Improve Energy Harvesting Rate of Sensor Nodes Using Stochastic Network Calculus for Aquatic Applications

Le résumé fourni par la source

Underwater Wireless Sensor Networks (UWSNs) consist of sensor nodes deployed in aquatic environments to monitor critical parameters such as pressure, temperature, pH and salinity, playing an essential role in underwater exploration, environmental monitoring, and defense applications.A key challenge in UWSNs is sustaining long-term operation due to limited energy sources and the impracticality of replacing or recharging batteries underwater.To address this, the study proposes a novel mathematical modelling framework based on Stochastic Network Calculus (SNC), which incorporates stochastic environmental factors like node depth, pH variation, and acoustic channel variability to accurately model energy dynamics and predict energy harvesting over time.The proposed SNC-based DTAR routing protocol is evaluated through both analytical methods and simulations across performance metrics including Energy Harvesting Rate (EHR), Network Lifetime, End-to-End Delay, Network Throughput, Packet Delivery Ratio (PDR), and Packet Loss Ratio (PLR).The results show that the proposed method achieves an overall performance improvement of 19.45% compared to existing protocols, significantly outperforming deterministic approaches that yielded an improvement of only 11.63%.Additionally, the simulation results closely match the analytical predictions with an average deviation of -5.04%, highlighting the model's accuracy and the impact of real-world environmental factors.These outcomes validate the effectiveness of the SNC-based framework in enabling energy-efficient, resilient, and sustainable UWSN operations under dynamic underwater conditions.

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