Hybrid AI-Electronic systems for Real-Time Edge Processing in IoT networks

Abstract

The rapid expansion of the Internet of Things (IoT) has driven the need for real-time, low-latency data processing at the edge. Traditional cloud-based architectures suffer from high transmission delays, increased energy consumption, and privacy concerns, limiting their efficiency in time-sensitive applications. To address these challenges, this paper proposes a Hybrid AI-Electronic System for Real-Time Edge Processing in IoT Networks, integrating artificial intelligence (AI) with electronic edge computing to enhance performance, efficiency, and decision-making capabilities. The proposed system utilizes lightweight AI models optimized for edge devices, reducing computational overhead while maintaining accuracy. A hybrid architecture combining neuromorphic computing, FPGA accelerators, and energy-efficient processors ensures rapid inference and adaptive learning at the edge. Experiments conducted on real-world IoT datasets demonstrate a 34% improvement in processing speed, a 22% reduction in energy consumption, and 15% higher inference accuracy compared to conventional cloud-based approaches. These results highlight the feasibility and effectiveness of hybrid AI-electronic systems in enabling scalable, real-time, and intelligent IoT networks. Future research will focus on enhancing security, improving model adaptability, and integrating self-learning mechanisms to further optimize performance.