An Analysis on the Delay-Aware Data Collection Network Structure Using Pareto Optimality

Chi-Tsun Cheng, C. K. Tse
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引用次数: 6

Abstract

Clustering techniques are effective techniques in reducing energy consumption in wireless sensor networks (WSNs). A data collection process (DCP) is an operation for a base station (BS) to collect a complete set of data from a WSN. Clustering techniques may, however, introduce bottlenecks to a DCP and cause extra delays. For time-sensitive applications, a delay-aware network structure is necessary. A delay-aware network structure should not only minimize the duration of a single DCP, but also shorten the duration of consecutive DCPs. Furthermore, for better sensing quality, a delay-aware network structure should try to accommodate as many wireless sensor nodes as possible. This paper investigates the trade-offs among the above objective functions in a delay-aware data collection network structure using the concepts of Pareto optimality. The analyses provide an insight into selecting the most suitable network parameters.
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基于Pareto最优的延迟感知数据采集网络结构分析
聚类技术是降低无线传感器网络能耗的有效技术。数据收集过程(DCP)是基站(BS)从无线传感器网络(WSN)收集一整套数据的操作。然而,集群技术可能会给DCP带来瓶颈,并导致额外的延迟。对于时间敏感的应用,延迟感知网络结构是必要的。延迟感知网络结构不仅要使单个DCP的持续时间最小化,而且要缩短连续DCP的持续时间。此外,为了获得更好的传感质量,延迟感知网络结构应该尝试容纳尽可能多的无线传感器节点。本文利用帕累托最优的概念研究了延迟感知数据采集网络结构中上述目标函数之间的权衡。这些分析为选择最合适的网络参数提供了见解。
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