Dynamic and Distributed Routing in IoT Networks based on Multi-Objective Q-Learning
This addresses the need for adaptable and efficient routing in dynamic IoT environments, offering a novel distributed solution for real-time priority shifts.
The paper tackled the problem of conflicting and dynamic routing goals in IoT networks, such as balancing packet delivery, delay, and energy efficiency, by proposing a distributed multi-objective Q-learning algorithm that adapts in real time, achieving up to 80-90% lower energy consumption and 2-5x higher rewards and packet delivery compared to baselines.
IoT networks often face conflicting routing goals such as maximizing packet delivery, minimizing delay, and conserving limited battery energy. These priorities can also change dynamically: for example, an emergency alert requires high reliability, while routine monitoring prioritizes energy efficiency to prolong network lifetime. Existing works, including many deep reinforcement learning approaches, are typically centralized and assume static objectives, making them slow to adapt when preferences shift. We propose a dynamic and fully distributed multi-objective Q-learning routing algorithm that learns multiple per-preference Q-tables in parallel and introduces a novel greedy interpolation policy to act near-optimally for unseen preferences without retraining or central coordination. A theoretical analysis further shows that the optimal value function is Lipschitz-continuous in the preference parameter, ensuring that the proposed greedy interpolation policy yields provably near-optimal behavior. Simulations show that our approach adapts in real time to shifting priorities and achieves up to 80-90\% lower energy consumption and more than 2-5x higher cumulative rewards and packet delivery compared to six baseline protocols. These results demonstrate significant gains in adaptability, delivery, and efficiency for dynamic IoT environments.