Anomalous Path Detection for Spatial Crowdsourcing-Based Indoor Navigation System

Indoor navigation system provides customized path planning for requesters who are unfamiliar with the indoor environment, such as shopping mall and airport. Spatial crowd-sourcing technology can be applied to indoor navigation to offer fundamental services related to location. However, spatial crowdsourcing-based indoor navigation is vulnerable to the intrusion of injected anomalous paths from attackers. In this paper, we propose an anomalous path detection (APD) scheme to classify attackers according to their reputation management and abnormal trajectory sequence. Specifically, we first develop a crowdsourcing system to support the indoor location service using the fog as the spatial crowdsourcing server. Then, we identify two levels of attackers, i.e., the malicious responders and the semi-honest responders in the indoor environment according to their attacking purposes. Through the responders' historical records from the fog server, we analyze a series of trajectory sequences consisting of the distance between the current position and the destination to distinguish the semi-honest responders from the normal. In addition, we propose a semi-supervised learning with hidden Markov model (HMM) to detect the semi-honest responders. Finally, the extensive simulations show that the APD scheme can achieve higher accuracy with the acceptable false rate.