Authors: Assistant Professor Dr. S. Muthukumaran, Assistant Professor Dr. A. Victoria Anand Mary, Assistant Professor J. Antony Daniel Rex, Assistant Professor P. Simon Vasantha Rooban
Abstract: The proliferation of the Internet of Things (IoT) in various fields such as critical infrastructures, health care, and industry has brought about the need for intelligent systems that can process huge volumes of data with low latency. In this paper, we pro-pose an Edge AI based intelligent IoT framework which utilizes lightweight deep learning models coupled with distributed edge computing for real-time monitoring, fault detection, and analysis of data. In this work, we use a hybrid CNN-LSTM model on resource-constrained edge devices such as Raspberry Pi and NVIDIA Jetson Nano. Our experimental results show that the proposed system is able to detect faults with an accuracy of 92.0%, with response time below 150ms, a decrease in latency of 52% and a reduction of bandwidth utilization by 38% as compared to the cloud approach. We obtain a high precision of 91.9% and F1 score of 90.8% in physiological time series data using federated learning.
