Energy optimization using swarm intelligence for IoT-Authorized underwater wireless sensor networks

Abstract

Several data forwarding and clustering techniques have been proposed to improve Underwater Wireless Sensor Networks (UWSNs) performance, but void communications, packet collisions, and energy efficiency remain unresolved. We introduce a novel routing solution for energy and QoS-efficient data transmission from the underwater sensor node to the surface sink using Swarm Intelligence (SI). The protocol Energy Optimization using the Routing Optimization (EORO) is proposed to overcome the existing challenges. We design Effective Fitness Function-based Particle Swarm Optimization (EFF-PSO) to choose the optimal forwarder node for UWSN data delivery. In EORO, forwarding relay nodes are discovered by the intended source node using location information first. Then EFF-PSO algorithm is applied to select the optimal relay node considering the rich set of parameters. Four parameters of each forwarder node are used for fitness computation as residual energy, packet transmission ability, node connectivity, and distance. These parameters are deliberately chosen to reduce energy consumption, latency, and throughput by avoiding packet collisions. EORO surpassed underlying routing solutions in throughput, energy consumption, latency, and Packet Delivery Ratio (PDR).