School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Sensors (Basel). 2022 Apr 14;22(8):3013. doi: 10.3390/s22083013.
Mobile crowd-sensing (MCS) is a well-known paradigm used for obtaining sensed data by using sensors found in smart devices. With the rise of more sensing tasks and workers in the MCS system, it is now essential to design an efficient approach for task allocation. Moreover, to ensure the completion of the tasks, it is necessary to incentivise the workers by rewarding them for participating in performing the sensing tasks. In this paper, we aim to assist workers in selecting multiple tasks while considering the time constraint of the worker and the requirements of the task. Furthermore, a pricing mechanism is adopted to determine each task budget, which is then used to determine the payment for the workers based on their willingness factor. This paper proves that the task-allocation is a non-deterministic polynomial (NP)-complete problem, which is difficult to solve by conventional optimization techniques. A worker multitask allocation-genetic algorithm (WMTA-GA) is proposed to solve this problem to maximize the workers welfare. Finally, theoretical analysis demonstrates the effectiveness of the proposed WMTA-GA. We observed that it performs better than the state-of-the-art algorithms in terms of average performance, workers welfare, and the number of assigned tasks.
移动众包感知 (MCS) 是一种通过使用智能设备中的传感器获取感知数据的知名范例。随着 MCS 系统中更多的感知任务和工作人员的出现，现在必须设计一种有效的任务分配方法。此外，为了确保任务的完成，有必要通过奖励工作人员参与执行感知任务来激励他们。在本文中，我们旨在帮助工作人员在考虑工作人员的时间限制和任务要求的情况下选择多个任务。此外，采用定价机制来确定每个任务的预算，然后根据工作人员的意愿因素确定对其的支付。本文证明了任务分配是一个非确定性多项式 (NP) 完全问题，这很难通过传统的优化技术来解决。提出了一种工作人员多任务分配遗传算法 (WMTA-GA) 来解决这个问题，以最大限度地提高工作人员的福利。最后，理论分析证明了所提出的 WMTA-GA 的有效性。我们观察到，它在平均性能、工作人员福利和分配任务数量方面都优于最先进的算法。
