Adaptive Optimization for Optimal Mobile Sink Placement in Wireless Sensor Networks

In recent years, Wireless Sensor Networks (WSN) with mobile sinks has attracted much attention as the mobile sink roams over the sensing field and collects sensing data from sensor nodes. Mobile sinks are mounted on moving objects, such as people, vehicles, robots, and so on. However, optimal placement of the sink for the effective management of the WSN is the major challenge. Hence, an adaptive Fractional Rider Optimization Algorithm (adaptive-FROA) is developed for the optimal placement of mobile sink in WSN environment for effective routing. The adaptive FROA, which is the integration of the adaptive concept in the FROA, operates based on the fitness measure based on distance, delay, and energy measure of the nodes in the network. The main objective of the research work is to compute the energy and distance. The proposed method is analyzed based on the metrics, such as energy, throughput, distance, and lifetime of the network. The simulation results reveal that the proposed method acquired a minimal distance of 24.87m, maximal network energy of 94.54 J, maximal alive nodes of 77, maximal throughput of 94.42 bps, minimum delay of 0.00918s, and maximum Packet delivery ratio (PDR) of 87.98%, when compared with the existing methods.