LAUSR

With the rapid increasing of smart phones and the advances of embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for large-scale sensing applications. One of the key challenges of large-scale mobile crowd sensing is how to effectively select the minimum set of participants from the huge user pool to perform the tasks and achieve a certain level of coverage while satisfying some constraints. This becomes more complex when the sensing tasks are dynamic (coming in real time) and heterogeneous (with different temporal and spacial coverage requirements). In this paper, we consider such a dynamic participant selection problem with heterogeneous sensing tasks which aims to minimize the sensing cost while maintaining certain level of probabilistic coverage. Both offline and online algorithms are proposed to solve the challenging problem. Extensive simulations over a real-life mobile dataset confirm the efficiency of the proposed algorithms.