An Enhanced Sparrow Search Based Adaptive and Robust Data Gathering Scheme for WSNs

• Published in: IEEE sensors journal Vol. 22; no. 11; pp. 10602 - 10612
• Main Authors: Khedr, Ahmed M., Aghbari, Zaher Al, Raj, P. V. Pravija
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive search techniques; Adaptive systems; Ant colony optimization; Ant Colony Optimization (ACO); Data collection; Delays; Energy consumption; Energy utilization; mobile sink (MS); Network latency; Network topologies; Network topology; Nodes; Optimization; Robustness; Searching; Sensors; sparrow search algorithm (SSA); Travel time; Wireless networks; Wireless sensor network (WSN); Wireless sensor networks
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3167515

Wireless Sensor Networks based on Mobile Sinks (MS) are becoming more popular because of their benefits in enhancing performance and minimizing unequal energy usage of the nodes. 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. Also, it is difficult to plan an efficient path for data gathering by MS using a set of Rendezvous Points (RPs) that could reduce the MS travel time while reducing the WSN energy usage. In this paper, we propose an Enhanced Sparrow Search based Adaptive and Robust Data Gathering Scheme for WSNs (ESRP-MP) to create shorter travel route for MS and minimize data gathering latency. For effective utilization of RPs, the best RP locations that maximize the coverage of nodes and minimize the overlap in coverage with adjacent RPs are considered. Also, the algorithm is adaptive to node failures by updating the RPs and refining the planned MS path when necessary. In the first phase, we introduce an enhanced Sparrow Search based algorithm to select a set of RPs that maximizes the coverage of nodes and minimizes the overlap in RP coverage. Then, the shortest tour through the RPs is obtained using Ant Colony Optimization (ACO). The results reveal the effectiveness of ESRP-MP against other related approaches in terms of number of RPs, data gathering time, MS path, energy utilization and network lifetime.