Environment-Aware Positioning by Leveraging Unlabeled Crowdsourcing Data

The heavy burden of fingerprint collection and annotation has become one of the biggest bottlenecks in wireless indoor positioning, particularly in the context of the Internet of Things (IoT). Fortunately, crowdsourcing can be leveraged to alleviate the fingerprint collection burden by harnessing the collective intelligence of crowdsourcing users. However, it is rather difficult to acquire an accurate positioning model-based solely on training unlabeled crowdsourcing data. To overcome this problem, we propose a novel positioning model called environment aware positioning (ENAP), utilizing unlabeled crowdsourcing trace data. The proposed ENAP mainly consists of three steps, i.e., transforming the unlabeled crowdsourcing trace data into a cluster space, mapping the cluster space into the positioning space, and continuously updates the positioning model in an unsupervised manner. To enhance the performance and robustness against device heterogeneity of crowdsourcing users, we propose a novel clustering scheme for space transformation by adaptively fusing multiple signal features. Then, to ensure long-term positioning stability and continual environmental aware capability, we incorporate a dynamic replay memory into ENAP that enables the unsupervised online updating of positioning models, distinguishing our proposal from most existing positioning models. Simulation and experimental results demonstrate the effectiveness and superiority of the proposed ENAP approach as a practical and efficient solution for wireless indoor positioning in the IoT era.