Abstract
The province of wireless sensor networks (WSNs) is continuously increasing due to widespread applications, like, military, monitoring environmental conditions, and several other domains. However, trust management in the WSN is a major challenge as trust is used when cooperation between nodes becomes critical to attaining reliable communication. Therefore, a new trust-based routing algorithm is proposed for initiating secured routing. Additionally, the paper proposes Chicken-Dragonfly (CHicDra) optimization algorithm for assisting secure communication by finding the optimal cluster heads (CHs) in the network. Once the CHs are selected with Multi-Objective Taylor Crow Optimization, the trusted nodes are optimally finalized using the Joint Trust that depends on the trust parameters, like integrity factor, consistency factors, forwarding rate factor, and availability factors. The proposed CHicDra is the modification of the chicken swam optimization with dragonfly algorithm. Finally, the optimally chosen path is employed for further communications in the network, which is secure and trustworthy. The proposed CHicDra computes maximal packet delivery ratio (PDR) of 44%, throughput of 52.8%, and minimal delay of 0.344, respectively.
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Arjunan, S., & Sujatha, P. (2018). Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol. Applied Intelligence,48, 2229–2246.
Kong, L., Pan, J. S., Snášel, V., Tsai, P. W., & Sung, T. W. (2018). An energy-aware routing protocol for wireless sensor network based on genetic algorithm. Telecommunication Systems,67(3), 451–463.
Sarkar, A., & Murugan, T. S. (2019). Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wireless Networks,25(1), 303–320.
Shankar, T., Shanmugavel, S., & Rajesh, A. (2016). Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm and Evolutionary Computation,30, 1–10.
Crosby, G. V., Pissinou, N., & Gadze, J. (2006). A framework for trust-based cluster head election in wireless sensor networks. In Proceedings of Second IEEE Workshop on Dependability and Security in Sensor Networks and Systems (p. 10).
Khan, F., Gul, T., Ali, S., Rashid, A., Shah, D., & Khan, S.(2018). Energy aware cluster-head selection for improving network life time in wireless sensor network. In Proceedings of Science and Information Conference (pp. 581-593). Springer.
Robinson, Y. H., Julie, E. G., & Kumar, R. (2019). Probability-based cluster head selection and fuzzy multipath routing for prolonging lifetime of wireless sensor networks. Peer-to-Peer Networking and Applications,12, 1061–1075.
Ram Mohan, C., & Ananthula, V. R. (2019). Reputation-based secure routing protocol in mobile ad-hoc network using Jaya Cuckoo optimization. International Journal of Modeling, Simulation, and Scientific Computing,10(3), 1950014.
RamMohan, C., & Reddy, A. V. (2018). T-Whale: Trust and Whale optimization model for secure routing in mobile ad-hoc network. International Journal of Artificial Life Research (IJALR),8(2), 67–79.
Gilbert, E. P. K., Baskaran, K., Rajsingh, E. B., Lydia, M., & Immanuel Selvakumar, A. (2019). Trust aware nature inspired optimised routing in clustered wireless sensor networks. International Journal of Bio-Inspired Computation,14(2), 103–113.
Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE Wireless Communications,11(6), 6–28.
Draves, R., Padhye, J., & Zill, B. (2004). Comparison of routing metrics for multi-hop wireless networks. In Proceedings of ACM SIGCOMM.
Perkins, C. E., & Bhagwat, P. (1994). Highly dynamic destination sequenced distance-vector routing (DSDV) for mobile computers. In Proceedings of ACM SIGCOMM.
Johnson, D. B., Maltz, D. A., & Broch, J. (2001). DSR: The dynamic source routing protocol for multihop wireless ad hoc networks. Ad Hoc Networking,5, 1–25.
Perkins, C. E., & Royer, E. M. (1999). Ad hoc on-demand distance vector routing. In Proceedings of the Workshop on Mobile Computing Systems and Applications.
Zahariadis, T., Leligou, H., Karkazis, P., Trakadas, P., Papaefstathiou, I., Vangelatos, C., et al. (2011). Design and implementation of a trust-aware routing protocol for Largewsns. International Journal of Network Security & Its Applications,2(3), 52–68.
