Edge Intelligence Framework for Ultra-Low Latency IoT Applications in 6G Networks

The rapid expansion of the Internet of Things (IoT) has resulted in billions of connected devices generating large volumes of data in real time. Many modern IoT applications, such as autonomous vehicles, remote healthcare, industrial automation, and smart city monitoring, require extremely fast response times and highly reliable communication. Traditional cloudbased architectures often struggle to meet these requirements because sending data to centralized servers introduces significant network delays. To address these challenges, this research proposes an Edge Intelligence Framework for ultra-low latency IoT applications in sixth-generation (6G) networks. The framework integrates edge computing, artificial intelligence (AI), and advanced 6G communication technologies to process data closer to IoT devices. By performing intelligent data analysis at the network edge, the system reduces communication delays and improves decision-making speed. The proposed framework uses distributed edge nodes, AI-based resource management, and ultra-reliable low-latency communication (URLLC) features of 6G networks. Simulation results demonstrate that the proposed architecture significantly reduces latency, improves network throughput, and enhances overall system reliability. Experimental analysis shows that the system can achieve sub-millisecond response times, making it suitable for time-critical IoT applications such as smart healthcare monitoring and autonomous systems. The results highlight the importance of edge intelligence in enabling next-generation IoT ecosystems in future 6G networks.