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<p>For Ocean exploration, underwater wireless systems must communicate at a faster data rate. Currently, low speed of data, high prices, high latency, and high energy usage are characteristics of acoustic sensor networks, and detrimental effects on marine mammals are used to attain extensive coverage. Underwater networks, on the other hand, benefit from optical communication’s faster data rate, although over shorter communication distances. Furthermore, because battery power is restricted and it might be challenging to replace or recharge a sensor node’s battery, another major issue with underwater sensor networks is energy usage. Adding the energy-collecting ability of acoustic-optical sensor nodes is the final solution to this issue. Despite advances in UWSN routing and energy harvesting, no existing framework jointly models hybrid acoustic-optical energy harvesting using stochastic calculus with adaptive routing in a unified manner. This paper addresses this gap by proposing a hybrid acoustic-optical UWSN framework that integrates Stochastic Network Calculus (SNC) with the Adaptive Underwater Routing Protocol (AUWRP). The research establishes an analytical framework for intelligent power management using Adaptive Underwater Routing Protocol (AUWRP) and Stochastic Network Calculus (SNC). The SNC-based model is able to evaluate energy availability with high accuracy, maximizes duty cycling, and improves transmission dependability. To improve scalability and lessen network disruptions, machine learning-driven flexible approaches and energy-aware communication protocols are also being investigated. Both simulation outcomes and analytical evaluations demonstrate that the framework effectively decreases power usage, packet loss, and maintaining stable network performance over extended periods in changing underwater conditions. The proposed AUWRP outperforms EE-LRP by up to 25% in power consumption, 25% in PDR, 67% reduction in end-to-end delay (EE-LRP’s delay is approximately 3x higher), and 25% in transmission attenuation. It also outperforms EE-DORA by 25%, 20%, 25%, and 25%, respectively. Analytical bounds derived via SNC are validated through NS-3 simulations averaged over multiple runs, with results demonstrating statistically reliable performance under varying underwater conditions.</p>

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Optimizing energy harvesting in underwater wireless sensor networks using integrated stochastic network calculus and adaptive underwater routing protocol

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • C. Agnel Shyam Kumar,
  • M. R. Christhu Raj

摘要

For Ocean exploration, underwater wireless systems must communicate at a faster data rate. Currently, low speed of data, high prices, high latency, and high energy usage are characteristics of acoustic sensor networks, and detrimental effects on marine mammals are used to attain extensive coverage. Underwater networks, on the other hand, benefit from optical communication’s faster data rate, although over shorter communication distances. Furthermore, because battery power is restricted and it might be challenging to replace or recharge a sensor node’s battery, another major issue with underwater sensor networks is energy usage. Adding the energy-collecting ability of acoustic-optical sensor nodes is the final solution to this issue. Despite advances in UWSN routing and energy harvesting, no existing framework jointly models hybrid acoustic-optical energy harvesting using stochastic calculus with adaptive routing in a unified manner. This paper addresses this gap by proposing a hybrid acoustic-optical UWSN framework that integrates Stochastic Network Calculus (SNC) with the Adaptive Underwater Routing Protocol (AUWRP). The research establishes an analytical framework for intelligent power management using Adaptive Underwater Routing Protocol (AUWRP) and Stochastic Network Calculus (SNC). The SNC-based model is able to evaluate energy availability with high accuracy, maximizes duty cycling, and improves transmission dependability. To improve scalability and lessen network disruptions, machine learning-driven flexible approaches and energy-aware communication protocols are also being investigated. Both simulation outcomes and analytical evaluations demonstrate that the framework effectively decreases power usage, packet loss, and maintaining stable network performance over extended periods in changing underwater conditions. The proposed AUWRP outperforms EE-LRP by up to 25% in power consumption, 25% in PDR, 67% reduction in end-to-end delay (EE-LRP’s delay is approximately 3x higher), and 25% in transmission attenuation. It also outperforms EE-DORA by 25%, 20%, 25%, and 25%, respectively. Analytical bounds derived via SNC are validated through NS-3 simulations averaged over multiple runs, with results demonstrating statistically reliable performance under varying underwater conditions.