Energy Optimization using Swarm Intelligence for IoT-Authorized Underwater Wireless Sensor Networks

Abstract The technology advancement in the Internet of Things (IoT) enables a variety of smart monitoring applications assisted by networks like Wireless Sensor Networks (WSNs) and Underwater WSNs (UWSNs). The IoT-UWSNs supported a wide range of applications such as underwater data collection, underwater equipment monitoring, underwater imaging, etc. The acoustic signals have been utilized for communication in IoT-UWSNs over radio signals and optical signals. Data transmission using acoustic signals is suffering from lower throughput, excessive energy consumption, long transmission delay, and lower network lifetime. Several data forwarding and clustering algorithms have recently been proposed to enhance UWSN's performances. This paper proposed a novel routing solution for energy and QoS-efficient data transmission from the underwater sensor node to the surface sink using Swarm Intelligence (SI). This protocol called Energy Optimization using Routing Optimization (EORO) protocol. To optimize the UWSNs performance, we used Effective Fitness Function-based Particle Swarm Optimization (EFF-PSO) to select the best forwarder node for data transmission. In EORO, forwarding relay nodes discovered by the intended source node using location information firstly. Then EFF-PSO algorithm is applied to select the optimal relay node considering the rich set of parameters. Four parameters of each forwarder node used for fitness computation as residual energy, packet transmission ability, node connectivity, and distance. These parameters are intelligently selected to avoid packet collisions to achieve energy consumption and delay reduction with higher throughput. An experimental result shows that the EORO protocol outperformed underlying routing techniques using throughput, energy consumption, delay, and Packet Delivery Ratio (PDR).