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Wireless Networks
Erschienen in: Wireless Networks 7/2017

19.04.2016

Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter

verfasst von: Raj K. Jaiswal, C. D. Jaidhar

Erschienen in: Wireless Networks | Ausgabe 7/2017

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Abstract

Vehicular ad-hoc network (VANET) is an essential component of the intelligent transportation system, that facilitates the road transportation by giving a prior alert on traffic condition, collision detection warning, automatic parking and cruise control using vehicle to vehicle (V2V) and vehicle to roadside unit (V2R) communication. The accuracy of location prediction of the vehicle is a prime concern in VANET which enhances the application performance such as automatic parking, cooperative driving, routing etc. to give some examples. Generally, in a developed country, vehicle speed varies between 0 and 60 km/h in a city due to traffic rules, driving skills and traffic density. Likewise, the movement of the vehicle with steady speed is highly impractical. Subsequently, the relationship between time and speed to reach the destination is nonlinear. With reference to the previous work on location prediction in VANET, nonlinear movement of the vehicle was not considered. Thus, a location prediction algorithm should be designed by considering nonlinear movement. This paper proposes a location prediction algorithm for a nonlinear vehicular movement using extended Kalman filter (EKF). EKF is more appropriate contrasted with the Kalman filter (KF), as it is designed to work with the nonlinear system. The proposed prediction algorithm performance is measured with the real and model based mobility traces for the city and highway scenarios. Also, EKF based prediction performance is compared with KF based prediction on average Euclidean distance error (AEDE), distance error (DE), root mean square error (RMSE) and velocity error (VE).
Vorheriger Artikel A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks
Nächster Artikel CoSFR: coordinated soft frequency reuse for OFDMA-based multi-cell networks with non-uniform user distribution

