Mobility Intelligence: Machine Learning Methods for Received Signal Strength Indicator-based Passive Outdoor Localization - Fanchen Bao; Stepan Mazokha; Jason O. Hallstrom (Advances in Science, Technology and Engineering Systems Journal, 2022)
Knowledge of pedestrian and vehicle movement patterns can provide valuable insights for city planning. Such knowledge can be acquired via passive outdoor localization of WiFi-enabled devices using measurements of Received Signal Strength Indicator (RSSI) from WiFi probe requests. In this paper, which is an extension of the work initially presented in WiMob 2021, we continue the work on the mobility intelligence system (MobIntel) and study two broad approaches to tackle the problem of RSSI-based passive outdoor localization. One approach concerns multilateration and fingerprinting, both adapted from traditional active localization methods. For fingerprinting, we also show flaws in the previously reported area-under-the-curve method. The second approach uses machine learning, including machine learning-boosted multilateration, reference point classification, and coordinate regression. The localization performance of the two approaches is compared, and the machine learning methods consistently outperform the adapted traditional methods. This indicates that machine learning methods are promising tools for RSSI-based passive outdoor localization.
Keywords: Passive outdoor localization; RSSI; WiFi probe requests; Mobility intelligence