Data Management Techniques in Edge computing: Systematic Survey

Edge computing has emerged as a critical paradigm for enabling low-latency, bandwidth-efficient, and scalable data processing in distributed IoT environments. However, its effectiveness fundamentally depends on how data is cached, stored, aggregated, and fused across heterogeneous and resource-constrained edge nodes. To address this, the present survey conducts a comprehensive and methodologically rigorous examination of data-management techniques in edge computing. An initial corpus of 150 publications was collected from major scientific databases and processed through the PRISMA framework, resulting in 25 high-quality surveys that revealed data management as the most fragmented and underdeveloped component of the edge ecosystem. Building on these insights, we performed an in-depth analysis of 75 state-of-the-art research papers published between 2018 and 2025, covering four core data-management pillars: data caching, data storage, data aggregation, data validation and data fusion. For each area, we synthesize current design strategies, highlight measurable performance outcomes, and critically evaluate architectural, algorithmic, and system-level limitations. A unified cross-technique analysis further reveals unresolved challenges in scalable data placement, coded storage, privacy-preserving aggregation, multi-modal fusion, and the absence of integrated data pipelines. The survey concludes by outlining open research directions and proposing a consolidated roadmap toward intelligent, interoperable, and workload-aware data-management frameworks for next-generation edge computing systems.