Constraint chaining: on energy-efficient continuous monitoring in sensor networks

Adam Silberstein, R. Braynard, Jun Yang
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引用次数: 187

Abstract

Wireless sensor networks have created new opportunities for data collection in a variety of scenarios, such as environmental and industrial, where we expect data to be temporally and spatially correlated. Researchers may want to continuously collect all sensor data from the network for later analysis. Suppression, both temporal and spatial, provides opportunities for reducing the energy cost of sensor data collection. We demonstrate how both types can be combined for maximal benefit. We frame the problem as one of monitoring node and edge constraints. A monitored node triggers a report if its value changes. A monitored edge triggers a report if the difference between its nodes' values changes. The set of reports collected at the base station is used to derive all node values. We fully exploit the potential of this global inference in our algorithm, CONCH, short for constraint chaining. Constraint chaining builds a network of constraints that are maintained locally, but allow a global view of values to be maintained with minimal cost. Network failure complicates the use of suppression, since either causes an absence of reports. We add enhancements to CONCH to build in redundant constraints and provide a method to interpret the resulting reports in case of uncertainty. Using simulation we experimentally evaluate CONCH's effectiveness against competing schemes in a number of interesting scenarios.
约束链:传感器网络中节能连续监测的研究
无线传感器网络为各种场景中的数据收集创造了新的机会,例如环境和工业,我们期望数据在时间和空间上相关。研究人员可能希望从网络中持续收集所有传感器数据,以供以后分析。时间和空间抑制为降低传感器数据收集的能量成本提供了机会。我们将演示如何将这两种类型结合起来以获得最大效益。我们将该问题描述为一个监控节点和边缘约束的问题。如果被监视节点的值发生变化,则触发报告。如果被监视的边缘的节点值之间的差异发生变化,则触发报告。在基站收集的报告集用于派生所有节点值。我们在我们的算法CONCH中充分利用了这种全局推理的潜力,CONCH是约束链的缩写。约束链构建了一个约束网络,这些约束在本地维护,但允许以最小的成本维护全局的值视图。网络故障使抑制的使用复杂化,因为两者都会导致缺少报告。我们对CONCH进行了增强,以构建冗余约束,并提供了一种在不确定情况下解释结果报告的方法。通过模拟,我们在许多有趣的场景中实验评估了CONCH对竞争方案的有效性。
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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