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Managing Resources for Futuristic Wireless Networks最新文献

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Clustering and Compressive Data Gathering for Transmission Efficient Wireless Sensor Networks 无线传感器网络高效传输的聚类和压缩数据采集
Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-9493-2.ch002
U. Pacharaney, R. B. Jain, Rajivkumar Gupta
The chapter focuses on minimizing the amount of wireless transmission in sensory data gathering for correlated data field monitoring in wireless sensor networks (WSN), which is a major source of power consumption. Compressive sensing (CS) is a new in-node compression technique that is economically used for data gathering in an energy-constrained WSN. Among existing CS-based routing, cluster-based methods offer the most transmission-efficient architecture. Most CS-based clustering methods randomly choose nodes to form clusters, neglecting the topology structure. A novel base station (BS)-assisted cluster, spatially correlated cluster using compressive sensing (SCC_CS), is proposed to reduce number of transmissions in and form the cluster by exploiting spatial correlation based on geographical proximity. The proposed BS-assisted clustering scheme follows hexagonal deployment strategy. In SCC_CS, cluster heads are solely involved in data gathering and transmitting CS measurements to BS, saving intra-cluster communication cost, and thus, network life increases as proved by simulation.
本章的重点是最小化无线传感器网络(WSN)中相关数据场监测的传感数据采集中的无线传输量,这是功耗的主要来源。压缩感知(CS)是一种新的节点内压缩技术,可以经济地用于能量受限的WSN数据采集。在现有的基于cs的路由中,基于集群的方法提供了传输效率最高的体系结构。大多数基于cs的聚类方法随机选择节点组成聚类,忽略了拓扑结构。提出了一种新的基站辅助集群——基于压缩感知的空间相关集群(SCC_CS),利用地理邻近性的空间相关性来减少传输数量并形成集群。提出的bs辅助聚类方案遵循六边形部署策略。在SCC_CS中,簇头只负责数据采集和将CS测量值传递给BS,节省了簇内通信成本,从而通过仿真证明了网络寿命的延长。
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Managing Resources for Futuristic Wireless Networks
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