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Literatur
1.
Al-Sultan, S., Al-Doori, M. M., Al-Bayatti, A. H., & Zedan, H. (2014). A comprehensive survey on vehicular ad hoc network. Journal of Network and Computer Applications, 37, 380–392.CrossRef
2.
Alam, N., Tabatabaei Balaei, A., & Dempster, A. G. (2013). Relative positioning enhancement in vanets: A tight integration approach. IEEE Transactions on Intelligent Transportation Systems, 14(1), 47–55.CrossRef
3.
Anagnostopoulos, T., Anagnostopoulos, C., & Hadjiefthymiades, S. (2011). An adaptive location prediction model based on fuzzy control. Computer Communications, 34(7), 816–834.CrossRefMATH
4.
Attar, A., Tang, H., Vasilakos, A. V., Yu, F. R., & Leung, V. C. M. (2012). A survey of security challenges in cognitive radio networks: Solutions and future research directions. Proceedings of the IEEE, 100(12), 3172–3186.CrossRef
5.
Bavdekar, V. A., Deshpande, A. P., & Patwardhan, S. C. (2011). Identification of process and measurement noise covariance for state and parameter estimation using extended kalman filter. Journal of Process Control, 21(4), 585–601.CrossRef
6.
Boukerche, A. (2008). Algorithms and protocols for wireless mobile ad-hoc networks. London: Wiley.CrossRefMATH
7.
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding rmse in the literature. Geoscientific Model Development, 7(3), 1247–1250.CrossRef
8.
Chen, P., Ma, H., Gao, S., & Huang, Y. (2015). Modified extended kalman filtering for tracking with insufficient and intermittent observations. Mathematical Problems in Engineering, 2015, Article ID 981727. doi:10.​1155/​2015/​981727.MathSciNetCrossRef
9.
Drawil, N., & Basir, O. (2008). Vehicular collaborative technique for location estimate correction. In: IEEE 68th Vehicular Technology Conference, 2008. VTC 2008-Fall. IEEE, pp. 1–5.
10.
Dvir, A., & Vasilakos, A. V. (2011). Backpressure-based routing protocol for dtns. ACM SIGCOMM Computer Communication Review, 41(4), 405–406.
11.
Feng, H., Liu, C., Shu, Y., & Yang, O. W. (2015). Location prediction of vehicles in vanets using a Kalman filter. Wireless Personal Communications, 80(2), 543–559.CrossRef
12.
Feng, Z., Zhu, Y., Zhang, Q., Ni, L. M., & Vasilakos, A. V. (2014). Trac: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing. In: INFOCOM, 2014 Proceedings IEEE, IEEE, pp. 1231–1239.
13.
Fülöp, P., Imre, S., Szabó, S., & Szálka, T. (2009). The accuracy of location prediction algorithms based on markovian mobility models. International Journal of Mobile Computing and Multimedia Communications, 1(2), 1–21.CrossRef
14.
Haklay, M. M., & Weber, P. (2008). Openstreetmap: User-generated street maps. IEEE Pervasive Computing, 7(4), 12–18.CrossRef
15.
Härri, J., Filali, F., Bonnet, C., & Fiore, M. (2006). Vanetmobisim: Generating realistic mobility patterns for vanets. In: Proceedings of the 3rd international workshop on vehicular ad hoc networks, ACM, New York, NY, USA, VANET ’06, pp. 96–97.
16.
Hu, C., Chen, W., Chen, Y., & Liu, D. (2003). Adaptive Kalman filtering for vehicle navigation. Positioning, 1(04), 0.
17.
Jaiswal, R., & Jaidhar, C. (2015). An applicability of aodv and olsr protocols on ieee 802.11p for city road in vanet. In: Internet of things, smart spaces, and next generation networks and systems, Lecture Notes in Computer Science, Vol. 9247, Springer International Publishing, pp. 286–298.
18.
Jiang, T., Wang, H., & Vasilakos, A. V. (2012). Qoe-driven channel allocation schemes for multimedia transmission of priority-based secondary users over cognitive radio networks. IEEE Journal on Selected Areas in Communications, 30(7), 1215–1224.CrossRef
19.
Khan, R., Khan, S. U., Khan, S., & Khan, M. U. A. (2014). Localization performance evaluation of extended Kalman filter in wireless sensors network. Procedia Computer Science, 32, 117–124.CrossRef
20.
Li, P., Guo, S., Yu, S., & Vasilakos, A. V. (2012a). Codepipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. In: INFOCOM, 2012 Proceedings IEEE, IEEE, pp. 100–108.
21.
Li, P., Guo, S., Yu, S., & Vasilakos, A. V. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3264–3273.CrossRef
22.
Li, X., Mitton, N., & Simplot-Ryl, D. (2011). Mobility prediction based neighborhood discovery in mobile ad hoc networks. In: NETWORKING 2011, Springer, pp. 241–253.
23.
Li, Z., Cai, Zx, Xp, Ren, Ab, Chen, & Zc, Xue. (2012b). Vehicle kinematics modeling and design of vehicle trajectory generator system. Journal of Central South University, 19, 2860–2865.CrossRef
24.
Liu, J., Li, Y., Wang, H., Jin, D., Su, L., Zeng, L., et al. (2016). Leveraging software-defined networking for security policy enforcement. Information Sciences, 327(C), 288–299.CrossRef
25.
Liu, K., & Lim, H. B. (2012). Positioning accuracy improvement via distributed location estimate in cooperative vehicular networks. In: 15th international IEEE conference on intelligent transportation systems (ITSC), 2012, IEEE, pp. 1549–1554.
26.
