Delay-Aware Electric Eel and Improved Aquila Algorithm-based Energy efficient Clustering with Mobile Sink for improved lifetime in WSNs

In Wireless Sensor Networks (WSNs), management of energy efficiency under routing is a high paramount since it is essential for mitigating the network partitioning risk and depletion of premature node. Initially clustering techniques need to be implemented as an optimal solution to addresses the challenges related to minimizing energy consumption of sensor nodes. Then mobile sink deployment and its associated trajectory need to be optimized within the restricted time constraints for reducing the use of energy-intensive multi-hop data transmissions. In this paper, Electric Eel and Improved Aquila Algorithm-based Clustering and Constrained Mobile Sink Path optimization Protocol (EIACMSP) is proposed for guaranteeing both optimal clustering process and minimizing energy consumption while transmitting sensed data to the sink for reactive decision making. It specifically uses Electric Eel Foraging Optimization Algorithm (EEFOA) for choosing potent Cluster Heads (CHs) using the factors of node degree, node centrality, distance to Base Station (BS) and neighbours and Residual Energy (RE). It applies Improved Aquila Optimization Algorithm (IAQOA) for constructing closed paths that the mobile sink need to follow for enhancing the network lifetime under the constraints of delay and energy. This IAQOA identifies the optimal rendezvous points such that all of them can be included for determining shortest closed path such that the mobile sink can receive aggregated data from CHs when they are at proximity. The simulation outcomes of EIACMSP approach confirmed an enhanced network lifetime of 24.56%, minimized path length of 27.65%, reduced latency of 21.28%, across different node densities for guaranteeing energy stability and fairness compared to the baseline Delay-Constrained and Centroid Clustering Path-based Sink Mobility Approach (DCCCPSMA), Monarch Butterfly and Euclidean Distance-based Crow Search (MBOAEDCS), Fuzzy Dove Swarm Optimisation-Mobile Sink Clustering Routing Strategy (FDSOMSCRS) and approaches.