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MCTRP: An Energy Efficient Tree Routing Protocol for Vehicular Ad Hoc Network Using Genetic Whale Optimization Algorithm

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Abstract

VANETs are wireless sensor networks that suffer from the drawback of highly mobile nodes. The main objective of any type of network is to achieve efficient transmission goals. The vehicles act as the transmitting nodes. Cognitive radio technology helps in sensing the spectrum in order to ensure the efficient usage of the reserved channels by all the nodes. Our proposed system incorporates a routing protocol with the cognitive radio technology for efficient channel assignment. The routing protocol applies a tree based structure for efficient routing within and between networks. The tree routing protocol is further altered by the inclusion of an efficient optimized scheme. The proposed technique involves a Genetic Whale Optimization Algorithm which helps in choosing a root channel for transmission. When the selected root channel becomes active, the other channels are disabled. The proposed tree routing protocol is called the modified cognitive tree routing protocol (MCTRP).Apart from routing this protocol also caters to the need of effective channel utilization by allocating the spectrum fairly. This scheme results in ranking the channels based on their transmission efficiency and also aims to reduce the inherent delay usually associated with VANETs. The protocol also handles link breakages efficiently. The proposed scenario is simulated in NS2 and is evaluated based on the major network metrics. Our protocol shows a sharp decline in the associated delay and guarantees effective channel utilization. The proposed MCTRP method is effective over other protocols, such as, CTRP and OLSR. The analytical results show that MCTRP promises minimum overheads with effective channel utilization than the existing protocols.

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References

  1. Jalil Piran, M., Cho, Y., Yun, J., Ali, A., & Suh, D. Y. (2014). Cognitive radio-based vehicular ad hoc and sensor networks. International Journal of Distributed Sensor Networks,10(8), 1–11.

    Article  Google Scholar 

  2. Nassef, L., & Alhebshi, R. (2016). Secure spectrum sensing in cognitive radio sensor networks: A survey. International Journal of Computational Engineering Research (IJCER),6(3), 1–7.

    Google Scholar 

  3. Patel, J., & Thakkar, M. (2014). A survey on cognitive radio wireless sensor networks. International Journal of Engineering Development and Research, 1(3), 146–148.

    Google Scholar 

  4. Abolarinwa, J. A., Salawu. N., & Achonu. (2013). Cognitive radio-based wireless sensor networks as next generation sensor network: Concept, problems and prospects. Journal of Emerging Trends in Computing and Information Sciences,4(8), 146–148.

    Google Scholar 

  5. Qu, Y., Dong, C., Tang, S., Chen, C., Dai, H., Wang, H., et al. (2017). Opportunistic network coding for secondary users in cognitive radio networks. Ad Hoc Networks,56, 186–201.

    Article  Google Scholar 

  6. Rao, K. L., Kalyana Chakravarthy, C., & Chilukuri, S. (2015). Energy efficient routing in cognitive radio networks: Challenges and existing solutions. Journal on Communication Technology: Special Issue,6, 1039–1052.

    Google Scholar 

  7. Mishra, P., & Dewangan, N. (2015). Survey on optimization methods for spectrum sensing in cognitive radio networks. International Journal of New Technology and Research,1(6), 23–28.

    Google Scholar 

  8. Wang, J., Chen, R., Tsai, J. J. P., & Wang, D.-C. (2018). Trust-based mechanism design for cooperative spectrum sensing in cognitive radio networks. Computer Communications,116, 90–100.

    Article  Google Scholar 

  9. Singh, J. S. P., & Rai, M. K. (2017). CROP: Cognitive radio routing protocol for link quality channel diverse cognitive networks. Journal of Network and Computer Applications,104, 48–60.

    Article  Google Scholar 

  10. Rathika, P. D., & Sophia, S. (2017). A distributed scheduling approach for QoS improvement in cognitive radio networks. Computers & Electrical Engineering,57, 186–198.

    Article  Google Scholar 

  11. Ramzan, M. R., Nawaz, N., Ahmed, A., Naeem, M., Iqbal, M., & Anpalagan, A. (2017). Multi-objective optimization for spectrum sharing in cognitive radio networks: A review. Pervasive and Mobile Computing,41, 106–131.

    Article  Google Scholar 

  12. Bouabdellah, M., Kaabouch, N., El Bouanani, F., & Ben-Azza, H. (2018). Network layer attacks and countermeasures in cognitive radio networks: A survey. Journal of Information Security and Applications,38, 40–49.

    Article  Google Scholar 

  13. Ozger, M., & Akan. O.B. (2013). Event-driven spectrum-aware clustering in cognitive radio sensor networks. In Proceedings of INFOCOM (pp. 1483–1491).

