Analysis of Learning-Based Methods for Intelligent Edge Computing - Sandar, Nay Myo; Win, Thinzar Aung (Journal of Electronics and Informatics, 2026)
Edge Computing is recognized as an important computing paradigm that helps in the processing of data as well as the making of decisions near the user, hence reducing the need to rely on cloud infrastructure. Edge Computing is of high importance in fields that are sensitive to latency, such as smart vehicles, IoT services, health monitoring, UAVs, and smart cities. Edge Computing environments face various issues, such as limited computational power, network variability, mobility, and the need to process high volumes of diverse tasks. These issues in edge computing are difficult to handle using traditional methods of optimization. Deep Reinforcement Learning (DRL) algorithms are significantly used in edge computing systems as an intelligent decision-making tool that can optimize decisions through interactions with the edge computing system. This review paper presents a comprehensive analysis of recent studies and representative research that apply DRL techniques to edge computing systems. The reviewed papers cover various applications such as vehicular edge computing, IoT-based multi-access edge computing, UAV-assisted edge computing, collaboration between fog and cloud computing, and blockchain and federated learning-based edge computing systems. The discussion on DRL applications encompasses task offloading, resource management, workload management, caching, and mobility management. A comparative analysis discusses the most used DRL algorithms and their applications along with performance improvements. Additionally, this literature review highlights some of the main drawbacks of DRL, such as training complexity, scalability, privacy, and deployment, and identifies areas that require improvement to create safe and efficient DRL-based edge computing solutions.
Keywords: Edge Computing; Deep Reinforcement Learning; Task Offloading; Resource Allocation; Internet of Things (IoT); Vehicular Edge Computing