Abstract
In Spatial crowdsourcing, mobile users perform spatio-temporal tasks that involve travel to specified locations. Spatial crowdsourcing (SC) is enabled by SC platforms that support mobile worker recruitment and retention, as well as task assignment, which is essential to maximize profits that are accrued from serving task requests. Specifically, how to best achieve task assignment in a cost-effective manner while contending with spatio-temporal constraints is a key challenge in SC. To address this challenge, we formalize and study a novel Profit-driven Task Assignment problem. We first establish a task reward pricing model that takes into account the temporal constraints (i.e., expected completion time and deadline) of tasks. Then we adopt an optimal algorithm based on tree decomposition to achieve an optimal task assignment and propose greedy algorithms based on Random Tuning Optimization to improve the computational efficiency. To balance effectiveness and efficiency, we also provide a heuristic task assignment algorithm based on Ant Colony Optimization that assigns tasks by simulating behavior of ant colonies foraging for food. Finally, we conduct extensive experiments using real and synthetic data, offering detailed insight into effectiveness and efficiency of the proposed methods.
Original language | English |
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Article number | 9941474 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 35 |
Issue number | 8 |
Pages (from-to) | 8386-8401 |
Number of pages | 16 |
ISSN | 1041-4347 |
DOIs | |
Publication status | Published - 1 Aug 2023 |
Bibliographical note
Publisher Copyright:IEEE
Keywords
- Behavioral sciences
- Computational modeling
- Costs
- Crowdsourcing
- Optimization
- Pricing
- Profit
- Task analysis
- spatial crowdsourcing
- task assignment
- Spatial crowdsourcing
- profit