A Modified K-Medoids Algorithm for Deploying a Required Number of Computing Systems in a Three Dimensional Space in Underwater Wireless Sensor Networks

Mohammad Alsulami, Raafat S. Elfouly, R. Ammar, Abdullah Alenizi
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引用次数: 2

Abstract

Identifying the number and location of processing machines in underwater Wireless Sensor Networks (UWSNs) is one of the hot topics nowadays. UWSNs are vital in monitoring and detecting objects or phenomenon in underwater environment [11]. UWSNs, however, have some limitations and challenges. The low bandwidth capacity is a key challenge [10] [5]. The next main challenge in UWSNs is having long propagation delay [8] [5]. These two challenges negatively impact the performance of UWSNs even if the number and location of processing machines are chosen optimally. Therefore, in paper, we propose a framework including a Modified K-Medoids algorithm that can help to identify the location of processing machines that we need to deploy. We study the effectiveness of having such algorithm on end to end delay and load balancing. Semi-uniform distribution outperforms in term of load balancing comparing to the other two distributions. We consider three different scenario to show merits of our work.
水下无线传感器网络三维空间中部署所需数量计算系统的改进K-Medoids算法
水下无线传感器网络(UWSNs)中处理机器的数量和位置识别是当前研究的热点之一。水下传感器网络对于水下环境中物体或现象的监测和探测至关重要。然而,UWSNs存在一些局限性和挑战。低带宽容量是一个关键的挑战。UWSNs的下一个主要挑战是长传播延迟[8]b[5]。即使处理机器的数量和位置选择最佳,这两个挑战也会对UWSNs的性能产生负面影响。因此,在论文中,我们提出了一个框架,其中包括一个改进的k - mediids算法,可以帮助确定我们需要部署的加工机器的位置。研究了该算法在端到端延迟和负载均衡方面的有效性。与其他两种分布相比,半均匀分布在负载平衡方面表现更好。我们考虑了三种不同的场景来展示我们工作的优点。
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