  14. Bicen, A. O., Cagri Gungor, V., & Akan, O. B. (2012). Delay-sensitive and multimedia communication in cognitive radio sensor networks. Ad Hoc Networks,10(5), 816–830.

    Article  Google Scholar 

  15. Esmaeelzadeh, V., Hosseini, E. S., Berangi, R., & Akan, O. B. (2016). Modeling of rate-based congestion control schemes in cognitive radio sensor networks. Ad Hoc Networks,36, 177–188.

    Article  Google Scholar 

  16. Kumbhar, S. V., & Durafe, A. (2015). Cognitive radio sensor network future of wireless sensor network. International Journal of Advanced Research in Computer and Communication Engineering,4(2), 492–495.

    Google Scholar 

  17. Houaidia, C., Idoudi, H., Van Den Bossche, A., Saidane, L. A., & Val, T. (2017). Inter-flow and intra-flow interference mitigation routing in wireless mesh networks. Computer Networks,120, 141–156.

    Article  Google Scholar 

  18. Al-Turjman, F. (2017). Cognitive routing protocol for disaster-inspired internet of things. Future Generation Computer Systems,92, 2–21.

    Google Scholar 

  19. Hashem, M., Barakat, S. I., & AttaAlla, M. A. (2017). Enhanced tree routing protocols for multi-hop and multi-channel cognitive radio network (EMM-TRP). Journal of Network and computer applications,10, 1–19.

    Google Scholar 

  20. Walikar, G. A., & Biradar, R. C. (2017). A survey on hybrid routing mechanisms in mobile ad hoc networks. Journal of Network and Computer Applications,77, 48–63.

    Article  Google Scholar 

  21. Fadel, E., Faheem, M., Gungor, V. C., Nassef, L., Akkari, N., Malik, M. G. A., et al. (2017). Spectrum-aware bio-inspired routing in cognitive radio sensor networks for smart grid applications. Computer Communications,101, 106–120.

    Article  Google Scholar 

  22. Sekhar, R., Raja, K., Ravi Chandra, T. S., Pooja, S., & Tapaswi, S. (2016). Light weight security protocol for communications in vehicular networks. Wireless Networks,22(4), 1343–1353.

    Article  Google Scholar 

  23. Sathiamoorthy, J., & Ramakrishnan, B. (2016). Energy and delay efficient dynamic cluster formation using improved ant colony optimization algorithm in EAACK MANETs. Wireless Personal Communications,95, 1531–1552.

    Article  Google Scholar 

  24. Hof, P. R., & Van Der Gucht, E. (2007). Structure of the cerebral cortex of the humpback whale, Megaptera novaeangliae (Cetacea, Mysticeti, Balaenopteridae). The Anatomical Record,290, 1–31.

    Article  Google Scholar 

  25. Watkins, W. A., & Schevill, W. E. (1989). Aerial observation of feeding behavior in four baleen whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaean- gliae, and Balaenoptera physalus. Journal of Mammalogy,60, 155–163.

    Article  Google Scholar 

  26. Goldbogen, J. A., Friedlaender, A. S., Calambokidis, J., Mckenna, M. F., Simon, M., & Nowacek, D. P. (2013). Integrative approaches to the study of baleen whale diving behavior, feeding performance, and foraging ecology. BioScience,63, 90–100.

    Article  Google Scholar 

  27. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software,95, 51–67.

    Article  Google Scholar 

  28. Harikarthik, S. K., Palanisamy, V., & Ramanathan, P. (2017). Optimal test suite selection in regression testing with testcase prioritization using modified ann and whale optimization algorithm. Cluster Computing. https://doi.org/10.1007/s10586-017-1401-7.

    Article  Google Scholar 

  29. Ying, L., Zhou, Y., & Luo, Q. (2017). Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access,5, 6168–6186.

    Article  Google Scholar 

  30. www.wseas.org.

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Author notes

  1. Usha Mohanakrishnan & B. Ramakrishnan

    Present address: , Chennai, India

Authors and Affiliations

  1. Manonmaniam Sundaranar University, Tirunelveli, India

    Usha Mohanakrishnan

  2. ST Hindu College, Nagercoil, India

    B. Ramakrishnan

Authors
  1. Usha Mohanakrishnan
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  2. B. Ramakrishnan
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Correspondence to Usha Mohanakrishnan.

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Mohanakrishnan, U., Ramakrishnan, B. MCTRP: An Energy Efficient Tree Routing Protocol for Vehicular Ad Hoc Network Using Genetic Whale Optimization Algorithm. Wireless Pers Commun 110, 185–206 (2020). https://doi.org/10.1007/s11277-019-06720-4

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  • DOI: https://doi.org/10.1007/s11277-019-06720-4

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