Indoor Fingerprint Collection under Environment Changes by Vehicular Crowdsensing: A Bayesian Reinforcement Learning Approach

Indoor localization is crucial for applications such as navigation, asset tracking, and emergency response. Fingerprint-based methods that use RSSI are widely adopted; however, they fail under large environmental changes. Unmanned Vehicles (UVs) equipped with high precision sensors are able to collect fingerprints, serving as a promising way by forming a Vehicular Crowdsensing (VCS) campaign. In this paper, we propose “BRAVE”, a Bayesian RL Approach for VCS under Environment changing, while introducing a new metric “Calibration Benefit” to explicitly quantify how effectively a learned trajectory updates those regions of the fingerprint database that have changed and matter most for localization. Specifically, we propose a spatial-temporal Bayesian Network(BN) for change detection, a region rearrangement method for fewer restarts, and an optimistic strategy to balance the exploration and exploitation trade-offs in optimizing calibration benefit. Extensive results on two real-world datasets from SML Center (Shanghai) and Haopu Fashion City (Shanghai) demonstrate that BRAVE outperforms eight baselines and the derived dataset has better localization accuracy compared with the original dataset.