Abstract
With the rapid development of mobile networks and the proliferation of mobile devices, spatial crowdsourcing, which refers to recruiting mobile workers to perform location-based tasks, has gained emerging interest from both research communities and industries. In this paper, we consider a spatial crowdsourcing scenario: in addition to specific spatial constraints, each task has a valid duration, operation complexity, budget limitation, and the number of required workers. Each volunteer worker completes assigned tasks while conducting his/her routine tasks. The system has a desired task probability coverage and budget constraint. Under this scenario, we investigate an important problem, namely heterogeneous spatial crowdsourcing task allocation (HSC-TA), which strives to search a set of representative Pareto-optimal allocation solutions for the multi-objective optimization problem, such that the assigned task coverage is maximized, and incentive cost is minimized simultaneously. To accommodate the multi-constraints in heterogeneous spatial crowdsourcing, we build a worker mobility behavior prediction model to align with allocation process. We prove that the HSC-TA problem is NP-hard. We propose effective heuristic methods, including multi-round linear weight optimization and enhanced multi-objective particle swarm optimization algorithms to achieve adequate Pareto-optimal allocation. Comprehensive experiments on both real-world and synthetic data sets clearly validate the effectiveness and efficiency of our proposed approaches.
Recommended Citation
L. Wang et al., "Multi-Objective Optimization based Allocation of Heterogeneous Spatial Crowdsourcing Tasks," IEEE Transactions on Mobile Computing, vol. 17, no. 7, pp. 1637 - 1650, Institute of Electrical and Electronics Engineers, Jul 2018.
The definitive version is available at https://doi.org/10.1109/TMC.2017.2771259
Department(s)
Computer Science
Keywords and Phrases
Heterogeneous spatial crowdsourcing; mobility prediction; multi-objective optimization; particle swarm optimization
International Standard Serial Number (ISSN)
1536-1233
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jul 2018
Comments
National Natural Science Foundation of China, Grant 61402360