It means all vehicles get its position in less time for case III (sec) CDF of GPS Vehicles % 4% % 8% 1% (sec) Case-3) RFID tags at divider CDF of GPS Vehicles % 4% % 8% 1% (sec) Fig.8: Comparison of Three Cases for Fraction of RFID- Enable Vehicle Parameter The comparison of results shows that the three different cases for fraction of RFID enable vehicle parameter (in Fig.8). For % RFID enable vehicles require simulation time more than 8 sec (case I), 325 sec (case II), for 31 sec (case III). So, even if the less number of RFID enabled vehicles are present on road case II & III gives good result. But best is case III. Volume 2, Issue 6 November December 13 Page 86

5 Volume 2, Issue 6, November December 13 ISSN CONCLUSION A new localization system in VANETs i.e. Integrated System for Accurate Localization using GPS and RFID has presented use of a RFID system. It develops the mobile version of the DGPS system. RF-GPS calibrates GPS error and thus allows a vehicle to compute its accurate position. Moreover, a vehicle, which does not have a GPS receiver or cannot use the receiver temporarily, is also able to estimate its accurate position with the single peer localization scheme. This localization system has been evaluated extensively via simulations(matlab). The results shows that the impact of traffic volume, fraction of Non-GPS vehicles, speed variations & fraction of RFID enabled vehicles on the performance of the RF-GPS system. The simulations show feasibility and performance of the RF-GPS system with following three different cases, RFID at road side, RFID tags at centre of lane, and RFID tags at divider. Case III gives best and fast result as compared with case I & case II. This is due to RFID 4m entire range will be available along with its perimeter in the two lanes. Hence, maximum vehicles get its accurate position. REFERENCES [1] J. Hightower and G. Borriello, Location Systems for Ubiquitous Computing, Computer,pp 57-66,1 [2] B. Hofmann-Wellenho et al. Global Positioning System: Theory and Practice, 4th ed., Springer- Verlag, 597. [3] Azzedine Boukerche et al. Vehicular Ad Hoc Networks: A New Challenge for Localization- Based Systems [4] T. King et al. Dead-reckoning for position-based forwarding on highways, in: Proceedings of the 3 rd International Workshop on Intelligent Transportation (WIT 6), Hamburg, Germany, 6, pp [5] E. Krakiwsky, C. Harris, R. Wong, A kalman filter for integrating dead reckoning, map matching and gps positioning, in: Position Location and Navigation Symposium, 588. Record. Navigation into the 21st Century. IEEE PLANS 88. IEEE, 588, pp [6] M. Chen, D. 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6 Volume 2, Issue 6, November December 13 ISSN [] Rae, A. & Basir, O. (7). A Framework for Visual Position Estimation for Motor Vehicles, 4 th Workshop on Positioning, Navigation and Communication pp [21] Meguro, J.-i., Murata, T., Takiguchi, J.-i., Amano, Y. & Hashizume, T. (9). GPS Multipath Mitigation for Urban Area Using Omnidirectional Infrared Camera, IEEE Transactions on Intelligent Transportation Systems 1(1): [22] Eun-Kyu Lee et al. RFID Assisted Vehicle Positioning in VANETs [23] Eun- Kyu Lee et al. Department of Computer Science University of California, Los Angeles, RF- GPS: RFID Assisted Localization in VANETs [24] 1 federal radio navigation plan. Technical report, Department of Transportation and Department of Defense, March 2. [25] Q. Shengbo et al. An effective gps/dr device and algorithm used in vehicle positioning system. In IEEE ITS Conference, Oct. 3 [26] T. King, H. Fubler et al. Dead-reckoning for position-based forwarding on highways. In International Workshop on Intelligent Transportation (WIT), pages 199 4, 6 [27] E.D. Kaplan, Understanding GPS: Principles and Applications, Artech House, 596. [28] Sunita S. Shinde et al. Vehicular Ad-Hoc Network Localization Techniques: A Review [29] Wei Cheng, Member, IEEE et al. On the Design and Deployment of RFID Assisted Navigation Systems for VANETs [3] Mr. Vishal S. Hingmire et al. Vehicular Ad-Hoc Network (VANET) Integrated Localization Systems: A Survey [31] Utku Yıldırım & Erkan Kurt GPS Simulation Using MATLAB he has been with the Department of Electronics and Communication Engineering, PES Institute of Technology and Management, where he is currently Professor and Head of the Department. His research interests cover several aspects of microwave engineering including microwave antennas. He has published over 5 papers in technical journals and conferences. He is the reviewer of the Journal of the Electromagnetic Waves and Applications (JEMWA) Progress in Electromagnetic Research (PIER, PIER B, C, M, PIER Letters), USA. AUTHOR Mrs. Sunita S. Shinde received the Bachelor s degree, the Masters degree in Electronics Engineering from from Shivaji University, Kolhapur, Maharashtra She is having 14 years teaching experience. Her field of interest is Wireless communication, Adhoc Networks. She is a life member of ISTE. She wrote three books on Computer Networks. Ravi M. Yadahalli received the Bachelor s degree, the Masters degree, and the Ph.D. degree from the Gulbarga University, Gulbarga, India in 199, 1993, and 9, respectively. From 1993 to 1, he worked in various positions at the SDM College of Engineering and Technology, Dharwad, India. Since 1, Volume 2, Issue 6 November December 13 Page 88