AP Optimization for Wi-Fi Indoor Positioning-Based on RSS Feature Fuzzy Mapping and Clustering

Abstract

In indoor environments, Access Points (APs) are widely deployed in various locations of buildings, and thereby the AP optimization-based Wi-Fi indoor positioning technology is of great significance for achieving the satisfactory indoor Location-based Services (LBSs). However, the current Wi-Fi indoor positioning methods rarely pay attention to the diversity of Received Signal Strength (RSS) features for AP optimization, which may result in the low positioning accuracy and high positioning overhead. In order to deal with such issues, this article proposes a new concept of multi-dimensional RSS feature fuzzy mapping and clustering for AP optimization in Wi-Fi indoor positioning. Besides, the extensive experiments conducted in an actual indoor environment show that compared with the existing positioning methods, the proposed method can not only achieve higher positioning accuracy by using the optimized APs but also reduce the positioning overhead in the online phase.