Energy and bandwidth-efficient Wireless Sensor Networks for monitoring high-frequency events

Wireless Sensor Networks (WSNs) are mostly deployed to detect events (i.e., objects or physical changes) at a high/low frequency sampling that is usually adapted by a central unit (or a sink), thus requiring additional resource usage in WSNs. However, the problem of autonomous adaptive sampling regarding the detection of events has not been studied before. In this paper, we propose a novel scheme, termed 'event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)' by addressing the problem in two stages, which lead to reduced resource usage (e.g., energy, radio bandwidth) in WSNs. First, e-Sampling provides a solution 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 WSNs. In the absence of an event, 'uninteresting' data is not transmitted to the sink. We apply e-Sampling to structural health monitoring (SHM), which is a typical application of high frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in expanding the capacity of WSNs for high data rate applications.