User Recruitment Strategy for Maximizing Coverage in Mobile Crowdsensing

Over the last decade, mobile crowdsensing has been an effective way to collect data by utilizing users’ smart devices to collectively perform a large-scale sensing job. In this paper, we focus on the coverage problem of the user recruitment. In mobile crowdsensing, mobile users move on different paths among places; we focus on the problem that how users can be optimally selected in order to efficiently collect information from the sensing area. There are two goals in this paper: one is to select users at minimum cost when we can easily cover all points of interest (PoIs) in a designated area; the other is to maximize the coverage when cost is limited (recruiting the fixed number of users). We consider the scenarios with deterministic user mobility and formulate the user selection as a set cover problem with a submodular objective function. Obviously, the set cover problem is NP-hard and we propose the corresponding practical greedy heuristics and also infer their approximation ratios. Our simulation experiments with real datasets provide the performance of the greedy heuristics. The results satisfy the inferred approximation ratios to the optimal solution and achieve the better performances than the other compared solutions.