Contribution aware task allocation in sensor networks

Tingting Fu, Peng Liu
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引用次数: 7

Abstract

A wireless sensor network usually has a large amount of nodes deployed in a area to report ambient reading, to detect abnormal events, or to monitor the region. Each sensor performs several tasks, such as computing, sensing and communicating. A node equipped with multiple sensors is able to participate in many sensing tasks to improve measurement accuracy. However, the contribution a new node can make to the corresponding sensing accuracy depends on the number of existing nodes while the energy cost will increase in a consistent way. Therefore, there is a tradeoff between the task accuracy and the energy cost. In this paper, we consider the dynamic fading fact of contribution of redundant sensor nodes and bring forward a Contribution Aware Task Allocation method to maximize the total accuracy efficiency of sensor network. The method is derived from max-weight resource allocation algorithm (i.e., KM algorithm) and can guarantee the optimal of the solution. We compare our method with two other greedy and optimizing method. The experimental result shows our method outperform the competitors and are more efficiency.
传感器网络中贡献感知任务分配
无线传感器网络通常在一个区域内部署大量节点,以报告环境读数、检测异常事件或监控该区域。每个传感器执行几个任务,如计算、传感和通信。配备多个传感器的节点可以参与许多传感任务,以提高测量精度。然而,新节点对相应传感精度的贡献取决于现有节点的数量,而能量成本将以一致的方式增加。因此,在任务精度和能量成本之间存在权衡。本文考虑冗余传感器节点贡献的动态衰落事实,提出了一种感知贡献的任务分配方法,以最大限度地提高传感器网络的总精度效率。该方法源自最大权重资源分配算法(即KM算法),能够保证解的最优性。并与另外两种贪心优化方法进行了比较。实验结果表明,该方法优于其他同类方法,效率更高。
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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