Sampling rate adaptation is a critical issue in many resource-constrained networked systems, including Wireless Sensor Networks (WSNs). Existing algorithms are primarily employed to detect events such as objects or physical changes at a high, low, or fixed frequency sampling usually adapted by a central unit or a sink, therefore requiring additional resource usage. Additionally, this algorithm potentially makes a network unable to capture a dynamic change or event of interest, which therefore affects monitoring quality. This article studies the problem of a fully autonomous adaptive sampling regarding the presence of a change or event. We propose a novel scheme, termed “event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)” by addressing the problem in two stages, which leads to reduced resource usage (e.g., energy, radio bandwidth). First, e-Sampling provides the embedded algorithm to adaptive sampling that automatically switches between high- and low-frequency intervals to reduce the resource usage, while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identification algorithm suitable for decentralized computing in resource-constrained networks. In the absence of an event, the “uninteresting” data is not transmitted to the sink. Thus, the energy cost is further reduced. e-Sampling can be useful in a broad range of applications. We apply e-Sampling to Structural Health Monitoring (SHM) and Fire Event Monitoring (FEM), which are typical applications of high-frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in effectively expanding the capacity of WSNs for high data rate applications.

中文翻译：

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电子抽样

采样率适应是许多资源受限的网络系统中的一个关键问题，包括无线传感器网络 (WSN)。现有算法主要用于以通常由中央单元或接收器采用的高、低或固定频率采样检测物体或物理变化等事件，因此需要额外的资源使用。此外，该算法可能使网络无法捕获动态变化或感兴趣的事件，从而影响监控质量。本文研究了关于变化或事件存在的完全自主自适应采样的问题。我们提出了一种新颖的方案，称为“事件敏感自适应采样和低成本监控（e-Sampling）”，通过分两个阶段解决问题，从而减少资源使用（例如，能源、无线电带宽）。首先，e-Sampling 为自适应采样提供嵌入式算法，自动在高频和低频间隔之间切换，以减少资源使用，同时最大限度地减少误报检测。其次，通过分析频率内容，e-Sampling 提出了一种适用于资源受限网络中分散计算的事件识别算法。在没有事件的情况下，“无趣”的数据不会传输到接收器。因此，进一步降低了能源成本。电子采样可用于广泛的应用。我们将电子采样应用于结构健康监测 (SHM) 和火灾事件监测 (FEM)，这是高频事件的典型应用。通过模拟和实验进行的评估验证了电子采样在低成本事件监测中的优势，