Multiobjective Fractional Transit Waterwheel Optimization for Energy Efficient CH Selection and Routing

Abstract

In this research, an effective approach named Fractional Transit Water Wheel Optimization (FTWWO) is introduced for cluster head (CH) selection and routing in wireless sensor networks (WSNs) with mobile sink. Initially, WSN simulation with mobile sink is carried out based on the energy model, mobility model, and link life time (LLT) model. Then, the cluster formation is done by deep embedding clustering (DEC). After that, the CH selection is carried out by using a novel Hybrid Transit Water Wheel Optimization (TWWO) method, based on factors like predicted energy, intracluster and intercluster distances, LLT, trust, and delay. Moreover, the TWWO algorithm is designed by integrating the Transit Search Algorithm (TSA) and the Water Wheel Plant Algorithm (WWPA). Following this, routing is carried out by using a proposed fractional TWWO, by considering the factors that include predicted energy, distance, delay, and trust, in which the energy prediction is performed by the radial basis function networks (RBFNs). The fractional TWWO is developed by combining fractional calculus (FC) with the proposed TWWO. Finally, to enhance network lifespan, energy-efficient data aggregation techniques are applied. Additionally, the proposed method gained a minimum distance of 39.332 m, energy of 0.001 J, latency of 0.547 s, maximum throughput of 35.106 Mbps, and packet delivery rates (PDR) of 81.637%.