Babu, S. S., Raha, A., & Naskar, M. K. (2011). Trustworthy route formation algorithm for WSNs. International Journal of Computers and Applications,27(5), 0975–8887.
Zhan, G., Shi, W., & Deng, J. (2012). Design and implementation of TARF: A trust-aware routing framework for WSNs. IEEE Transactions on Dependable and Secure Computing,9(2), 184–197.
Karthick, S. (2018). TDP: A novel secure and energy aware routing protocol for wireless sensor networks. International Journal of Intelligent Engineering and Systems,11(2), 76–84.
Selvi, M., Thangaramya, K., Ganapathy, S., Kulothungan, K., Nehemiah, H. K., & Kannan, A. (2019). An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wireless Personal Communications,105(4), 1475–1490.
Veeraiah, N., & Krishna, B. T. (2018). Intrusion detection based on piecewise fuzzy C-means clustering and fuzzy Naïve Bayes rule. Multimedia Research,1(1), 27–32.
Dhand, G., & Tyagi, S. S. (2019). SMEER: Secure multi-tier energy efficient routing protocol for hierarchical wireless sensor networks. Wireless Personal Communications,105(1), 17–35.
Udhayavani, M., & Chandrasekaran, M. (2018). Design of TAREEN (trust aware routing with energy efficient network) and enactment of TARF: A trust-aware routing framework for wireless sensor networks. Cluster Computing,22, 11919–11927.
Asha, G., & Santhosh, R. (2019). Soft computing and trust-based self-organized hierarchical energy balance routing protocol (TSHEB) in wireless sensor networks. Soft Computing,23(8), 2537–2543.
Gilbert, E. P. K., Kaliaperumal, B., Rajsingh, E. B., & Lydia, M. (2018). Trust based data prediction, aggregation and reconstruction using compressed sensing for clustered wireless sensor networks. Computers & Electrical Engineering,72, 894–909.
Desai, S. S., & Nene, M. J. (2019). Node-level trust evaluation in wireless sensor networks. IEEE Transactions on Information Forensics and Security,14(8), 2139–2152.
Kavidha, V., & Ananthakumaran, S. (2018). Novel energy-efficient secure routing protocol for wireless sensor networks with mobile sink. Peer-to-Peer Networking and Applications,12, 881–892.
Meng, X., Liu, Y., Gao, X., & Zhang, H. (2014). A new bio-inspired algorithm: chicken swarm optimization. In Proceedings of International Conference in Swarm Intelligence (pp. 86–94). Springer.
Mirjalili, S. (2016). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications,27(4), 1053–1073.
John, J., & Rodrigues, P. (2019). MOTCO: Multi-objective Taylor Crow optimization algorithm for cluster head selection in energy aware wireless sensor network. Mobile Networks and Applications,24(5), 1509–1525.
Alamelu Mangai, S., Ravi Sankar, B., & Alagarsamy, K. (2014). Taylor series prediction of time series data with error propagated by artificial neural network. International Journal of Computer Applications,89(1), 41–47.
Askarzadeh, A. (2016). A novel meta heuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures,169, 1–12.
Kumar, R., & Kumar, D. (2016). Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wireless Networks,22(5), 1461–1474.
Kang, J., Zhang, Y., & Nath, B. (2005). Accurate and energy-efficient congestion level measurement in ad hoc networks. In Proceedings of IEEE International Conference on Wireless Communications and Networking Conference (Vol. 4).
Zhu, J. (2018). Wireless sensor network technology based on security trust evaluation model. International Journal of Online Engineering,14(4), 211–226.
Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Networks,18(7), 847–860.
Sarangi, S., & Thankchan, B. (2012). A novel routing algorithm for wireless sensor network using particle swarm optimization. Journal of Computer Engineering,4(1), 26–30.
Elshrkawey, M., Elsherif, S. M., & Wahed, M. E. (2018). An enhancement approach for reducing the energy consumption in wireless sensor networks. Journal of King Saud University – Computer and Information Sciences,30(2), 259–267.
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Rodrigues, P., John, J. Joint trust: an approach for trust-aware routing in WSN. Wireless Netw 26, 3553–3568 (2020). https://doi.org/10.1007/s11276-020-02271-w
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DOI: https://doi.org/10.1007/s11276-020-02271-w