Edge-Intelligent IoT: Adaptive Context-Aware Resource Orchestration for Ultra-Low-Latency Systems

The accelerated growth of Internet of Things (IoT) requires smart systems that can address the heterogenous resources and guarantee ultra-low latency to mission-critical systems. It will be described in this paper as an Edge-Intelligent IoT architecture allowing adaptive, context-sensitive resource coordination with lightweight machine learning on the network edge. The system takes into consideration dynamic contextual parameters such as workload patterns, mobility variations and network conditions to decide on the real time offloading and resource allocation decisions. The optimization of latency, energy usage, fairness of resources is made through reinforcement learning, and federated learning optimizes global intelligence without exposing raw data. Simulations with a wide scale indicate that the proposed solution is much faster and uses less energy than the conventional cloud-centric and static edge schemes. Scalability, responsiveness, and Quality of Service are also enhanced in the framework, which is compatible with next-generation IoT settings like autonomous vehicles, smart manufacturing and industrial automation