Archimedes Chicken Swarm Optimization‐Based Routing for Internet of Underwater Wireless Sensor Networks

ABSTRACT Underwater Wireless Sensor Networks (UWSNs) are employed in various applications ranging from observing the underwater environment to military applications, which leads to attaining interest for many investigators in the UWSNs area. They experience more problematic issues because they are deployed underwater. Some of the common difficulties involve the assessment of battery, installation, energy consumption, and long delay. To overcome this bridge, an effectual scheme is developed for the internet of UWSNs named Archimedes Chicken Swarm Optimization (ACSO). Here, ACSO is obtained by the fusion of Archimedes Optimization Algorithm (AOA) and Chicken Swarm Optimization (CSO). The underwater sensor network IoT module (IoUT) nodes are simulated and the energy model and mobility model are considered for the following progress. The energy prediction is achieved by employing a Deep Recurrent Neural Network (DRNN), which is trained by the proposed ACSO. The Cluster Head (CH) selection is performed utilizing the Energy Efficient Clustering Protocol. Lastly, the routing is carried out by the ACSO algorithm, where the best route is determined based on various parameters namely, energy, distance, delay, throughput, and Packet Delivery Ratio (PDR).