Approximate Data Collection for Wireless Sensor Networks

Chao Wang, Huadong Ma, Yuan He, Shuguang Xiong
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引用次数: 11

Abstract

Data collection is a fundamental issue in wireless sensor networks. In many application scenarios for sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with given error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform a global approximate data collection on the sink according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of several subsets of sensor nodes, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and use a greedy heuristic algorithm to find an approximate solution. We also propose a monitoring algorithm to adjust these subsets according to the changes of sensor readings. Our trace-driving simulation results show that our data collection approach ADC can notably reduce the communication cost with given error bounds.
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无线传感器网络的近似数据采集
数据采集是无线传感器网络的一个基本问题。在传感器网络的许多应用场景中,由于通信带宽和能量预算的限制,近似数据采集是一种明智的选择。本文主要研究无线传感器网络中具有给定误差边界的有效近似数据采集问题。我们的数据收集方法ADC (Approximate data collection)的关键思想是将传感器网络划分为簇,发现每个簇头上的局部数据相关性,并根据簇头上传的模型参数在sink上执行全局近似数据收集。具体来说,我们提出了一个局部估计模型来近似传感器节点的几个子集的读数,并使用该模型证明了数据收集的额定误差范围。在基于模型的数据收集过程中,我们将传感器节点最小子集的选择问题转化为一个np困难的最小支配集问题,并使用贪婪启发式算法求出近似解。我们还提出了一种监测算法,根据传感器读数的变化来调整这些子集。跟踪驱动仿真结果表明,在给定误差范围内,我们的数据采集方法ADC可以显著降低通信成本。
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