A Hybrid Deep Learning and Edge Computing Framework for Real-Time IoT Data Processing

Abstract

Massive amounts of continuous, real-time data have been produced by the quick growth of Internet of Things (IoT) ecosystems, requiring sophisticated, low-latency, and extremely efficient processing solutions. Due to bandwidth limitations, latency overheads, and escalating privacy issues, traditional cloud-centric architectures are becoming less and less suitable for time-sensitive IoT applications. In response, this study presents a Hybrid Deep Learning–Edge Computing Framework (HDL-ECF) that combines cloud-supported deep learning with on-device intelligence to enable quick and dependable IoT data processing. By strategically allocating computational jobs across edge and cloud resources, the suggested methodology investigates how hybrid AI systems increase efficiency and scalability while lowering response times and preserving energy. By dynamically adapting to changing network conditions, hybrid AI models greatly outperform standalone AI approaches, improving system scalability and overall operational performance, according to a thorough analysis of system design and performance metrics. The results of this research open the door to the deployment of intelligent, autonomous edge infrastructures that can handle the growing computational needs of contemporary IoT networks.