Recent Trends in Electronics Communication Systems Review Article
Framework for Edge AI and intelligent IoT with emphasis EEE integration, lightweight ML, communication, security, and deployment monitoring
Abstract
Edge Artificial Intelligence (Edge AI) brings machine learning inference and, in selected cases, learning close to the sensors and devices that generate data. When combined with the Internet of Things (IoT), it creates intelligent connected systems that can sense, interpret, decide, and act without depending continuously on a remote cloud. This review presents a practical framework for Edge AI and intelligent IoT with emphasis on electrical and electronics integration, lightweight machine learning, communication, security, and deployment monitoring. The paper explains a layered architecture from sensors and microcontrollers to gateways, edge servers, and cloud services. It discusses model choices such as compact convolutional networks, anomaly detection, quantization, pruning, and TinyML, together with hardware platforms including microcontrollers, embedded processors, neural accelerators, and single-board computers. It also describes distributed approaches such as edge-cloud collaboration and federated learning, which can reduce raw-data transfer while supporting coordinated model improvement. Applications are considered in smart manufacturing, healthcare, transportation, agriculture, energy, and smart environments. Rather than treating Edge AI as a model-only problem, the proposed framework treats sensing quality, power management, connectivity, inference latency, cybersecurity, device health, and human oversight as parts of one engineered system. The review concludes that useful Edge AI depends on careful co-design between algorithms and hardware, transparent monitoring, and a clear division of responsibilities between local devices, edge infrastructure, and cloud services.
Keywords
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