Opportunistic Routing Protocol for Underwater Wireless Sensor Network with Sink Mobility through Deep Kronecker Neural Network

Underwater Wireless Sensor Networks (UWSNs) have become increasingly significant due to their applications in underwater research, military operations, tsunami warning, and environmental monitoring. However, efficient communication among underwater sensors is challenging because of harsh and dynamic underwater conditions. In this paper, Opportunistic Routing Protocol for Underwater Wireless Sensor Network with Sink Mobility through Deep Kronecker Neural Network (ORPUWSS-SM-DKNN) is proposed. Here, the network is divided into twelve zones, each with 3–4 vertical and horizontal layers, comprising 500 randomly deployed sensor nodes and four mobile sinks, each covering three zones. Sensor nodes remain in sleep mode until a sink enters their zone, upon which a hello message activates node communication; nodes return to sleep after the sink exits. Here, the Opportunistic Routing Protocol employing Deep Kronecker Neural Network (DKNN) is utilized for effectively identifies optimal Next-Forwarder (NF) nodes to establish efficient routing paths. Then the proposed ORP-UWSS-SM-DKNN is implemented Matlab and the performance metrics including energy consumption, Packet Delivery Ratio (PDR), data integrity, average hop count, throughput, total energy of the network and number of alive nodes are analyzed. Finally the performance of proposed ORP-UWSS-SM-DKNN method provides 16.60%, 24.01% and 26.60%, lower energy consumption, 19.25%, 25.85% and 26.25% higher PDR when compared to the existing QATAR-UWSN-RL, UASN-FedTM and MUL-UASN-SqueezeNet methods respectively.