A Multi-layer Guidance Approach for Submerged Sensor Networks Integrating Acoustic and Optical Technologies

Rajkumar Krishnan, Arunkumar Muniyandi, Vinoth Kumar Kalimuthu, Mano Joel Prabhu Pelavendran
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Abstract

: Over the previous decade, there has been a significant focus on researching underwater acoustic sensor networks (UW-ASNs) for a diverse range of underwater applications, which in turn has facilitated human exploration of the expansive underwater environment. This research introduces an innovative architectural approach that signifies a noteworthy advancement. By combining both acoustic and optical components, it establishes an underwater wireless sensor network. Additionally, the research introduces an innovative multiple levels Q learning-grounded direction-finding procedure, denoted as the proposed system Multi-layer Guidance Approach (MLGA) which is meticulously tailored for such underwater networks. The network's architecture encompasses both physical grouping and logical division into two tiers: the upper tier is overseen by group leaders responsible for managing routing within the lower tier, where group members execute the actual data packet routing. This design capitalizes on the wider viewpoint of upper-tier group leaders and the concurrent learning processes occurring across all groups, resulting in a substantial enhancement in routing efficiency in comparison with traditional methodologies. The empirical results obtained from experimental tests underscore the robustness of the proposed system when confronted with changes in network topology. Moreover, it showcases the system's ability to achieve higher delivery rates and reduced delays in dynamic networks compared to the established approach of flat Q-learning routing. This innovative strategy holds the potential to significantly push the boundaries of underwater sensor networks, surpassing the constraints of conventional communication methods and providing a more effective and dependable means of transmitting data underwater. This advancement not only contributes to the technical aspects but also holds promise for fostering greater exploration and understanding of underwater environments.
整合声学和光学技术的水下传感器网络多层引导方法
:在过去的十年中,水下声学传感器网络(UW-ASN)在水下各种应用领域的研究一直备受关注,这反过来又促进了人类对广阔水下环境的探索。这项研究引入了一种创新的架构方法,标志着一个值得注意的进步。通过结合声学和光学元件,它建立了一个水下无线传感器网络。此外,该研究还引入了一种创新的多层次 Q 学习寻向程序,即为此类水下网络精心定制的拟议系统多层引导方法(MLGA)。该网络的架构包括物理分组和逻辑分层:上层由组长负责管理下层的路由,组员在下层执行实际的数据包路由。与传统方法相比,这种设计充分利用了上层组长的广阔视野和所有组的并发学习过程,从而大大提高了路由效率。实验测试得出的实证结果表明,当网络拓扑结构发生变化时,所提出的系统具有很强的鲁棒性。此外,与现有的平面 Q 学习路由方法相比,该系统还能在动态网络中实现更高的传输速率并减少延迟。这一创新策略有望极大地推动水下传感器网络的发展,超越传统通信方法的限制,提供更有效、更可靠的水下数据传输手段。这一进步不仅有助于技术方面的发展,而且有望促进对水下环境的探索和了解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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