AI-Driven Edge Computing Architecture for Real-Time Data Analysis in Next-Generation Smart Applications
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Abstract
The rapid growth of IoT, AI, 5G/6G, and smart applications has created a strong need for fast, secure, and intelligent real-time data processing. This study proposes an AI-Driven Edge Computing Architecture (AIDECA) that integrates edge computing, artificial intelligence, IoT, cloud collaboration, federated learning, explainable AI, and security mechanisms for next-generation smart applications. The architecture enables localized data processing, low-latency decision-making, efficient resource utilization, and privacy-aware analytics. Experimental evaluation using 8.7 million heterogeneous sensor records demonstrates improvements in processing latency, throughput, bandwidth efficiency, scalability, and resource utilization, while achieving 97.2% prediction accuracy. The findings highlight the potential of AI-driven edge computing to support intelligent and reliable applications in healthcare, autonomous systems, smart cities, industrial IoT, and intelligent energy systems.
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