Edge-Aware Optimization for AI-Driven Anomaly Detection in Smart City Wireless Networks

Abstract

The rapid growth of smart cities has led to increased reliance on wireless networks and IoT devices, raising critical security concerns, particularly regarding anomaly detection. Traditional methods fall short in dynamic and resource constrained environments due to their limited adaptability and high computational demands. This paper presents a comprehensive review of AI-driven approaches to anomaly detection in smart city wireless networks, focusing on real-time adaptability and edge-aware optimization. It evaluates state of the art techniques such as federated learning, lightweight deep learning models, and hybrid AI frameworks that balance detection accuracy with efficiency. Public datasets like CICIDS 2017 and IoT-23 are discussed in detail for their role in training and evaluating these systems. Despite significant progress, existing models struggle with scalability, high false positive rates, and limited deployment readiness for edge devices. The paper proposes a conceptual framework to address these gaps and lays a foundation for future research in secure, scalable smart city networks. Additionally, this study incorporates architectural visualization and proposes feasible real-time deployment through edge-based simulators, addressing gaps in prior implementations and privacy safeguards.