基于单移动Sink的无线传感器网络能量效率和快速数据采集协议

Shivkumar S. Jawaligi, G. Biradar
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引用次数: 10

摘要

近年来,通信系统需求呈指数级增长,促使全球学术界和工业界开发高效的通信技术,以满足能源效率和服务质量(QoS)的需求。无线传感器网络(WSN)作为最高效的通信技术之一,在民用、国防和工业等主要通信领域具有巨大的潜力。传感器移动性与无线传感器网络的结合,拓宽了其应用领域。无线传感器网络的有效性可以通过其高效的数据采集和传输到基站进行决策的能力来表征。基于聚类的路由方案一直是无线传感器网络系统的主流技术之一;然而,簇的形成、簇数和簇头的选择以及从传感器到移动接收器的数据传输决策等关键问题一直是一个开放的研究领域。提出了一种鲁棒节能的基于单移动sink的WSN数据采集协议。与现有方法不同,基于期望最大化(EEM)的概念,提出了一种增强的集中式聚类模型。此外,它通过使用最优簇数估计技术来加强,该技术确保网络区域中的簇数不会引入不必要的能量耗尽。同时,利用传感器节点与簇头之间的相对距离以及移动接收器之间的相对距离进行传输(路径)决策。结果表明,基于EEM的最优聚类选择和最优动态传输决策的聚类具有更高的吞吐量、更快的数据采集速度、最小的延迟和能耗以及更高的效率
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Single Mobile Sink Based Energy Efficiency and Fast Data Gathering Protocol for Wireless Sensor Networks
Recently, the exponential rise in communication system demands has motivated global academia-industry to develop efficient communication technologies to fulfill energy efficiency and Quality of Service (QoS) demands. Wireless Sensor Network (WSN) being one of the most efficient technologies possesses immense potential to serve major communication purposes including civil, defense and industrial purposes etc. The inclusion of sensor-mobility with WSN has broadened application horizon. The effectiveness of WSNs can be characterized by its ability to perform efficient data gathering and transmission to the base station for decision process. Clustering based routing scheme has been one of the dominating techniques for WSN systems; however key issues like, cluster formation, selection of the number of clusters and cluster heads, and data transmission decision from sensors to the mobile sink have always been an open research area. In this paper, a robust and energy efficient single mobile sink based WSN data gathering protocol is proposed. Unlike existing approaches, an enhanced centralized clustering model is developed on the basis of expectation-maximization (EEM) concept. Further, it is strengthened by using an optimal cluster count estimation technique that ensures that the number of clusters in the network region doesn’t introduce unwanted energy exhaustion. Meanwhile, the relative distance between sensor node and cluster head as well as mobile sink is used to make transmission (path) decision. Results exhibit that the proposed EEM based clustering with optimal cluster selection and optimal dynamic transmission decision enables higher throughput, fast data gathering, minima delay and energy consumption, and higher efficiency
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