MS-ExTdO: mobile sink path planning based on extended Tasmanian devil optimization for wireless sensor networks
K. Vignesh Saravanan, S. Kavipriya, Hima Gnanadhas Sobhinosannal, M. Dhanalakshmi · Journal of the Chinese Institute of Engineers · 2025
Path planning in mobile sink is a critical aspect of Wireless Sensor Networks (WSN) to optimize data collection efficiency, energy consumption, and lifetime of network. This work introduces an energy-efficient protocol for Path planning in mobile sink based on extended Tasmanian devil optimization (MS-ExTdO). The proposed approach employs optimized node clustering and Voronoi-based node deployment to resolve issues related to coverage and node failures. The system model integrates the ExTdO algorithm for optimal clustering and mobile sink path planning. The algorithm considers fitness factors such as energy consumption, and distance during clustering and selects Cluster Heads based on remaining energy and degree of centrality. The proposed method acquired the throughput measured by MS-ExTdO is 99.974%, which is 6.16%, 4.73%, and 1.60% superior compared to MPSORP, MMSCM, and MACR methods. In addition, the proposed method demonstrates the superiority of MS-ExTdO in terms of minimal delay, highest residual energy, packet delivery ratio, and throughput.