Edge Intelligence Framework for Real-Time Data Processing in Internet of Things Networks
Keywords:
Edge intelligence, real‑time data processing, Internet of Things, distributed computing, system architecture, latency‑aware systems, resource governance, sustainability, fault tolerance, policy designAbstract
The proliferation of Internet of Things (IoT) devices has generated massive volumes of streaming data that require low-latency processing, bandwidth efficiency, and privacy preservation. Traditional cloud-centric architectures, while powerful, introduce unacceptable delays and network congestion for time-sensitive applications. Edge intelligence, which integrates artificial intelligence capabilities directly into network edge nodes, has emerged as a paradigm shift that enables real‑time analytics and decision‑making at the source of data generation. This paper presents a comprehensive edge intelligence framework designed for real‑time data processing in IoT networks. The framework comprises four functional layers: the perception layer for data acquisition, the edge computing layer for distributed processing, the fog coordination layer for intermediary orchestration, and the cloud integration layer for global analytics. The architecture, governance, and policy dimensions are examined, including data sovereignty, security, and interoperability. Structural trade‑offs between latency, accuracy, energy consumption, and scalability are analyzed through a system‑level lens. Deployment considerations for heterogeneous IoT environments, such as smart cities, industrial automation, and healthcare, are discussed with illustrative examples. Robustness mechanisms, including fault tolerance, adaptive resource allocation, and dynamic task offloading, are evaluated. Fairness and accountability in edge‑driven algorithmic decisions are addressed, along with regulatory implications. The framework emphasizes sustainable operation through energy‑aware scheduling and hardware‑software co‑design. This work contributes a holistic perspective that bridges technical architecture with socio‑technical governance, providing a blueprint for deploying trustworthy, scalable, and efficient edge intelligence systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
