基于学习自动机的协同MIMO编队优化水下磁感应声传感器网络的能耗和覆盖

IF 7.9 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Qingyan Ren;Yanjing Sun;Sizhen Bian;Michele Magno
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引用次数: 0

摘要

水下无线传感器网络(uwsn)在具有挑战性的水下环境中提供了有前途的勘探能力,因此需要关注在保证监测覆盖的同时降低能耗。水下磁感应(MI)辅助声协同多输入多输出(MIMO)无线传感器网络由于传感器网络与通信技术的无缝集成,在许多方面都显示出传统UWSNs的优势。然而,作为一个新兴的课题,它们往往忽略了监测覆盖要求和未知水下环境的动态性等重要考虑因素,存在着严重的差距。此外,通过利用多个独立水下节点的协作潜力,可以进一步增强这些优势。本文介绍了利用创新的自信信息覆盖(CIC)和被称为学习自动机(LA)的强化学习范式在mi辅助声学合作MIMO WSNs领域的重大进展。本文提出了一种基于la的协同MIMO编队(LACMF)算法,该算法的设计目的是使传感器的通信能耗最小化,同时使覆盖性能最大化。实验结果表明,LACMF在能量消耗和网络覆盖方面明显优于其他方案,在满足所施加的约束条件下,CIC可以额外提高52%,降低11%的能量消耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Energy Consumption and Coverage in Underwater Magnetic Induction-Assisted Acoustic WSNs Using Learning Automata-Based Cooperative MIMO Formation
Underwater Wireless Sensor Networks (UWSNs) offer promising exploration capabilities in challenging underwater environments, necessitating a focus on reducing energy consumption while guaranteeing monitoring coverage. Underwater magnetic induction (MI)-assisted acoustic cooperative multiple-input–multiple-output (MIMO) WSNs have shown advantages over traditional UWSNs in various aspects due to the seamless integration of sensor networks and communication technology. However, as an emerging topic, a critical gap exists, as they often overlook the vital considerations of monitoring coverage requirements and the dynamic nature of the unknown underwater environment. Moreover, these advantages can be further enhanced by harnessing the collaborative potential of multiple independent underwater nodes. This paper introduces a significant advancement to the field of MI-assisted Acoustic Cooperative MIMO WSNs leveraging the innovative Confident Information Coverage (CIC) and a reinforcement learning paradigm known as Learning Automata (LA). The paper presents the LA-based Cooperative MIMO Formation (LACMF) algorithm designed to minimize communication energy consumption in sensors while concurrently maximizing coverage performance. Experimental results demonstrate the LACMF considerably outperforms other schemes in terms of energy consumption, and network coverage to satisfy the imposed constraints, the CIC can be improved up to by an additional 52%, 11% reduction in energy consumption.
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来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
期刊介绍: The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.
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