Abstract
Task allocation is a key technology in the research of mobile crowdsensing. The previous research only focused on single-task allocation, and seldom considered the monopoly nature of tasks, quality requirements, and the constraint relationship between tasks. This paper comprehensively considers the above factors and designs a multi-task allocation scheme for mobile crowdsensing to maximize the profit of the service platform. First, divide the tasks into monopoly tasks and non-monopoly tasks, and judge whether they will be executed according to the profit that monopoly tasks can bring to the platform; For non-monopoly tasks, an efficient allocation plan is designed based on genetic algorithm and greedy algorithm; Secondly, considering the quality requirements of tasks and the constraint relationship between tasks, comparing the existing classic task allocation schemes, simulation experiments verify that the proposed algorithm has better effects in terms of platform profit and task coverage.
Supported by the National Natural Science Foundation of China (61672022, U1904186), Shanghai Second Polytechnic University Key Discipline Electronic Information Special Master Program Project (XXKZD1604).
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Liu, J., Tan, W., Liang, Z., Ding, K. (2022). Multi-task Allocation Under Multiple Constraints in Mobile Crowdsensing. In: Zu, Q., Tang, Y., Mladenovic, V., Naseer, A., Wan, J. (eds) Human Centered Computing. HCC 2021. Lecture Notes in Computer Science, vol 13795. Springer, Cham. https://doi.org/10.1007/978-3-031-23741-6_17
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