Lightweight Cooperative Anomaly Detection Framework for Multi-Sensor Edge Systems

Edge-based anomaly detection has become increasingly important in Industrial IoT (IIoT) systems due to stringent latency, privacy, and connectivity requirements. However, edge devices such as IoT sensors operate under severe computational and memory constraints, making lightweight models essential to preserve detection performance under limited resources. This paper presents an anomaly detection framework that achieves robust multi-sensor cooperation by integrating multiple lightweight sensor models using entropy-based adaptive weighting. The weighting coefficient for each sensor is analytically derived from the entropy of its reconstruction error distribution, providing a principled, information-theoretic measure of sensor contribution without requiring raw data sharing, label supervision, or auxiliary meta-models. Experiments on an industrial acoustic benchmark show that the proposed weighting scheme improves key detection metrics - particularly recall - while maintaining robust performance across spatially heterogeneous sensing conditions. Additional analysis reveals that cooperation is most beneficial when sensors exhibit complementary error behaviors. Finally, a full implementation on Raspberry Pi 4 sensor nodes demonstrates that the method operates efficiently on low-power edge hardware, confirming its suitability for practical IIoT deployments.