The field of edge computing and anomaly detection is moving towards more efficient and accurate methods for real-time data processing and analysis. Researchers are exploring the use of Markov chains and stochastic modeling to improve the reliability and energy efficiency of edge computing systems. Additionally, there is a growing interest in developing lightweight and interpretable anomaly detection frameworks that can detect anomalies in real-time without requiring labeled data or intensive computation. Noteworthy papers in this area include: An Efficient Anomaly Detection Framework for Wireless Sensor Networks Using Markov Process, which proposes a lightweight and interpretable anomaly detection framework based on a first-order Markov chain model. Conditional Score Learning for Quickest Change Detection in Markov Transition Kernels, which introduces a score-based CUSUM procedure for detecting changes in Markov processes with unknown transition kernels.