Understanding spatial and temporal coverage in participatory sensor networks

Sensor coverage is a well established problem in sensor networks. Most of the work is focused on optimizing coverage in stationary networks or by controlling the movement of mobile nodes (e.g. robots) in order to maximize their coverage. In participatory sensor networks, we are faced with noncontrollable mobility. Humans move freely and there is no central nor distributed algorithm that optimizes coverage. There is no work in literature yet that explores coverage in the context of non-controllable mobility. To this end, we report results of a study applying an adapted greedy coverage algorithm onto three different data sets. Given these datasets, we report results studying the effect of different mobility characteristics on the spatial and temporal coverage. Our results show that high coverage can be achieved by a relatively small subset of nodes. Also, given a real-world participatory sensing system, turn-around times are relevant for continuous temporal coverage.