Multi-Sensors Time Series Change Point Detection in Wireless Sensor Networks Based on Deep Evidential Fusion and Self Distillation Learning

The increasing use of wireless sensor networks (WSNs) necessitates rapid detection and mitigation of system anomalies and state changes, which can be achieved through change point detection (CPD) methods. This article introduces a novel approach to detect change points in WSNs, employing an integrated multimodal method that combines three innovative feature extraction models and a learnable weighted fusion layer for evidence synthesis. Leveraging subjective logic and Dempster-Shafer theory with multivariate time series deep learning, the proposed approach establishes a prior probability distribution informed by subjective logistic loss, thus enhancing decision-making reliability by quantifying uncertainty. To address sample imbalance, this study integrates subjective logistic loss with a sample size parameter and introduces a Kullback-Leibler (KL) loss to prevent overconfident errors. A novel semi-supervised training model employing self-distillation and a multimodel KL loss is also proposed, which significantly improves accuracy and robustness. Comprehensive experiments validate the method, with accuracy improvements of up to 97.79% and 99.69% on various datasets, setting a new benchmark for CPD performance and demonstrating the method’s potential for real-world applications.