SMART DEVICE REAL-TIME ANALYTICS USING EDGE COMPUTING AND AI

By facilitating low-latency, effective, and decentralized processing, edge computing and artificial intelligence (AI) are revolutionizing real-time data analytics in smart devices.Conventional cloud-based methods are less practical for real-time applications due to their high latency, bandwidth limitations, and security threats.In order to improve real-time decision-making in smart devices, this study investigates Edge AI architectures, optimization strategies, and their integration with IoT.We examine a range of AI models, such as federated learning, lightweight neural networks, and quantization strategies, in order to maximize accuracy and computing economy.We also assess security issues and provide a blockchain-based trust management mechanism to protect edge-based AI implementations.According to experimental findings, Edge AI dramatically lowers latency by up to 45%, increases bandwidth usage by 30%, and improves the accuracy of real-time inference for applications including smart city infrastructure, industrial automation, and healthcare monitoring.In addition to offering future prospects for scalable and safe edge intelligence, this paper presents a thorough assessment of Edge AI's function in real-time analytics.