Q-Learning-Based Time-Critical Data Aggregation Scheduling in IoT

Abstract

Time-critical data aggregation in Internet of Things (IoT) networks demands efficient, collision-free scheduling to minimize latency for applications like smart cities and industrial automation. Traditional heuristic methods, with two-phase tree construction and scheduling, often suffer from high computational overhead and suboptimal delays due to their static nature. To address this, we propose a novel Q-learning framework that unifies aggregation tree construction and scheduling, modeling the process as a Markov Decision Process (MDP) with hashed states for scalability. By leveraging a reward function that promotes large, interference-free batch transmissions, our approach dynamically learns optimal scheduling policies. Simulations on static networks with up to 300 nodes demonstrate up to 10.87% lower latency compared to a state-of-the-art heuristic algorithm, highlighting its robustness for delay-sensitive IoT applications. This framework enables timely insights in IoT environments, paving the way for scalable, low-latency data aggregation.