Location-Dependent Task Allocation for Mobile Crowdsensing With Clustering Effect

Mobile crowdsensing (MCS) offers a promising paradigm for large-scale sensing with the rapid growth of mobile smart devices. Compared with traditional sensing methods, MCS is more effective and efficient in energy and cost. Task allocation is a key problem in MCS, which has a significant impact on the performance. It is challenging to design a generic solution to the task allocation problem because MCS applications typically consider distinct targets under specific constraints. However, there are many common interests such as data quality, budget, and energy consumption. In this paper, we analyze and formulate the task allocation problem from two perspectives, respectively. First, we focus on data quality and propose a genetic algorithm (GA) to maximize data quality. Then, we take the profit of workers into account and propose a detective algorithm (DA) to improve the profit. In the GA-based solution, only the platform is able to decide the task assignment. However, in the DA-based solution, the workers are allowed to determine and submit their task sets to the platform, which just needs to make a selection from these task sets. In addition, we consider the clustering effect of tasks and the influence caused by different geographic distributions of tasks. To evaluate the performance of the proposed solutions, extensive simulations are conducted. The results demonstrate that our proposed solutions outperform the baseline algorithm and there is a tradeoff between the data quality and the profit of workers.