Duration-Sensitive Task Allocation for Mobile Crowd Sensing

In mobile crowd sensing, task allocation is of vital importance, and it has attracted much attention in recent years. Though there have been many studies focusing on task allocation, rare works took sensing duration of tasks into consideration. However, sensing duration plays a key role for the success of many sensing tasks. For example, when the crowd sensing system needs to monitor the crowd flow in locations of interest, it is better to allocate this task to workers who can record a video of certain duration rather than those who can only take a picture. In this article, we try to solve this problem by designing a duration-sensitive task allocation model, where each task is associated with a specific sensing duration. The model aims at maximizing the number of completed tasks under the constraints of sensing duration and task capacity of each worker. To find an efficient task allocation scheme for the model, we design a utility function that can reflect the probability of task completion by using the exponential distribution. Then, an efficient greedy heuristic is proposed based on the utility function. Extensive evaluations based on the simulated and real-world datasets demonstrate that the proposed algorithm outperforms the baseline methods.