Inferring mobile trajectories using a network of binary proximity sensors

Understanding human mobility in an environment can be approached in many forms, one of which is to recover the underlying structure of user movement. In our work, we show that we can use a network of binary proximity sensors to detect paths between nodes and also extract highly popular trajectories users take. We show that with sufficient amount of these binary data, even with no prior knowledge of the location of these sensors, we can capture a correlation between the detection timestamps in the case where a physical path exists between any two nodes. Our algorithm also generates characteristics of the path, such as the distribution of transition times and volume. We further show that with sampling techniques we can estimate the underlying trajectories that generated the time stamps. We have tested our algorithm on a simulator and two sensor network deployments. We found that, despite the lack of position information about the sensor nodes, with timestamps alone our algorithm can accurately detect the trajectories and is robust enough to use in a real-world office building.