Meng, T., Wu, F., Yang, Z., Chen, G., & Vasilakos, A. V. (2016). Spatial reusability-aware routing in multi-hop wireless networks. IEEE Transactions on Computers, 65(1), 244–255.MathSciNetCrossRefMATH
27.
Mo, Z., Zhu, H., Makki, K., & Pissinou, N. (2008). Mobility-assisted location management for vehicular ad hoc networks. In: IEEE Wireless communications and networking conference, 2008. WCNC 2008. IEEE, pp. 2224–2228.
28.
Perkins, C., & Royer, E. (1999). Ad-hoc on-demand distance vector routing. In: Proceedings of second IEEE workshop on mobile computing systems and applications, 1999. WMCSA ’99. pp. 90–100.
29.
Qureshi, K. N., & Abdullah, A. H. (2014). Localization-based system challenges in vehicular ad hoc networks: Survey. SmartCR, 4(6), 515–528.
30.
Rad, H. J., Van Waterschoot, T., & Leus, G. (2011). Cooperative localization using efficient kalman filtering for mobile wireless sensor networks. In: 19th European, IEEE signal processing conference, 2011. pp. 1984–1988.
31.
Raj, K. & Jaiswal, J. C. D. (2015). Edagf: Estimation and direction aware greedy forwarding for urban scenario in vehicular ad-hoc network. In: UIC-ATC-ScalCom-CBDCom-IoP, 2015 IEEE, pp. 814–821.
32.
Reza, T. A., Barbeau, M., & Alsubaihi, B. (2013). Tracking an on the run vehicle in a metropolitan vanet. In: Intelligent vehicles Symposium (IV), 2013 IEEE, IEEE, pp. 220–227.
33.
Sharef, B. T., Alsaqour, R. A., & Ismail, M. (2014). Review: Vehicular communication ad hoc routing protocols: A survey. Journal of Network and Computer Applications, 40, 363–396.CrossRef
34.
Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.CrossRef
35.
Sun, L., Wu, Y., Xu, J., & Xu, Y. (2012). An rsu-assisted localization method in non-gps highway traffic with dead reckoning and v2r communications. In: 2nd international conference on consumer electronics, communications and networks (CECNet), 2012, IEEE, pp. 149–152.
36.
Vasilakos, A. V., Li, Z., Simon, G., & You, W. (2015). Information centric network: Research challenges and opportunities. Journal of Network and Computer Applications, 52, 1–10.CrossRef
37.
Welch, G., & Bishop, G. (1995). An introduction to the Kalman filter. Tech. report, Chapel Hill, NC, USA.
38.
Xiao, Y. Y., Zhang, H., & Wang, H. Y. (2007). Location prediction for tracking moving objects based on grey theory. In: Fourth international conference on fuzzy systems and knowledge discovery, 2007. FSKD 2007. IEEE, Vol. 2, pp. 390–394.
39.
Yang, M., Li, Y., Jin, D., Zeng, L., Wu, X., & Vasilakos, A. V. (2015a). Software-defined and virtualized future mobile and wireless networks: A survey. Mobile Networks and Applications, 20(1), 4–18.CrossRef
40.
Yang, M., Li, Y., Jin, D., Zeng, L., Wu, X., & Vasilakos, A. V. (2015b). Software-defined and virtualized future mobile and wireless networks: A survey. Mobile Networks and Applications, 20(1), 4–18.CrossRef
41.
Yao, Y., Cao, Q., & Vasilakos, A. V. (2013). Edal: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In: IEEE 10th international conference on mobile ad-hoc and sensor systems (MASS), 2013, pp. 182–190.
42.
Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). Edal: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23(3), 810–823.CrossRef
43.
Yen, Y. S., Chao, H. C., Chang, R. S., & Vasilakos, A. (2011). Flooding-limited and multi-constrained qos multicast routing based on the genetic algorithm for manets. Mathematical and Computer Modelling, 53(11), 2238–2250.CrossRef
44.
Youssef, M., IBRAHIM, M., Abdelatif, M., Chen, L., & Vasilakos, A. V. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys Tutorials, 16(1), 92–109.CrossRef
45.
Zeng, Y., Xiang, K., Li, D., & Vasilakos, A. V. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.CrossRef
46.
Zhang, X. M., Zhang, Y., Yan, F., & Vasilakos, A. V. (2015). Interference-based topology control algorithm for delay-constrained mobile ad hoc networks. IEEE Transactions on Mobile Computing, 14(4), 742–754.CrossRef
47.
Zhou, J., Cao, Z., Dong, X., Lin, X., & Vasilakos, A. V. (2013). Securing m-healthcare social networks: Challenges, countermeasures and future directions. IEEE Wireless Communications, 20(4), 12–21.CrossRef
48.
Zhou, J., Cao, Z., Dong, X., Xiong, N., & Vasilakos, A. V. (2015a). 4s: A secure and privacy-preserving key management scheme for cloud-assisted wireless body area network in m-healthcare social networks. Information Sciences, 314, 255–276.CrossRef
49.
Zhou, J., Dong, X., Cao, Z., & Vasilakos, A. V. (2015b). Secure and privacy preserving protocol for cloud-based vehicular dtns. IEEE Transactions on Information Forensics and Security, 10(6), 1299–1314.CrossRef
Metadaten
Titel
Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter
verfasst von
Raj K. Jaiswal
C. D. Jaidhar
Publikationsdatum
19.04.2016
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 7/2017
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-016-1265-4

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