[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2298":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":48},2298,"A systematic review of data handling and management techniques in edge computing for IoT","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09533-w","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.","物联网（IoT）设备的激增以及对延迟敏感的应用需求推动了边缘计算的发展，边缘计算通过在更接近数据源的位置进行数据处理，对现有云系统形成补充。通过在更接近数据源的位置处理数据，边缘计算降低了延迟、带宽消耗和响应时间。由于边缘环境采用去中心化系统且资源有限，组织在数据管理方面面临重大挑战，需要管理多项数据管理任务，包括存储资源、数据缓存、数据聚合和数据完整性验证。解决这些问题对于构建能够大规模运行且保持可信和安全操作的边缘物联网系统至关重要。本文对2022年至2025年间物联网应用中边缘计算数据处理优化技术的最新进展进行了全面系统的综述。本研究采用符合Scopus和Web of Science标准的结构化综述方法，将研究文献分为四大类进行评估，包括数据存储优化、数据缓存策略、数据聚合机制和数据完整性保障技术。共选取52篇主要技术研究进行详细比较分析，同时使用其他二手文献来源提供理论背景和情境讨论。本研究进行了全面的比较分析，从延迟和能耗、系统可扩展性、安全开销以及不同工作负载下的系统性能等方面评估性能。综述评估了特定应用场景，包括智慧城市、医疗健康、工业物联网、自动驾驶汽车和智慧农业，以展示当前解决方案的实际意义。综述指出了自适应优化、异构设备支持、隐私保护和跨层协调方面的不足，将其列为关键研究问题。本文描述了现有挑战和未来研究方向，有助于为边缘赋能的物联网网络构建先进的数据处理系统。",null,"Discover Applied Sciences","2026-09-11T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"系统综述边缘计算数据管理技术，含智慧农业等应用场景，对农业物联网部署有参考价值，但非农业专属研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","边缘计算","物联网","农业传感器","数据管理",0,"10.1007\u002Fs42452-026-09533-w",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":41,"direction":45,"ingested_from":47},"W7212114265",[37,39],{"name":38,"orcid":9},"Sourabh Natu",{"name":40,"orcid":9},"Ankur Goyal",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"系统综述2022-2025年IoT边缘计算数据处理与管理优化技术，涵盖存储、缓存、聚合与完整性四类。","遵循Scopus\u002FWoS标准的结构化综述，比较分析52篇主要技术研究。","识别出自适应优化、异构设备支持、隐私保护与跨层协调四大关键研究空白。","智慧农业 \u002F 农业物联网","可将边缘数据管理优化方法迁移至农业IoT场景，填补农业边缘计算中自适应与跨层协调的研究空白。","openalex","2026-09-13T23:30:09.503310Z"]