An Energy‐Efficient Adaptive Framework for Anomaly Detection and Collaborative Isolation in Wireless Sensor Networks

ABSTRACT Because of their limited resources and vulnerability to security threats, wireless sensor networks (WSNs) are crucial for critical applications. It is possible to optimize WSNs in terms of security and energy efficiency by using ASOCIDA (Adaptive Self‐Optimizing Framework for Anomaly Detection and Collaborative Isolation). The ASOCIDA system optimizes performance by aggregating adaptive data, predicting anomalies, and controlling feedback. Local decision‐making is also guided by a Markov Decision Process. Compared to existing WSN models, ASOCIDA outperforms them on energy consumption, response time, recovery time, and latency.