Optimal Coverage in Wireless Sensor Network using Augmented Nature-Inspired Algorithm
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Abstract
One of the difficult problems that must be carefully considered before any network configuration is getting the best possible network coverage. The amount of redundant information that is sensed is decreased due to optimal network coverage, which also reduces the restricted energy consumption of battery-powered sensors. WSN sensors can sense, receive, and send data concurrently. Along with the energy limitation, accurate sensors and non-redundant data are a crucial challenge for WSNs. To maximize the ideal coverage and reduce the waste of the constrained sensor battery lifespan, all these actions must be accomplished. Augmented Nature-inspired algorithm is showing promise as a solution to the crucial problems in “Wireless Sensor Networks” (WSNs), particularly those related to the reduced sensor lifetime. For “Wireless Sensor Networks” (WSNs) to provide the best coverage, we focus on algorithms that are inspired by Augmented Nature in this research. In wireless sensor networks, the cluster head is chosen using the Diversity-Driven Multi-Parent Evolutionary Algorithm. For Data encryption Improved Identity Based Encryption (IIBE) is used. For centralized optimization and reducing coverage gaps in WSNs Time variant Particle Swarm Optimization (PSO) is used. The suggested model's metrics are examined and compared to various traditional algorithms. This model solves the reduced sensor lifetime and redundant information in Wireless Sensor Networks (WSNs) as well as will give real and effective optimum coverage to the Wireless Sensor Networks (WSNs).
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References
Anand, J.V., 2020. Trust-value-based wireless sensor network using compressed sensing. Journal of Electronics, 2(02), pp.88-95.
Attea, B.A.A., Abbas, M.N., Al-Ani, M. and Özdemir, S., 2019. Bio-inspired multi-objective algorithms for connected set K-covers problem in wireless sensor networks. Soft Computing, 23(22), pp.11699-11728.
Ketshabetswe, L.K., Zungeru, A.M., Mangwala, M., Chuma, J.M. and Sigweni, B., 2019. Communication protocols for wireless sensor networks: A survey and comparison. Heliyon, 5(5), p.e01591.
Wang, P. and Tu, G., 2020. Localization algorithm of wireless sensor network based on matrix reconstruction. Computer Communications, 154, pp.216-222.
Zagrouba, R. and Kardi, A., 2021. Comparative study of energy-efficient routing techniques in wireless sensor networks. Information, 12(1), p.42.
Wang, B., Lim, H.B. and Ma, D., 2009. A survey of movement strategies for improving network coverage in wireless sensor networks. Computer Communications, 32(13-14), pp.1427-1436.
Yalçın, S. and Erdem, E., 2019. Bacteria interactive cost and balanced-compromised approach to clustering and transmission boundary-range cognitive routing in mobile heterogeneous wireless sensor networks. Sensors, 19(4), p.867.
Alarifi, A. and Tolba, A., 2019. Optimizing the network energy of cloud-assisted internet of things by using the adaptive neural learning approach in wireless sensor networks. Computers in Industry, 106, pp.133-141.
Daanoune, I., Abdennaceur, B. and Ballouk, A., 2021. A comprehensive survey on LEACH-based clustering routing protocols in Wireless Sensor Networks. Ad Hoc Networks, 114, p.102409.
Zhu, C., Zheng, C., Shu, L., and Han, G., 2012. A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35(2), pp.619-632.
Binh, H.T.T., Hanh, N.T., Van Quan, L. and Dey, N., 2018. Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural computing and applications, 30(7), pp.2305-2317.
Wen, F., Wang, H., He, T., Shi, Q., Sun, Z., Zhu, M., Zhang, Z., Cao, Z., Dai, Y., Zhang, T., and Lee, C., 2020. Battery-free short-range self-powered wireless sensor network (SS-WSN) using TENG-based direct sensory transmission (TDST) mechanism. Nano Energy, 67, p.104266.
Karimi Bidhendi, S., 2022. Energy-Efficient Node Deployment in Wireless Sensor Networks (Doctoral dissertation, UC Irvine).
Saba, T., Haseeb, K., Ud Din, I., Almogren, A., Altameem, A. and Fati, S.M., 2020. EGCIR: energy-aware graph clustering and intelligent routing using the supervised system in wireless sensor networks. Energies, 13(16), p.4072.
Nilashi, M., Ibrahim, O., Dalvi, M., Ahmadi, H., and Shahmoradi, L., 2017. Accuracy improvement for diabetes disease classification: a case on a public medical dataset. Fuzzy Information and Engineering, 9(3), pp.345-357.
Tian, J., Gao, M. and Ge, G., 2016. Wireless sensor network node optimal coverage based on improved genetic algorithm and binary ant colony algorithm. EURASIP Journal on Wireless Communications and Networking, 2016(1), pp.1-11.
Singh, R., Singh, J., and Singh, R., 2016. WRHT: a hybrid technique for detection of wormhole attack in wireless sensor networks. Mobile Information Systems, 2016.
Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S. and Kannan, A., 2019. Energy-aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, pp.211-223.
Khabiri, M. and Ghaffari, A., 2018. Energy-aware clustering-based routing in wireless sensor networks using a cuckoo optimization algorithm. Wireless Personal Communications, 98(3), pp.2473-2495.
Jagadeesh, S. and Muthulakshmi, I., 2022. Hybrid Metaheuristic Algorithm-Based Clustering with Multi-Hop Routing Protocol for Wireless Sensor Networks. In Proceedings of Data Analytics and Management (pp. 843-855). Springer, Singapore.