Volume 4, Issue 2, PP: 84-96, 2022 | Cite this article as | XML | | Html PDF
Manal M. Nasir 1 * , Salim M. Hebrisha 2
Doi: https://doi.org/10.54216/IJWAC.040204
Wireless communication inculcates transfer of information not having any physical connection among two or more points. The significant operation of a sensor network becomes collecting and forwarding data to destiny. It is highly crucial to have an awareness regarding the place of collected data. This data is acquired by leveraging localization method in wireless sensor networks (WSNs). Localization is a way of determining the sensor nodes (SNs) location. Localization of SNs turns out to be an exciting research area, and several studies were performed till now. It is very favourable to model scalable, effectual, and low-cost localization systems for WSNs. This study develops a Leopard optimization based Node Localization Technique for Wireless Communication (LONLT-WC). The goal of the LONLT-WC model is to recognize the location of the nodes involved in the network. The LONLT-WC model involves the design of snow leopard optimization (SLO) algorithm, inspired from the characteristics of snow leopards. The presented LONLT-WC approach computes the unidentified location of the nodes utilizing anchor nodes in the network with the accomplishment of least error rate. The experimental analysis of the LONLT-WC model involves a series of simulations and the results highlighted the betterment of the presented technique.
Wireless communication , Node localization , Snow leopard optimization , Wireless sensor networks , Anchor nodes
[1] Sivasakthiselvan, S. and Nagarajan, V., 2020, July. Localization techniques of wireless sensor
networks: A review. In 2020 International Conference on Communication and Signal Processing
(ICCSP) (pp. 1643-1648). IEEE.
[2] He, W., Cheng, R., Mao, K., Yan, K., Wei, J. and Xu, Y., 2022. A Novel Anchorless Node Positioning
Method for Wireless Sensor Network. Journal of Sensors, 2022.
[3] Sivasakthiselvan, S. and Nagarajan, V., 2019. A new localization technique for node positioning in
wireless sensor networks. Cluster Computing, 22(2), pp.4027-4034.
[4] 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.
[5] Cheng, L., Hang, J., Wang, Y. and Bi, Y., 2019. A fuzzy C-means and hierarchical voting based RSSI
quantify localization method for wireless sensor network. IEEE Access, 7, pp.47411-47422.
[6] Vikram, R., Sinha, D., De, D. and Das, A.K., 2020. EEFFL: energy efficient data forwarding for forest
fire detection using localization technique in wireless sensor network. Wireless Networks, 26(7),
pp.5177-5205.
[7] Kulkarni, V.R., Desai, V. and Kulkarni, R.V., 2019. A comparative investigation of deterministic and
metaheuristic algorithms for node localization in wireless sensor networks. Wireless Networks, 25(5),
pp.2789-2803.
[8] Annepu, V. and Rajesh, A., 2020. Implementation of an efficient artificial bee colony algorithm for
node localization in unmanned aerial vehicle assisted wireless sensor networks. Wireless Personal
Communications, 114(3), pp.2663-2680.
[9] Jin, Y., Zhou, L., Zhang, L., Hu, Z. and Han, J., 2022. A Novel Range-Free Node Localization Method
for Wireless Sensor Networks. IEEE Wireless Communications Letters, 11(4), pp.688-692.
[10] Walia, G.S., Singh, P., Singh, M., Abouhawwash, M., Park, H.J., Kang, B.G., Mahajan, S. and Pandit,
A.K., 2022. Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor
Networks. CMC-Computers, Materials & Continua, 70(1), pp.305-321.
[11] 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.
[12] He, W., Lu, F., Chen, J., Ruan, Y., Lu, T. and Zhang, Y., 2021. A kernel-based node localization in
anisotropic wireless sensor network. Scientific Programming, 2021.
[13] 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.
[14] 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.
[15] Amri, S., Khelifi, F., Bradai, A., Rachedi, A., Kaddachi, M.L. and Atri, M., 2019. A new fuzzy logic
based node localization mechanism for wireless sensor networks. Future Generation Computer
Systems, 93, pp.799-813.
[16] Coufal, P., Hubálovský, Š., Hubálovská, M. and Balogh, Z., 2021. Snow leopard optimization
algorithm: A new nature-based optimization algorithm for solving optimization
problems. Mathematics, 9(21), p.2832.
[17] Vojdani, M. and Dehghan, M., 2011. Localization in anchor less wireless sensor network.
In Proceedings of the International Conference on Computer Engineering and Applications (Vol. 2, pp.
365-369).
