Edge-AI Enabled Smart Sensor Networks for Real-Time Decision Making in 6G Environments

The rapid advancement of sixth-generation (6G) wireless technologies is enabling the development of highly intelligent and interconnected sensor networks capable of supporting real-time, data-driven applications. In this context, Edge Artificial Intelligence (Edge-AI) has emerged as a transformative paradigm that integrates computational intelligence directly at the network edge, significantly reducing latency and improving responsiveness. This paper presents an Edge-AI enabled smart sensor network framework designed for real-time decision making in dynamic 6G environments. The proposed architecture leverages distributed learning models deployed on edge devices to process sensor data locally, minimizing reliance on centralized cloud infrastructure. By combining deep learning and lightweight inference mechanisms, the system enables efficient data analysis, anomaly detection, and context-aware decision-making in real time. Furthermore, the framework incorporates adaptive resource management strategies to optimize energy consumption, communication overhead, and computational efficiency across heterogeneous sensor nodes. The integration of advanced 6G technologies, including ultra-reliable low-latency communication (URLLC) and network slicing, enhances the system’s ability to support mission-critical applications such as smart cities, industrial automation, and intelligent healthcare. Simulation results demonstrate that the proposed Edge-AI framework significantly improves latency, reliability, and energy efficiency compared to conventional cloud-centric approaches. The findings highlight the potential of Edge-AI to transform traditional sensor networks into intelligent, autonomous systems capable of operating effectively in highly dynamic and resource-constrained environments. This work provides a scalable and efficient solution for next-generation real-time sensing and decision-making applications in 6G ecosystems.