Multitask-Oriented Participant Selection in Mobile Crowd Sensing

Participant selection or task allocation is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multitask-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper studies the task allocation issue in MCS, and we propose a new multitask-oriented participant selection problem. The difference between our work and other studies in participant selection is that each participant can complete as many tasks as possible within the given time period, rather than merely one task. This participant selection problem has some advantages: First, each participant can complete multiple tasks, and this will bring benefits to the running of the MCS platform. It is particularly useful in cases that user resources are not sufficient regarding to the published tasks. Second, there exist geographical proximity among the published tasks, and we can optimize the MCS platform performance by assigning the tasks as a whole. Third, each participant can perform as many tasks as possible, and this may improve personal income and participation enthusiasm. To address this participant selection issue, we propose MultiTasker, the objective of which is to minimize the total distance the selected participants move while minimizing the number of participants, subjecting to that all tasks are allocated with the needed number of participants and the tasks are completed by each selected participant within the given time period. In order to achieve the objective, we propose three algorithms: T-Random, T-Most and PT-Most. T-Random and T-Most select participants in a task-centric way, while PT-Most selects participants in a people-centric manner. We evaluated the three algorithms using a large-scale real-world dataset, and studied the relationship between participants and other factors, such as the distribution of tasks and task completion time.