MCH-EOR: Multi-objective Cluster Head Based Energy-aware Optimized Routing algorithm in Wireless Sensor Networks - Mehta, Deepak; Saxena, Sharad (Sustainable computing informatics and systems, 2020)
•Wireless Sensor nodes are constrained with limited energy, storage capacity and processing power.•Data transmission over long distance is the biggest source of energy consumption in Wireless Sensor Networks.•Presented energy efficient multi-objective criteria clustering and optimization-based routing approach provides a solution.•Cluster head is selected based on multiple objectives, primarily keeping focus on minimizing the energy consumption.•Sailfish optimizer algorithm is used for optimal path selection to transmit data to base station to improve energy efficiency. Hierarchical Wireless Sensor Networks (WSNs) have got vital application domains in modern era especially in monitoring and tracking of events, and without human intervention. In WSN, sensor nodes are characterized to have short life span due to continuous sensing and consequently the battery drains quickly. Under heavy traffic condition, sensors in close proximity to sink die quickly and initiate energy-hole problem. Thus, optimal usage of available energy is a key challenge in WSN assisted applications. A precise clustering and optimal path selection from sensor nodes to sink has become extremely important to preserve energy. Keeping this in view, the paper presents a Multi-Objective Based Clustering and Sailfish Optimizer (SFO) guided routing method to sustain energy efficiency in WSNs. In it the Cluster Head (CH) is selected, based on effective fitness function which is formulated from multiple objectives. It helps to minimize energy consumption and reduces number of dead sensor nodes. After CH selection, SFO is used to select an optimal path to sink node for data transmission. The proposed approach is analytically analyzed and results are compared with the similar existing approaches namely, Grey wolf optimization (GWO), Genetic algorithm (GA), Ant Lion optimization (ALO), and Particle Swarm Optimization (PSO) in terms of energy consumption, throughput, packet delivery ratio, and network lifetime. The simulation results show that proposed method has performed 21.9% and 24.4% better in terms of energy consumption and number of alive sensor nodes respectively when compared to GWO. Further, it shows significantly better results than other optimization-based approaches.