A Bisection Reinforcement Learning Approach to 3-D Indoor Localization

The demand for indoor localization services in the Internet of Things (IoT) has been increasing dramatically during the last decade. Many indoor localization systems adopt Wi-Fi fingerprinting with received signal strength indicators (RSSIs) as a source of sensors to localize an object because it is cost effective and can give high accuracy. However, the fluctuation of wireless signals resulting from environmental uncertainties leads to considerable variations in RSSIs, which poses a challenge to accurate localization on a single floor, not to mention multifloor or even 3-D localization. Most existing multifloor methods employ a sequential approach where a different algorithm is tailored for each step in the sequence to determine the floor and then the location of an object. In this article, we formulate the indoor localization problem as a Markov decision process rather than a typical classification or regression problem. A deep reinforcement learning method is used to bisect the search space in a hierarchy from the entire building down to a prespecified distance scale to the object position. This approach significantly reduces the time complexity of the searching from O(N-3) to O(logN), where N indicates the localization resolution. The proposed method tackles environmental dynamics with Wi-Fi fingerprinting for 3-D continuous space. The experimental results demonstrate the high accuracy, efficiency, and robustness of the proposed approach.