EdgefusionAI: An Intelligent Edge Computing Framework For Realtime Iot Data Processing Using Federated Learning

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Authors: Dr N.Umamaheswari, Dr.K.Gayathri

Abstract: The Internet of Things (IoT) links sensors, machines, vehicles and everyday objects so that they can exchange data and act with little human intervention. Fifth-generation (5G) networks introduced dedicated support for massive machine-type and ultra-reliable low-latency communication, but emerging applications such as autonomous transport, remote surgery, immersive extended reality and large-scale digital twins demand still higher data rates, lower latency, greater reliability, precise positioning and far denser connectivity. Sixth-generation (6G) networks, now being defined under the IMT-2030 framework, aim to meet these needs and to make artificial intelligence a native part of the network. This survey reviews 6G-enabled IoT. It summarises the requirements and key performance indicators, and explains the enabling technologies: terahertz and sub-terahertz communication, intelligent reflecting surfaces, integrated sensing and communication, edge intelligence, AI-native network management and non-terrestrial networks. A five-layer architecture for 6G-enabled IoT is presented, linking devices, access, edge, core and applications under a cross-layer security and energy plane. Applications in smart cities, connected vehicles, healthcare, industrial automation, precision agriculture and environmental monitoring are discussed. Finally, open challenges including infrastructure cost, spectrum regulation, energy consumption, security and privacy, and the management of billions of devices are identified.

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