A Hybrid Edge-Cloud Framework for Efficient Big Data Processing in IoT Environments

Abstract

The rapid proliferation of Internet of Things networks generates voluminous and heterogeneous data, requiring robust mechanisms for real-time processing and analysis. A hybrid edge-cloud framework for efficient big data management emerges as a promising architecture to support latency-sensitive services and resource-intensive computations. This framework strategically leverages edge servers to handle immediate and localized tasks, thereby alleviating the burden on centralized cloud infrastructures. By processing large data streams at or near the source of their generation, network bottlenecks can be minimized, and faster response times become possible. Meanwhile, the cloud remains essential for complex analytical workloads and high-capacity data storage. These dual-layer interactions enable adaptive workload distribution, facilitate dynamic scaling, and can be guided by advanced resource allocation principles. This approach effectively addresses the demand for low-latency processing in applications such as autonomous vehicles, connected healthcare, and industrial automation. This work explores the design of a sophisticated hybrid architecture that orchestrates data traffic and computation flows among distributed edge nodes and centralized cloud data centers. The proposed framework aims to improve communication overhead, computation accuracy, and real-time responsiveness, even in resource-constrained devices. Limitations, such as varying network conditions and potential security vulnerabilities, are also examined to highlight the challenges of deploying this approach in real-world scenarios.