Distributed localization for IoT with multi-agent reinforcement learning

Localization has become one of the important techniques for Internet of Things (IoT). However, most existing localization methods need a central controller and operate on an off-line manner, which cannot satisfy the requirements of real-time IoT applications. In order to address this issue, a novel distributed localization scheme based on multi-agent reinforcement learning (MARL) is proposed. The localization problem is first reformulated as a stochastic game for maximizing the sum of the negative localization error. Each non-anchor node is then modeled as an intelligent agent, where its action space corresponds to possible locations. After that, we invoke a MARL framework on the basis of conventional Q-learning framework to learn the optimal policy, and to maximize the long-term expected reward. The novel strategy is also proposed to reduce the localization error. Extensive simulations demonstrate that the proposed localization method is superior to game theoretic-based distributed localization algorithm and virtual force-based distributed localization algorithm in terms of both localization accuracy and convergence speed, and is suitable for on-line localization scenarios.