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Title	Hybrid Ai-Electronic Systems For Real-Time Edge Processing In Iot Networks
ID_Doc	29699
Authors	Sweety Prasanna Kiruba G.; Esakki Madura E.; Kumar B.V.; Kavitha M.; Reddy T.S.; Athiraja A.
Year	2025
Published	Proceedings of the 3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025
DOI	http://dx.doi.org/10.1109/IITCEE64140.2025.10915254
Abstract	Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.
Author Keywords	Edge processing; Hybrid AI systems; IoT networks; Low Latency; Machine Learning; Real-Time data

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