Abstract
The UAV mobile crowd sensing problem is a new research area due to the flexibility and low-cost advantage of UAVs. Current research rarely considers the task assignment and path planning problem simultaneously. In this paper, we describe the improved UAV mobile crowed sensing model that takes both the task assignment and path planning into consideration, meanwhile the model also considered the limit of UAV's power. In our paper, a task location distribution based genetic algorithm is proposed to solve the problem more efficiently. A series of instances involving different number of tasks and UAV bases is used in the paper. The results of the experiment indicate that our proposed method is efficient and can deal with large-scale problems. This research has resulted in a solution of the UAV mobile crowd sensing problem and can provide ideas to similar problems.
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Huang, Y., Luo, A., Zhang, M., Bai, L., Song, Y., Li, J. (2023). Task Location Distribution Based Genetic Algorithm for UAV Mobile Crowd Sensing. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_14
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DOI: https://doi.org/10.1007/978-981-99-1549-1_14
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