Abstract
One of the fundamental features of the Internet of Things (IoT) is the ability to track its users location over time as a way to provide service in the right context. While many localization and tracking techniques have been proposed using cameras and RF-based techniques,they are ultimately limited by the ability to deploy tracking infrastructure and cannot operate when outside coverage area. To overcome this limitation, we propose encounter-based cooperative tracking for proximity-enabled wireless nodes. These nodes, carried by the subjects being tracked, roam inside a mapped area and can sense their trajectories (e.g., by inertial sensing) and each other's proximity but without access to localizing infrastructure such as beacons. We model their locations over time on the map as particles and apply particle filtering as the basis for tracking. Our contribution is to augment the base technique with encounter information to dramatically reduce the number of possible particles in the model by spatial-temporal intersection. Experimental results show our techniques to be effective even in maps that contain layouts that have many possible matches for given trajectories.