Resource Optimization in IoT Systems : A Hybrid AI-based Approach for Enhancing Computational Efficiency and Reducing Latency

With the rapid proliferation of IoT and smart devices, optimizing resource management in edge computing environments is critical to ensuring efficient computational performance and minimal latency. This study proposes a hybrid AI-based framework that integrates reinforcement learning with heuristic optimization techniques, including genetic algorithms, to dynamically predict and allocate resources based on real-time workload characteristics. The proposed model enhances computational efficiency by optimizing resource distribution, reducing latency, and improving system scalability. Comparative experiments indicate that the AI-driven approach achieves up to a 30% improvement in computational efficiency and scalability compared to conventional resource allocation methods. This adaptive framework provides a robust and scalable solution for real-time data processing in IoT networks, significantly reducing response time and enhancing overall system performance.