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Vehicular Ad hoc Networks: A hybrid approach to data dissemination in exigency situations

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Abstract

Vehicular Ad hoc Network (VANET) is a type of mobile ad hoc network based on short range communications among vehicles moving on roads and between moving vehicles and road side units. Endangerment situations occur when a vehicle met with an accident at accident prone areas. Due to problem of high mobility and dynamic topology the performance of messaging in VANET ceases. To have sustainability in cross-correlated situations the pooling data acquisition requires data collection from multiple sources. In this paper, the data collection is done using Sensors and radio frequency identification (RFID) tags. The raw data collected by the sensors is optimized using Ant-Colony optimization for selecting shortest path dynamically to crossover the danger zone. The data detected by RFID tags is optimized using Artificial Bee Colony algorithm to obtain the information already collected by neighboring nodes. The information collected by VANET nodes will be cross-verified by authenticating with the Web Server for preventing fake message transmission over the network. The multivariate traffic data is well-kept at Web Server for smooth transmission of emergency messages. The process of preserving multivariate data provides elegant solution even on the part of signal failures in global system for mobile communications.

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References

  1. Jaber, N., Doyle, N. C., & Tepe, K. E. (2012). New combined WiMAX/DSRC infrastructure design for efficient vehicular networking. EURASIP Journal on Wireless Communications and Networking, 2012, 264.

    Article  Google Scholar 

  2. Cheng, H., et al. (2012). Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks. Ad Hoc Networks, 10(5), 760–773.

    Article  Google Scholar 

  3. Kassotakis, I. E., et al. (2000). A hybrid genetic approach for channel reuse in multiple access telecommunication networks. IEEE Journal on Selected Areas in Communications, 18(2), 234–243.

    Article  Google Scholar 

  4. Duarte, P. B. F., et al. (2012). On the partially overlapped channel assignment on wireless mesh network backbone: A game theoretic approach. IEEE Journal on Selected Areas in Communications, 30(1), 119–127.

    Article  Google Scholar 

  5. Yen, Y.-S., et al. (2011). Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Mathematical and Computer Modelling, 53(11–12), 2238–2250.

    Article  Google Scholar 

  6. Youssef, M., et al. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 16(1), 92–109.

    Article  Google Scholar 

  7. Liu, L., et al. (2015). Physarum optimization: A biology-inspired algorithm for the steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 819–832.

    MathSciNet  Google Scholar 

  8. Li, P., et al. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3264–3273.

    Article  Google Scholar 

  9. Meng, T., et al. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE TMC,. doi:10.1109/TC.2015.2417543.

    Google Scholar 

  10. Ozturk, S., Misic, J., & Misic, V. B. (2011). Reaching spatial or networking satuaration in VANET. EURASIP Journal on Wireless Communications and Networking, 2011, 174.

    Article  Google Scholar 

  11. Eiza, M. H., Ni, Q., Owes, T., & Min, G. (2013). Investigation of routing reliablitiy of vehicular ad hoc networks. EURASIP Journal on Wireless Communications and Networking, 2014, 179.

    Article  Google Scholar 

  12. Florin, R. (2015). A survey of vehicular communications for traffic signal optimization. Vehicular Communications, 2, 70–79.

    Article  Google Scholar 

  13. Vasilakos, A., et al. (2012). Delay tolerant networks: Protocols and applications. Boca Raton: CRC Press.

    Google Scholar 

  14. Hsieh, M.-Y., Lin, H.-Y., Lai, C.-F., & Li, K.-C. (2011). Secure protocols for data propagation and group communication in vehicular networks. EURASIP Journal on Wireless Communications and Networking, 2011, 167.

    Article  Google Scholar 

  15. Marwaha, S., et al. (2004). Evolutionary fuzzy multi-objective routing for wireless mobile ad hoc networks. In Congress on Evolutionary Computation, 2004. CEC2004 (Vol. 2, pp. 1964–1971).

  16. Ou, H.-H., Hwang, M.-S., & Jan, J.-K. (2009). The UMTS-AKA protocols for intelligent transportation systems. EURASIP Journal on Wireless Communications and Networking, 2009, 12. doi:10.1155/2009/267283.

    Google Scholar 

  17. Li, P., et al. (2012). CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined networkcoding. In INFOCOM (pp. 100–108)

  18. Xue, G., Luo, Y., Yu, J., & Li, M. (2012). A novel vehicular location prediction based on mobility patterns for routing in urban VANET. EURASIP Journal on Wireless Communications and Networking, 2012, 222.

    Article  Google Scholar 

  19. Liu, X.-Y., et al. (2014). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems. doi:10.1109/TPDS.2014.2345257.

    Google Scholar 

  20. Shen, Z., et al. (2011). Peer-to-peer media streaming: Insights and new developments. Proceedings of the IEEE, 99(12), 2089–2109.

