Abstract
Route-oriented participants recruitment is a critical problem in collaborative crowdsensing, where task publisher uses monetary reward to motivate private cars collecting data along their routes. For map producers, route-oriented crowdsensing scheme helps them achieve maximum roads coverage with a limited budget, by selecting appropriate participants from a group of candidates.
Focused on route-oriented participants recruitment problem, this paper first formalizes the road network and vehicle route model. Each vehicle’s route is mapped to a coverage rate on the road set. The recruitment problem therefore transforms to a combinatorial optimization problem, which has proved to be NP-hard. To find a solution, we proposed an approximation algorithm, which leverages submodularity to reduce computation complexity and has a worst performance guarantee. Finally we evaluate the performance of proposed algorithm on real road and trajectory data in Beijing, China.
This work is supported by the National Science and Technology Major Project of China under Grant No. 2016ZX03001025-003 and Special found for Beijing Common Construction Project.
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Yang, S., Li, J., Yuan, Q., Liu, Z. (2018). Route-Oriented Participants Recruitment in Collaborative Crowdsensing. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_16
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