A Comprehensive Review of Edge Computing Approaches for Secure and Efficient Data Processing in IoT Networks

Abstract

The exponential growth of IoT networks brings the related concerns of data security, privacy, and regulatory compliance to the fore, especially when threats to traditional cloud-based processing models include latency, cyberattacks, and unauthorized access to the data. Edge computing emerged as a decentralized solution that brings data processing closer to IoT devices to reduce single points of failure while enhancing real-time threat detection. In this paper, we examine some of the most important security technologies for edge-based IoT environments, such as Trusted Execution Environments (TEEs) and their use in conjunction with homomorphic encryption and federated learning, analyzing their strengths and weaknesses. It highlights scalability challenges, security vulnerabilities, and regulatory compliance issues within edge computing. Other emerging trends like blockchain-integrated edge AI, post-quantum cryptography, and self-learning cybersecurity models will enable the next generation of secure, privacy-preserving IoT ecosystems. By adopting hybrid security frameworks and adaptive AI-driven security mechanisms, firms can guarantee a robust, scalable, and compliant edge computing solution for IoT network