American Journal of Advanced Multidisciplinary Research and Innovation

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Intelligent Edge Networks for Real-Time Healthcare and Industrial Applications

Author(s) Abhishek Nagaraj
Country United States
Abstract The rapid growth of Internet of Things (IoT) devices, connected medical systems, industrial automation and artificial intelligence has created an increasing demand for computing architectures capable of processing data close to where it is generated. Intelligent edge networks address this requirement by combining edge computing, artificial intelligence, high-speed networking, distributed analytics and real-time decision-making. This paper examines the emerging role of intelligent edge networks in healthcare and industrial environments, where latency, reliability, privacy, bandwidth efficiency and operational continuity are critical. In healthcare, edge intelligence can support real-time patient monitoring, remote diagnostics, medical imaging, connected medical devices, emergency response and personalised care. In industrial environments, it can enable predictive maintenance, machine vision, autonomous robotics, process optimisation, digital twins and real-time quality control. The paper proposes an integrated intelligent edge architecture comprising sensing devices, edge nodes, AI inference, network orchestration, cloud coordination and security mechanisms. Particular attention is given to edge AI, federated learning, 5G/6G connectivity, digital twins and adaptive resource management. The paper also discusses challenges related to interoperability, cybersecurity, energy consumption, model deployment, data governance and heterogeneous hardware. It argues that the future of real-time digital infrastructure will increasingly depend on distributed intelligence in which computation, analytics and decision-making are dynamically coordinated between devices, edge nodes and cloud platforms.
Keywords Intelligent Edge Computing, Edge AI, Edge Networks, Healthcare IoT, Industrial IoT, Real-Time Analytics, 5G, Federated Learning, Digital Twins, Smart Manufacturing.
Field Engineering
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-03-05
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