JACIII Vol.18 No.1 pp. 9-21
doi: 10.20965/jaciii.2014.p0009


Fuzzy Inference Based Vehicle to Vehicle Network Connectivity Model to Support Optimization Routing Protocol for Vehicular Ad-Hoc Network (VANET)

Chehung Lin, Fangyan Dong, and Kaoru Hirota

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

June 15, 2013
October 18, 2013
January 20, 2014
optimization, routing protocol, fuzzy inference, ad-hoc network, VANET
A Fuzzy Inference based Vehicle to Vehicle Network Connectivity Model is proposed to support Optimization Routing Protocol for Vehicular Ad-hoc Network (VANET), where the real-time vehicle to vehicle network connectivity situation of road segments is expressed using fuzzy inference according to the vehicle distribution situation, and the optimized routing protocol modifies the transmission path dynamically and optimizes packet forwarding. The proposed model expresses the real-time vehicle to vehicle network connectivity of each road segment that cannot be easily expressed directly by a mathematical model and decreases the end-to-end delay and the overall network control overhead. The computation time of the proposed protocol is analyzed and shown as O(IlgI + R + V) where I, R, and V represent the number of intersections on a map, the number of road segments on a map, and the number of vehicles within communication range of the vehicle that wants to transfer a data packet, respectively. The simulation tools NS2 and TraNS are used to perform experiments that include wireless data packet transmission and vehicle mobility traces. The results show that the proposed method decreases end-to-end delay and decreases the control overhead by 20% compared with other routing protocols, e.g. GyTAR and RTRP. This proposal implements an intelligent transportation system application and a traffic-monitoring system in NS2 using the optimization routing protocol. This protocol will be implemented to develop a real vehicle telematics system using the embedded system to improve the user-driving experience.
Cite this article as:
C. Lin, F. Dong, and K. Hirota, “Fuzzy Inference Based Vehicle to Vehicle Network Connectivity Model to Support Optimization Routing Protocol for Vehicular Ad-Hoc Network (VANET),” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.1, pp. 9-21, 2014.
Data files:
  1. [1] Y. Toor, P.Muhlethaler, and A. Laouiti, “Vehicle Ad Hoc networks: applications and related technical issues,” IEEE Communications Surveys & Tutorials, Vol.10, No.3, pp. 74-88, 2008.
  2. [2] Intelligent Transportation Systems (ITS) Standards Program Strategic Plan for 2011–2014.
  3. [3] S. Dashtinezhad, T. Nadeem, B. Dorohonceanu, C. Borcea, P. Kang, and L. Iftode, “TrafficView: a driver assistant device for traffic monitoring based on car-to-car communication,” IEEE 59th Vehicular Technology Conf., Vol.5, pp. 2946-2950, 2004.
  4. [4] Y. Ching-Yi and L. Shou-Chih, “Street Broadcast with Smart Relay for Emergency Messages in VANET,” IEEE 24th Int. Conf. on Advanced Information Networking and Applications Workshops (WAINA), pp. 323-328, 2010.
  5. [5] C. Lochert, H. Hartenstein, J. Tian, H. Fussler, D. Hermann, and M. Mauve, “A routing strategy for vehicular ad hoc networks in city environments,” IEEE Proc. of Intelligent Vehicles Symposium, pp. 156-161, 2003.
  6. [6] J. J. Blum, A. Eskandarian, and L. J. Hoffman, “Challenges of intervehicle ad hoc networks,” IEEE Trans. on Intelligent Transportation Systems, Vol. 5 No. 4, pp. 347-351, 2004.
  7. [7] L. Fan and W. Yu, “Routing in vehicular ad hoc networks: A survey,” IEEE Vehicular Technology Magazine, Vol.2 No.2, pp. 12-22, 2007.
  8. [8] C. Wai, R. K. Guha, K. Taek Jin, J. Lee, and I. Y. Hsu, “A survey and challenges in routing and data dissemination in vehicular adhoc networks,” IEEE Int. Conf. on Vehicular Electronics and Safety, pp. 328-333, 2008.
