Hybrid Termite Queen and Walrus Optimization Algorithm-based energy efficient cluster-based routing with static and mobile sink node in WSNs

Clustering and routing approaches helps in addressing the design challenges which are primarily responsible for achieving energy efficiency and network lifetime in Wireless Sensor Networks (WSNs). Swarm-Intelligence (SI) based algorithms helps in determining optimal or near optimal solutions to the problems of Non-deterministic Polynomial (NP)-hard optimization related to clustering and routing. In this paper, Hybrid Termite Queen and Walrus Optimization Algorithm (HTQWOA)-based energy efficient cluster-based routing protocol with static sink node and mobile sink node for addressing the issue of hot spot and subsequent extension of network lifetime. Termite Queen Optimization algorithm (TQOA) is specifically used for determining optimal number of cluster heads (CHs). On the other hand, Walrus Optimization Algorithm (WOA) is adopted for achieving energy efficient routing between the selected CHs and the sink node for the objective of maximizing network lifetime and at the same time reducing the energy consumption. This proposed HTQWOA utilized sink mobility for preventing the multi-hop communication between the CHs and the sink nodes for sustaining necessitated energy in the network. It also included the derivation of multi-objective factors which are related to residual energy, inter-cluster distance, intra-cluster distance, packet drop rate, path loss, node degree, node centrality, link quality and restart number into account for achieving better CH selection. The experiments of HTQWOA conducted using ns-2 simulator revealed improved throughput of 16.21%, increased sustenance of alive nodes by 23.46%, and reduced residual energy of 25.12%, better than the baseline static and ACO-based mobile sink mobility approach (BACOSNM). Water strider algorithm (WSA) and ACO-based CH selection (WSACOCHS) and sink mobility approach, Cat Swarm Optimization algorithm-based CH selection and sink mobility technique (CSOCHSM) and Improved Squirrel Search Algorithm-based Optimized Fuzzy Clustering (ISSAOFC) approaches used for investigation.