ARO-RTP: Performance analysis of an energy efficient opportunistic routing for underwater IoT networks

The term "Underwater Internet of Things" (UIoT) refers to a system of intelligent, networked devices that may function in a variety of water conditions. During communication, the transmission quality is diminished by high levels of interference and collisions, which in turn causes a slow End-to-End (E2E), low latency, and a low Packet Delivery Ratio (PDR). And when the basis node does not choose an immediate forwarder node, an issue known as the "void hole" occurs and thus leads to the wastage of more power. Despite playing a crucial role in enabling underwater communication, IoUT frameworks still face difficulties because of their unstable radio signals, low resources, unreliable transmission medium, low bandwidth, limited range, low transmission rate, inborn noise, node mobility, slow propagation speed, and limited battery capacity. Therefore, it is greatly desirable to have secure communication with a prolonged network lifetime. In this research work, an opportunistic routing-based reliable transmission protocol (OR-RTP) is proposed to send the packs toward the superficial sink to lessen the high energy consumption (EC). The source node recognizes the furtherance relay set in the proposed protocol based on the forwarder's local information. The proposed protocol uses a meta-heuristic-based relay selection scheme (Artificial rabbits’ optimization-ARO) to pick the best relay by balancing the forwarder's EC and packet delivery ratio (PDR). During packet transmission, most of the energy is ruined because of collisions among Under Water Acoustic Sensor Networks (UW-ASNs') sensor nodes. This work developed an RTP for every forwarder node that sends data to the surface basin to reduce the problem of collision. The OR-RTP protocol has been extensively simulated, and the results are compared to those of other routing protocols in terms of EC, PDR, throughput, and network lifetime. The proposed model achieved 85% of PDR on 400 nodes and 92% of PDR for 700 nodes, whereas the existing model Grey Wolf Optimization achieved 51% of PDR on 400 nodes and 73% of PDR for 700 nodes.