    Article  Google Scholar 

  21. Xu, X., et al. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactionson Sensor Networks (TOSN), 11(3), 45.

    Google Scholar 

  22. Wang, P., Liu, F., Li, D., & Van, N. N. (2014). A traffic flow phase adaptive routing for vehicular communication on highways. EURASIP Journal on Wireless Communications and Networking, 2014, 38.

    Article  Google Scholar 

  23. Zhou, L., et al. (2011). Joint forensics-scheduling strategy for delay-sensitive multimedia applications over heterogeneous networks. IEEE Journal on Selected Areas in Communications, 29(7), 1358–1367.

    Article  Google Scholar 

  24. Xiang, L., et al. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In SECON (pp. 46–54).

  25. Wei, G., et al. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  26. Xiang, L., et al. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In IEEE SECON.

  27. Song, Y., et al. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

    Article  Google Scholar 

  28. Rahimi, M. R., et al. (2012). MAPCloud: Mobile applications on an elastic and scalable 2-tier cloud architecture. In IEEE/ACMUCC (pp. 83–90).

  29. Spyropoulos, T., et al. (2010). Routing for disruption tolerant networks: Taxonomy and design. Wireless Networks, 16(8), 2349–2370.

    Article  Google Scholar 

  30. Liu, Y., et al. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.

    Article  Google Scholar 

  31. Tsai, P.-W., Pan, J.-S., Liao, B.-Y., & Chu, S.-C. (2009). Enhanced artificial bee colony optimization. International Journal of Innovative Computing, Information and Control, 5(12), 5081–5092.

    Google Scholar 

  32. Attar, A., et al. (2012). A survey of security challenges in cognitive radio networks: Solutions and future research directions. Proceedings of the IEEE, 100(12), 3172–3186.

    Article  Google Scholar 

  33. Lalitha, R. V. S., & Suma, G. J. (2015). Vehicular ad-hoc networks: Trimming pile-ups in data dissemination using HTPVANET algorithm. In IC3T 2015. Conference dates 25th and 26th of July, Springer.

  34. Lalitha, R. V. S., & Suma, G. J. (2015). Alleviating the effect of security vulnerabilities in VANETs through proximity sensors. In S. C. Satapathy, et al. (Eds.), Emerging ICT for Bridging the Future—Volume 1, Advances in Intelligent Systems and Computing (Vol. 337, pp. 31–41). Switzerland: Springer International Publishing. doi:10.1007/978-3-319-13728-5_4.

  35. Lalitha, R. V. S., & Suma, G. J. (2014). An adaptive approach for RFID based data dissemination in VANETs through ABC algorithm using android mobiles. In 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, 978-1-4799-7910-3/14 $31.00 © 2014 IEEE, IEEE Computer Society (p. 298). doi:10.1109/ICAIET.2014.56

  36. Lalitha, R. V. S., & Suma, G. J. (2015). A contemporary solution to ferret out and obviate the fake messages in vehicular ad hoc networks by not percolating through Web Server. In International Conference on Advanced Computing Technologies and Applications (ICACTA), Dwarakadas J. Sanghavi College of Engineering, 26th, 27th March 2015, Mumbai. Procedia Computer Science (Vol. 45(2015), 696–705). Elsevier.

  37. Zeng, Yanyuan, et al. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  38. Busch, C., et al. (2012). Approximating congestion + dilation in networks via “quality of routing” games. IEEE Transactions on Computers, 61(9), 1270–1283.

    Article  MathSciNet  Google Scholar 

  39. Li, M., et al. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.

    Article  Google Scholar 

  40. Yao, Y., et al. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In MASS (pp. 182–190).

  41. Wang, X., et al. (2012). A survey of green mobile networks: Opportunities and challenges. MONET, 17(1), 4–20.

    Google Scholar 

  42. Dvir, A., et al. (2011). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 41(4), 405–406.

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of I.T., JNTUK-University College of Engineering, Vizianagaram, India

    G. Jaya Suma

  2. Department of C.S.E., Sri Sai Aditya Institute of Science and Technology, Surampalem, India

    R. V. S. Lalitha

Authors
  1. G. Jaya Suma
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  2. R. V. S. Lalitha
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Correspondence to R. V. S. Lalitha.

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Suma, G.J., Lalitha, R.V.S. Vehicular Ad hoc Networks: A hybrid approach to data dissemination in exigency situations. Wireless Netw 22, 1725–1737 (2016). https://doi.org/10.1007/s11276-015-1052-7

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  • DOI: https://doi.org/10.1007/s11276-015-1052-7

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