Abstract
Mobile crowdsensing (MCS) is a popular paradigm to collect sensed data for numerous sensing applications. With the increment of tasks and workers in MCS, it has become indispensable to design efficient task allocation schemes to achieve high performance for MCS applications. Many existing works on task allocation focus on single-task allocation, which is inefficient in many MCS scenarios where workers are able to undertake multiple tasks. On the other hand, many tasks are time-limited, while the available time of workers is also limited. Therefore, time validity is essential for both tasks and workers. To accommodate these challenges, this paper proposes a multi-task allocation problem with time constraints, which investigates the impact of time constraints to multi-task allocation and aims to maximize the utility of the MCS platform. We first prove that this problem is NP-complete. Then two evolutionary algorithms are designed to solve this problem. Finally, we conduct the experiments based on synthetic and real-world datasets under different experiment settings. The results verify that the proposed algorithms achieve more competitive and stable performance compared with baseline algorithms.
Highlights
IN recent years, mobile crowdsensing (MCS) [1] is becoming a popular sensing paradigm to take advantage of the collective sensing capabilities of the large population of mobile users
Given time constraints of workers and tasks, the competition among workers and tasks become more complex when designing efficient task allocation methods. Considering these challenging scenarios, we propose efficient multi-task allocation schemes with time constraints based on genetic algorithm (GA) [23], which has been proved to be efficient in solving complex combination optimization problems as illustrated above
We evaluate the performance of the algorithms with respect to the utility of the MCS platform, the number of allocated tasks, and the average number of tasks allocated to each worker
Summary
IN recent years, mobile crowdsensing (MCS) [1] is becoming a popular sensing paradigm to take advantage of the collective sensing capabilities of the large population of mobile users. A user can play the role of information requester by actively asking some mobile users to collect sensed data to fulfill their requirements. There are three roles in MCS applications: workers (i.e., users who undertake sensing tasks), requesters (i.e., users who send task requests), and the MCS platform. The MCS platform is responsible for allocating sensing tasks to suitable workers and integrating sensed data for the corresponding task requesters. Considering that the numbers of workers and tasks can be rather large, a proper task allocation scheme is important to match workers with tasks, such that the MCS applications can operate efficiently and improve user stickiness
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