Topology Construction Based on Indoor Crowdsourcing Data using Manifold Learning: Evaluation of Algorithms and Key Parameters

Abstract

Fingerprint matching localization based on indoor ubiquitous wireless signal have been widely used in indoor localization. However, it’s labor-intensive to collect and update the radio map which consists of fingerprints, while crowdsourcing might be a potential way to solve the problem due to the popularization of smartphones. The radio map construction with crowdsourcing can be regarded as a topology construction problem, and manifold learning is popularly used to construct the topology from high-dimensional fingerprint space to two-dimensional plane. The challenge is how to obtain accurate location labels and construct the radio map from crowdsourcing data without any position information. In this paper, the performance of three typical manifold learning algorithms was compared, including the effectiveness of topology construction, the robustness to noise and the preservation of global and local structures. A comprehensive evaluation method was used to find the optimal topology construction algorithm and corresponding parameters for different scenes. Experimental results show that LLE and Isomap are suitable for simple indoor scenes and t-SNE is much more robust for complex scenes.