Metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor Networks

Abstract

Wireless Sensor Network (WSN) is a vital element in Internet of Things (IoT) as the former enables the collection of huge quantities of data in energy-constrained environment. WSN offers independent access to the target region and performs data collection in an effective manner. But energy constraints remain a challenging issue in WSN since it operates on in-built battery. The studies conducted earlier recommended that the energy spent on communication process must be considerably reduced to improve the efficiency of WSN. Cluster organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic algorithms. Clustering and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of WSN. In this background, the current study develops an Improved Duck and Traveller Optimization (IDTO)-enabled cluster-based Multi-Hop Routing (IDTOMHR) technique for WSN. Primarily, IDTO algorithm is exploited for the selection of Cluster Head (CH) and construction of clusters. Besides, Artificial Gorilla Troops Optimization (ATGO) technique is also used to derive an optimal set of routes to the destination. Both clustering and routing approaches derive a fitness function with the inclusion of multiple input parameters. The proposed IDTOMHR model was experimentally validated for its performance under different aspects. The extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches.

Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm

“…The calculation results of most meta-heuristic algorithms are random due to the initial parameter settings, and there is a risk of falling into a local optimum when dealing with complex problems. On this basis, the combination of various meta-heuristic approaches is derived to solve multi-objective problems, such as butterfly algorithm-based clustering combined with ant colony optimization-based routing [22], improved duck and traveler optimization-based clustering combined with artificial gorilla troop optimization-based routing [23], hybrid gray wolf optimization, and the marine predator algorithm for clustering integrated with hybrid gray wolf optimization and the graph model for routing [24]. However, the complexity of the protocols is largely increased for execution.…”