Energy Harvesting using Stochastic Network Calculus for Monitoring Underwater Tunnelling Applications

Abstract The implementation of Underwater Wireless Sensor Networks (UWSNs) for applications in oceanographic environment monitoring, marine biology and ecology, and underwater infrastructure monitoring requires frequent battery replacements due to the limited energy capacity of sensor nodes. Underwater Energy Harvesting (UEH) offers a promising solution to extend sensor node lifetimes and reduce maintenance costs. Among various techniques, piezoelectricity emerges as a particularly efficient method for UEH. This research proposes a novel approach to improve energy harvesting for underwater infrastructure monitoring, specifically for underwater tunnelling applications. By implementing piezoelectric wave energy and a Stochastic Network Calculus (SNC) mathematical model, the proposed method aims to simulate Efficient Energy Harvesting (EEH) to enhance sensor node performance parameters. This includes maximising the Energy Harvesting Rate (EHR), Delivery Probability Ratio (DPR), and overall Network Throughput (NT). This research contributes to the advancement of sustainable UWSNs by enabling efficient energy management and prolonging sensor node operation.