Optimal Localization of Multi-Computer Architecture for Large-Scale Underwater Wireless Sensor Networks

Hussain Albarakati, R. Ammar, Raafat S. Elfouly
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Abstract

Underwater wireless acoustic sensor networks (UWASNs) have emerged as a powerful communication technology for discovering and extracting data in aquatic environments. UWASNs have numerous applications in areas such as fisheries, resource exploration, mine reconnaissance, oil and gas inspection, marine exploration and military surveillance. However, these applications are limited by the capacity of networks to detect, discover, transmit, and forward big data. In particular, transmitting and receiving large volumes of data requires great lengths of time and substantial power, and thus fails to meet the real-time constraints. This problem has motivated us to focus on developing an underwater computer-embedded system capable of efficient big-data management. Thus, we have developed methods to discover and extract valuable information beneath the ocean using data-mining approaches. Previously, we introduced real-time underwater system architectures (RTUSAs) that use a single computer. In this study, we extend our results and propose a new RTUSA for large-scale networks. This novel RTUSA uses multi-computers and aims to enhance the reliability of our proposed system. Determining the optimal location of computers with respect to their membership of acoustic sensor nodes, so as to minimize delay time, power consumption, and balance loads, are NP-hard problems. Therefore, we propose a heuristic approach that enables optimization of computer locations and their memberships of acoustic sensor nodes. We conduct simulations to show the merits of our findings and measure the performance of our proposed solution.
大规模水下无线传感器网络多计算机体系结构的最优定位
水下无线声传感器网络(UWASNs)已成为一种强大的通信技术,用于在水生环境中发现和提取数据。uwasn在渔业,资源勘探,矿山侦察,石油和天然气检查,海洋勘探和军事监视等领域有许多应用。然而,这些应用受到网络检测、发现、传输和转发大数据能力的限制。特别是大容量数据的发送和接收需要耗费大量的时间和功率,无法满足实时性的限制。这个问题促使我们专注于开发一种能够有效管理大数据的水下计算机嵌入式系统。因此,我们开发了使用数据挖掘方法来发现和提取海洋下有价值的信息的方法。之前,我们介绍了使用单台计算机的实时水下系统架构(RTUSAs)。在这项研究中,我们扩展了我们的结果,并提出了一种新的大规模网络RTUSA。这种新颖的RTUSA使用多台计算机,旨在提高我们提出的系统的可靠性。根据声学传感器节点的隶属关系确定计算机的最佳位置,以最小化延迟时间、功耗和平衡负载,是np困难问题。因此,我们提出了一种启发式方法,可以优化计算机位置及其声传感器节点的隶属关系。我们进行模拟,以显示我们的发现的优点,并衡量我们提出的解决方案的性能。
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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