  9. [9] G. Zhang, D. Mu, Z. Xu, W. Yang, and X. Cai, “A survey on the routing schemes of urban Vehicular Ad Hoc Networks,” 27th Chinese Control Conf., pp. 338-343, 2008.
  10. [10] J. Nzouonta, N. Rajgure, G. Wang, and C. Borcea, “VANET routing on city roads using real-time vehicular traffic information,” IEEE Trans. on Vehicular Technology, Vol.58, No.7, pp. 3609-3626, 2009.
  11. [11] C. Lochert , M. Mauve, H. Füssler, H. Hartenstein et al., “Geographic routing in city scenarios,” ACM SIGMOBILE Mobile Computing and Communications Review, Vol.9, No.l, pp. 69-72, 2005.
  12. [12] W. Yan-Bo, W. Tin-Yu, L.Wei-Tsong, and K. Chih-Heng, “A Novel Geographic Routing Strategy over VANET,” IEEE 24th Int. Conf. on Advanced Information Networking and Applications Workshops (WAINA), pp. 873-879, 2010.
  13. [13] V. K. Muhammed Ajeer, P. C. Neelakantan, and A. V. Babu, “Network connectivity of one-dimensional Vehicular Ad hoc Network,” Int. Conf. in Communications and Signal Processing (ICCSP), pp. 241-245, 2011.
  14. [14] J. Xin, S.Weijie, andW. Yan, “Quantitative Analysis of the VANET Connectivity: Theory and Application,” IEEE 73rd Vehicular Technology Conf. (VTC Spring), pp. 1-5, 2011.
  15. [15] M. Jerbi, S. M. Senouci, T. Rasheed, and Y. Ghamri-Doudane, “Towards Efficient Geographic Routing in Urban Vehicular Networks,” IEEE Trans. on Vehicular Technology, Vol.58, No.9, pp. 5048-5059, 2009.
  16. [16] H. Pham Thi, P. Hyunhee, and K. Chul-Hee, “A Road and Trafficaware Routing Protocol in Vehicular Ad hoc Networks,” 13th Int. Conf. on Advanced Communication Technology (ICACT), pp. 24-28, 2011.
  17. [17] The Network Simulator - ns-2., 1995.
  18. [18] M. Piorkowski, M. Raya, A. L. Lugo, P. Papadimitratos, M. Grossglauser, and J. P. Hubaux, “ TraNS: Realistic Joint Traffic and Network Simulator for VANETs,” ACM SIGMOBILE Mobile Computing and Communications Review, Vol.12, pp. 31-33, 2008.
  19. [19] L. Qiang, X. Lunhui, C. Zhihui, and H. Yanguo, “Simulation analysis and study on car-following safety distance model based on braking process of leading vehicle,” 9th World Congress on Intelligent Control and Automation (WCICA), pp. 740-743, 2011.
  20. [20] D. Hepu, and Y. Chung-Hsing, “Simulation-based evaluation of defuzzification-based approaches to fuzzy multiattribute decision making,” IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems and Humans, Vol.36, No.5, pp. 968-977, 2006.
  21. [21] A. Ginart, G. Sanchez, I. Links, and G. Back, “Fast defuzzification method based on centroid estimation,” Applied Modelling and Simulation, 2002.
  22. [22] I. Leontiadis, G. Marfia, D. Mack, G. Pau, C. Mascolo, and M. Gerla, “On the Effectiveness of an Opportunistic Traffic Management System for Vehicular Networks,” IEEE Trans. on Intelligent Transportation Systems, Vol.12, pp. 1537-1548, 2011.
  23. [23] E.W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, Vol.1, No.1, pp. 269-271, 1959.
  24. [24] H. Saleet, R. Langar, O. Basir, and R. Boutaba, “A Distributed Approach for Location Lookup in Vehicular Ad Hoc Networks,” IEEE Int. Conf. on Communications (ICC ’09), pp. 1-6, 2009.
  25. [25] S. Zaki, M. A. Ngadi, S. Razak, M. Kamat, and J. Shariff, “Location Service Management Protocol for Vehicular Ad Hoc Network Urban Environment,” Advances in Computer Science and Information Technology, Computer Science and Engineering, Vol.85, pp. 563-574, 2012.
  26. [26] M. L. Fredman and R. E. Tarjan, “Fibonacci heaps and their uses in improved network optimization algorithms,” J. of the ACM, Vol.34, pp. 596-615, 1987.
  27. [27] D. Krajzewicz, “Traffic Simulation with SUMO – Simulation of Urban Mobility,” Fundamentals of Traffic Simulation, Vol.145, pp. 269-293, 2010.
  28. [28] M. Behrisch, L. Bieker, J. Erdmann, and D. Krajzewicz, “SUMOSimulation of Urban MObility-an Overview,” the 3rd Int. Conf. on Advances in System Simulation (SIMUL ’11), pp. 55-60, 2011.

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