ppNav: Peer-to-Peer Indoor Navigation for Smartphones

Most of existing indoor navigation systems work in a client/server manner, which needs to deploy comprehensive localization services together with precise indoor maps a prior. In this paper, we design and realize a Peer-to-Peer navigation system, named ppNav, on smartphones, which enables the fast-to-deploy navigation services, avoiding the requirements of pre-deployed location services and detailed floorplans. ppNav navigates a user to the destination by tracking user mobility, promoting timely walking tips, and alerting potential deviations, according to a previous traveller's trace experience. Specifically, we utilize the ubiquitous WiFi fingerprints in a novel diagrammed form and extract both radio and visual features of the diagram to track relative locations and exploit fingerprint similarity trend for deviation detection. Consolidating these techniques, we implement ppNav on commercial mobile devices and validate its performance in real environments. Our results show that ppNav achieves delightful performance, with an average relative error of 0.9m in trace tracking and a maximum delay of 9 samples (about 4.5s) in deviation detection.