Energy-Aware Edge Computing Optimization for Real-Time Anomaly Detection in IoT Networks

This research addresses the critical challenge of energy-efficient anomaly detection in resource-constrained edge computing environments for IoT networks. With the proliferation of IoT devices generating exponential data volumes and information technology energy consumption projected to reach 20% of global electricity production by 2030, sustainable computing approaches at the network edge are imperative. We propose a novel optimization framework that dynamically balances computation offloading decisions with local processing capabilities to minimize energy consumption while maintaining detection accuracy and meeting real-time requirements. The framework incorporates: (1) a calibrated energy consumption model for heterogeneous edge environments, (2) an adaptive resource allocation strategy responding to network conditions, (3) lightweight machine learning architecture optimized for minimal energy footprint, and (4) intelligent computation offloading based on device energy states. Experimental evaluation on a testbed of 16 heterogeneous edge devices processing real-world IoT traffic demonstrates energy consumption reduction of 23.8% compared to traditional approaches, while maintaining detection accuracy above 92.5% across diverse anomaly types. The system extends battery life by up to 165% in energy-constrained scenarios through dynamic adjustment of detection parameters. Comparative analysis confirms superior performance against state-of-the-art methods in both energy efficiency and detection capability, particularly in environments with variable energy availability.