Volume 3 , Issue 1 , PP: 08-16, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Rabie A. Ramadan 1 *
Doi: https://doi.org/10.54216/IJWAC.030101
Localization is widely employed in wireless sensor networks (WSN) to detect the present position of the nodes. Generally, WSN comprises numerous sensors, which makes the deployment of GPS in all nodes cost and fails to provide precise localization outcomes in several cases. The manual configuration of the position reference of the sensors is not feasible under dense networks. Therefore, the NL process can be treated as an NP-hard problem and solved by metaheuristic algorithms. In this aspect, this paper presents an improved group teaching optimization algorithm-based NL technique called IGTOA-NL for WSN. The IGTOA technique is derived by integrating the basic concepts of GTOA with the β-hill-climbing technique to improve the overall node localization process. The IGTOA-NL technique can effectually localize the nodes in WSN under varying anchor node count. To showcase the productive outcome of the IGTOA technique, a series of simulations take place under a diverse number of anchors. The resultant values highlighted the proficient NL outcome of the IGTOA technique over the current state of art NL techniques in terms of different measures.
Node localization, WSN, Euclidean distance, Metaheuristics, GTOA, Anchor nodes
[1] Pandey, O.J., Gautam, V., Jha, S., Shukla, M.K. and Hegde, R.M., 2020. Time Synchronized Node Localization Using Optimal H-Node Allocation in a Small World WSN. IEEE Communications Letters, 24(11), pp.2579-2583.
[2] Arjunan, S. and Sujatha, P., 2018. Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol. Applied Intelligence, 48(8), pp.2229-2246.
[3] Tan, R., Li, Y., Shao, Y. and Si, W., 2020. Distance mapping algorithm for sensor node localization in wsns. International Journal of Wireless Information Networks, 27(2), pp.261-270.
[4] Zhang, K., Zhang, G., Yu, X. and Hu, S., 2021. Boundary-Based Anchor Selection Method for WSNs Node Localization. Arabian Journal for Science and Engineering, 46(4), pp.3779-3792.
[5] Wang, L., Er, M.J. and Zhang, S., 2020. A kernel extreme learning machines algorithm for node localization in wireless sensor networks. IEEE Communications Letters, 24(7), pp.1433-1436.
[6] Arjunan, S. and Pothula, S., 2019. A survey on unequal clustering protocols in Wireless Sensor Networks. Journal of King Saud University-Computer and Information Sciences, 31(3), pp.304-317.
[7] Phoemphon, S., So-In, C. and Leelathakul, N., 2020. A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks. Expert Systems with Applications, 143, p.113044.
[8] El Khediri, S., Fakhet, W., Moulahi, T., Khan, R., Thaljaoui, A. and Kachouri, A., 2020. Improved node localization using K-means clustering for Wireless Sensor Networks. Computer Science Review, 37, p.100284.
[9] Famila, S., Jawahar, A., Sariga, A. and Shankar, K., 2020. Improved artificial bee colony optimization based clustering algorithm for SMART sensor environments. Peer-to-Peer Networking and Applications, 13(4), pp.1071-1079.
[10] Thilagavathi, P. and Manickam, J.M.L., 2021. ERTC: an Enhanced RSSI based Tree Climbing mechanism for well-planned path localization in WSN using the virtual force of Mobile Anchor Node. Journal of Ambient Intelligence and Humanized Computing, 12(6), pp.6665-6676.
[11] Zhu, H. and Luo, M., 2020. Hybrid robust sequential fusion estimation for WSN-assisted moving-target localization with sensor-node-position uncertainty. IEEE Transactions on Instrumentation and Measurement, 69(9), pp.6499-6508.
[12] Annepu, V. and Rajesh, A., 2020. An unmanned aerial vehicle-aided node localization using an efficient multilayer perceptron neural network in wireless sensor networks. Neural Computing and Applications, 32(15), pp.11651-11663.
[13] Yadav, R.K., Verma, S. and Venkatesan, S., 2021. iHRNL: Iterative Hessian-based manifold regularization mechanism for localization in WSN. The Journal of Supercomputing, pp.1-24.
[14] Miloud, M., Abdellatif, R. and Lorenz, P., 2019. Moth flame optimization algorithm range-based for node localization challenge in decentralized wireless sensor network. International Journal of Distributed Systems and Technologies (IJDST), 10(1), pp.82-109.
[15] Kanoosh, H.M., Houssein, E.H. and Selim, M.M., 2019. Salp swarm algorithm for node localization in wireless sensor networks. Journal of Computer Networks and Communications, 2019.
[16] Hao, Z., Dang, J., Yan, Y. and Wang, X., 2021. A node localization algorithm based on Voronoi diagram and support vector machine for wireless sensor networks. International Journal of Distributed Sensor Networks, 17(2), p.1550147721993410.
[17] Lv, Y., Liu, W., Wang, Z. and Zhang, Z., 2020. WSN localization technology based on hybrid GA-PSONL-BP algorithm for indoor three-dimensional space. Wireless Personal Communications, 114(1), pp.167-184.
[18] Sekhar, P., Lydia, E.L., Elhoseny, M., Al-Akaidi, M., Selim, M.M. and Shankar, K., 2021. An effective metaheuristic based node localization technique for wireless sensor networks enabled indoor communication. Physical Communication, 48, p.101411.
[19] Zhang, K., Zhang, G., Yu, X. and Hu, S., 2021. Boundary-Based Anchor Selection Method for WSNs Node Localization. Arabian Journal for Science and Engineering, 46(4), pp.3779-3792.
[20] Zhang, Y. and Jin, Z., 2020. Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Systems with Applications, 148, p.113246.
[21] Zhu, S., Wu, Q., Jiang, Y. and Xing, W., 2021. A novel multi-objective group teaching optimization algorithm and its application to engineering design. Computers & Industrial Engineering, 155, p.107198.
[22] Feng, H., Ni, H., Zhao, R. and Zhu, X., 2020. An enhanced grasshopper optimization algorithm to the bin packing problem. Journal of Control Science and Engineering, 2020.
