Comparative Analysis of Data Management in Internet of Things Using Cloud and Edge Computing Approaches

The increasing expansion of the Internet of Things (IoT) has led to the continuous generation of massive volumes of diverse data. Efficient processing and analysis of such information has become a serious challenge, in which the manner of data management plays a key role. The principal approaches for data management in these environments include cloud computing and edge computing, each possessing its own distinct advantages and limitations. Cloud computing, with its scalable architecture and high processing power, offers a powerful solution for big data analytics and centralized management, yet it faces challenges such as network latency and high bandwidth costs. In contrast, edge computing, by moving processing close to the data source, provides reduced latency and lower network load; however, limited computational resources, the complexity of distributed management, and energy challenges constrain its use. This paper, adopting a review-oriented approach and analyzing credible sources, presents a comparative examination of data management in IoT with a focus on comparing cloud and edge paradigms. The study first introduces fundamental concepts and characteristics of IoT data, then analyzes data management methods in each approach, drawing on studies such as the Snowflake architecture (Hentschel et al, ۲۰۲۵) for cloud computing and data management patterns in edge computing (Al-Shareeda et al, ۲۰۲۴). Subsequently, a comparative analysis is presented based on criteria such as latency, scalability, energy consumption, cost, complexity, and network efficiency. The results indicate that the two approaches complement each other, and hybrid Cloud-Edge architectures are emerging as the main future direction for data management.

کلیدواژه ها:

Internet of Things ، Data Management ، Cloud Computing ، Edge Computing