Edge Computing and AI for Real-time Analytics in Smart Devices

Edge computing and artificial intelligence (AI) are transforming real-time data analytics in smart devices by enabling low-latency, efficient, and decentralized processing. Traditional cloud-based approaches introduce high latency, bandwidth constraints, and security risks, making them less viable for real-time applications. This research explores Edge AI architectures, optimization techniques, and their integration with IoT to enhance real-time decision-making in smart devices. We analyze various AI models, including lightweight neural networks, federated learning, and quantization techniques, to optimize computational performance while maintaining energy efficiency and accuracy. Additionally, we evaluate security concerns and propose a blockchain-integrated trust management system for safeguarding edge-based AI deployments. Experimental results demonstrate that Edge AI significantly reduces latency by up to 45%, improves bandwidth utilization by 30%, and enhances real-time inference accuracy for applications such as healthcare monitoring, industrial automation, and smart city infrastructure. This study provides a comprehensive evaluation of Edge AI’s role in real-time analytics and offers future directions for scalable and secure edge intelligence.