MSSPP: modified sparrow search algorithm based mobile sink path planning for WSNs

Abstract

Past studies reveal the benefits of using Mobile Sink in Wireless Sensor Networks to bring about increased data collection efficiency and overall network performance in numerous applications. While several MS data gathering methods have been proposed, most of them are less adaptive to changes in network topology and fails to modify the MS path suitably in response to node failures. In this paper, we propose a Modified Sparrow Search Algorithm-based Mobile Sink Path Planning for WSNs (MSSPP) to create shorter travel route for MS and minimize data gathering latency. The proposed method helps in improving the performance of basic SSA by enhancing the quality of initial sparrow population, population diversity and search ability through modified strategies and is adaptive to node failure scenarios. In the first phase, we introduce a modified Sparrow Search-based algorithm to select a set of RPs that maximizes the coverage of nodes and minimizes the overlap in RP coverage. Then, an ACO-based path planning algorithm is utilized to determine the shortest tour through the RPs. The results reveal the effectiveness of MSSPP against other related approaches in terms of number of RPs, data gathering time, MS path, energy utilization and network lifetime.