A systematic review of data handling and management techniques in edge computing for IoT
The proliferation of Internet of Things (IoT) devices and latency-sensitive applications has increased the need for edge computing, which complements existing cloud systems by enabling data processing closer to the source. By processing data closer to the source, edge computing reduces latency, bandwidth consumption, and response time. Organisations face major challenges in data management because edge environments use decentralised systems and limited resources, which require them to manage multiple data management tasks, including storage resources, data caching, data aggregation and data integrity verification. The resolution of these problems is necessary to create Edge-IoT systems that can operate at a large scale while maintaining trustworthy and secure operations. This paper presents a comprehensive and systematic review of recent advancements that occurred between 2022 and 2025 in data handling optimisation techniques for edge computing in IoT applications. The study employs a structured review methodology that meets Scopus and Web of Science standards to evaluate research studies that are divided into four main categories, which include data storage optimisation, data caching strategies, data aggregation mechanisms and data integrity assurance techniques. A total of 52 primary technical studies were selected for detailed comparative analysis, while additional secondary literature sources were used for theoretical background and contextual discussion. The study conducts a comprehensive comparative analysis, which assesses performance through latency and energy consumption, system scalability, security overhead and system performance under varying workloads. The review evaluates application-specific use cases, which include smart cities, healthcare, industrial IoT, autonomous vehicles and smart agriculture, to demonstrate the real-world significance of present solutions. The review identifies gaps in adaptive optimisation, heterogeneous device support, privacy preservation, and cross-layer coordination as critical research problems. The document describes existing challenges and future research paths that will help create advanced data processing systems for edge-enabled Internet of Things networks.