Change Point Monitoring in Wireless Sensor Networks Under Heavy-Tailed Sequence Environments

In the special case of a heavy-tailed sequence environment, change point monitoring in wireless sensor networks faces many serious challenges, such as high communication overhead, particularly sensitivity to sparse changes, and dependence on strict parameter assumptions. In order to solve these limitations, a distributed robust M-estimator-based change point monitoring (DRM-CPM) method is proposed. This method combines ratio statistics with sliding window technology so that in online detection, there is no need to know the distribution before and after changes in advance. A threshold-triggered communication strategy is introduced, where sensors exchange local statistics only when exceeding predefined thresholds, significantly reducing energy consumption. By means of theoretical analysis, the asymptotic characteristics of the statistics are confirmed, and the robustness of the algorithm to heavy-tail noise and unknown parameters is also proved. Simulation results show that the algorithm is better than the existing methods in terms of empirical size control, empirical power, and communication efficiency, particularly in the face of sparse variation or heavy-tailed data. This framework provides a scalable solution for real-time anomaly monitoring with non-Gaussian data characteristics in industrial and environmental applications.