Participant selection algorithms for large-scale mobile crowd sensing environment

Mobile crowd sensing (MCS) is an emerging sensing platform that concedes mobile users to efficiently collect data and share information with the MCS service providers. Despite its benefits, a key challenge in MCS is how beneficially select a minimum subset of participants from the large user pool to achieve the desired level of coverage. In this paper, we propose several algorithms to choose a minimum number of mobile users(or participants) who met the desired level of coverage. We consider two different cases, in the first case, only a single participant is allowed to upload a data packet for a particular target, whereas for the other case, two participants are allowed to do the same (provided that the target is covered by more than one participants). An optimal solution to the problem can be found by solving integer linear programmings (ILP's). However, due to the exponential complexity of the ILP problem, for the large input size, it is infeasible from the point of execution time as well as the requirement of having the necessary information about all the participants in a central location. We also propose a distributed participant selection algorithm considering both the cases, which are dynamic in nature and run at every user. Each user exchanges their message with the neighbors to decide whether to remain idle or active. A series of experiments are executed to measure the performance of the proposed algorithms. Simulation results reveal the proximity of the proposed distributed algorithm compared to the optimal result providing the same coverage.