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.