ML Driven Anomaly Detection for IoT Edge Devices: Insights from ADMM-Based Frequency Management
DOI:
https://doi.org/10.48047/zc304719Keywords:
Keywords: Anomaly Detection, ADMM (Alternating Direction Method of Multipliers), Real-Time Processing, Scalability in IoT.Abstract
The rapid growth of Internet of Things devices has generated an urgent requirement for the development of efficient and dependable anomaly detection systems to ensure system integrity, security, and performance. Traditional centralized systems for anomaly detection are becoming increasingly ineffective due to issues related to scalability, periods of inactivity, and difficulties in handling the
diverse and substantial volumes of data generated by IoT edge bias. This design presents a novel framework for anomaly detection powered by machine literacy, specifically tailored for IoT edge devices. The Alternating Direction Method of Multipliers (ADMM) is employed to effectively enhance frequency operation
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