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Multi-hop routing optimization method based on improved ant algorithm for vehicle to roadside network

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Abstract

This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network (VANET). A novel bionic swarm intelligence algorithm, which is called ant colony algorithm, was introduced into a traditional ad-hoc route algorithm named AODV. Based on the analysis of movement characteristics of vehicles and according to the spatial relationship between the vehicles and the roadside units, the parameters in ant colony system were modified to enhance the performance of the route selection probability rules. When the vehicle moves into the range of several different roadsides, it could build the route by sending some route testing packets as ants, so that the route table can be built by the reply information of test ants, and then the node can establish the optimization path to send the application packets. The simulation results indicate that the proposed algorithm has better performance than the traditional AODV algorithm, especially when the vehicle is in higher speed or the number of nodes increases.

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References

  1. Wisitpongphan N, Bai F, Mudalige P, Sadekar V, Tonguz O. Routing in sparse vehicular ad hoc wireless networks. IEEE Journal on Selected Areas in Communications, 2007, 25, 1538–1556.

    Article  Google Scholar 

  2. Wisitpongphan N, Tonguz O K, Parikh J S, Mudalige P, Bai F, Sadekar V. Broadcast storm mitigation techniques in vehicular ad hoc networks. IEEE Wireless Commimications, 2007, 14, 84–94.

    Article  Google Scholar 

  3. Bruno R, Conti M. Throughput and fairness analysis of 802.11-based vehicle-to-infrastructure data transfers. 2011 IEEE 8th International Conference on Mobile Adhoc and Sensor Systems (MASS), Valencia, Spain, 2011, 232–241.

    Google Scholar 

  4. Liu Y, Han L. The research on an AODV-BRL to increase reliability and reduce routing overhead in MANET. 2010 International Conference on Computer Application and System Modeling (ICCASM), Taiyuan, China, 2010, 526–530.

    Google Scholar 

  5. Jarupan B, Ekici E. PROMPT: A cross-layer position-based communication protocol for delay-aware vehicular access networks. Ad Hoc Networks, 2010, 8, 489–505.

    Article  Google Scholar 

  6. Perkins C E, Royer E M. Ad-hoc on-demand distance vector routing. Proceedings of the 2nd IEEE Workshop on Mobile Computer Systems and Applications, New Orleans, USA, 1999, 80–90.

    Google Scholar 

  7. Perkins C E, Bhagwat P. Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. Proceedings of the Conference on Communication, Architectures, Protocols and Applications, London, UK, 1994, 24, 234–244.

    Google Scholar 

  8. Rak J. Providing differentiated levels of service availability in VANET communications. IEEE Communications Letters, 2013, 17, 1380–1383.

    Article  Google Scholar 

  9. Prathapani A, Santhanam L, Agrawal D P. Detection of blackhole attack in a wireless mesh network using intelligent honeypot agents. Journal of Supercomputing, 2013, 64, 777–804.

    Article  Google Scholar 

  10. Barba C T, Aguiar L U, Igartua M A. Design and evaluation of GBSR-B, an improvement of GPSR for VANETs. IEEE Latin America Transactions, 2013, 11, 1083–1089.

    Article  Google Scholar 

  11. Yu J, Yin C. An improved MAC protocol in mobile vehicular ad-hoc networks. 2011 International Conference on Energy and Environmental, Singapore, 2011, 1992–2000.

    Google Scholar 

  12. Qi G P, Song P, Li K J. Blackboard mechanism based ant colony theory for dynamic deployment of mobile sensor networks. Journal of Bionic Engineering, 2008, 5, 197–203.

    Article  Google Scholar 

  13. Liu F, Lo H, Juan S C. Energy-efficient ant-colony-based routing algorithm for the MANETs. Journal of Internet Technology, 2013, 14, 21–30.

    Google Scholar 

  14. Ait-Salem B, Riahla M A, Tamine K. A hybrid multiagent routing approach for wireless ad hoc networks. Wireless Networks, 2012, 18, 837–845.

    Article  Google Scholar 

  15. Yadav M, Arya K V, Rishiwal V. Improved ant colony optimization technique for mobile adhoc networks. 1st International Conference on Computer Science and Information Technology, Bangalore, India, 2011, 1–13.

    Google Scholar 

  16. Duan H B, Zhang X Y, Wu J, Ma G J. Max-min adaptive ant colony optimization approach to multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments. Journal of Bionic Engineering, 2009, 6, 161–173.

    Article  Google Scholar 

  17. Shi L, Xi L, Ma X M, Weng M, Hu X H. A novel ensemble algorithm for biomedical classification based on ant colony optimization. Applied Soft Computing, 2011, 11, 5674–5683.

    Article  Google Scholar 

  18. Dorigo M, Di G, Gambardella L M. Ant algorithms for discrete optimization. Artificial Life, 1999, 5, 137–172.

    Article  Google Scholar 

  19. Dorigo M, Maniezzo V, Colorni A. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, 1996, 26, 29–41.

    Article  Google Scholar 

  20. Dorigo M, Gambardella L M. Ant Colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1997, 1, 53–66.

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Communication Engineering, Jilin University, Changchun, 130000, P. R. China

    Hao Dong, Xiaohui Zhao, Liangdong Qu, Xuefen Chi & Xinyu Cui

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  1. Hao Dong
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  2. Xiaohui Zhao
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  3. Liangdong Qu
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  4. Xuefen Chi
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  5. Xinyu Cui
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Correspondence to Liangdong Qu.

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Dong, H., Zhao, X., Qu, L. et al. Multi-hop routing optimization method based on improved ant algorithm for vehicle to roadside network. J Bionic Eng 11, 490–496 (2014). https://doi.org/10.1016/S1672-6529(14)60061-5

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