Adaptive Task Scheduling for Energy-Efficient Multicore Embedded Systems using Reinforcement Learning

The rapid expansion of Internet of Things (IoT) devices has increased the need for intelligent mechanisms capable of identifying abnormal system behavior in real time. Conventional anomaly detection approaches often depend on computationally intensive machine learning models and centralized cloud processing, which can introduce latency, communication overhead, and increased resource requirements. This paper presents a lightweight machine learning-based architecture for real-time anomaly detection in embedded IoT systems. The proposed system integrates an embedded sensing and communication platform with a lightweight machine learning module capable of identifying abnormal patterns locally at the edge. The architecture incorporates data acquisition, preprocessing, feature selection, lightweight classification, anomaly decision, alert generation, and wireless communication. Several lightweight machine learning algorithms, including Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine, can be considered for deployment according to available computational resources. Feature reduction is incorporated to decrease memory requirements and inference complexity. The proposed architecture minimizes unnecessary transmission of raw IoT data by performing anomaly analysis near the data source. An energy-aware operating mechanism is also incorporated to support deployment on resource-constrained embedded devices. The proposed approach is suitable for industrial IoT, smart homes, healthcare IoT, environmental monitoring, and embedded cybersecurity applications requiring low-latency anomaly detection.