Spatial crowdsourcing: a survey
Yongxin Tong,Zimu Zhou,Yuxiang Zeng,Lei Chen,Cyrus Shahabi +4 more
- Vol. 29, Iss: 1, pp 217-250
TLDR
A comprehensive and systematic review of existing research on four core algorithmic issues in spatial crowdsourcing: (1) task assignment, (2) quality control, (3) incentive mechanism design, and (4) privacy protection.Abstract:
Crowdsourcing is a computing paradigm where humans are actively involved in a computing task, especially for tasks that are intrinsically easier for humans than for computers. Spatial crowdsourcing is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy, where tasks are spatiotemporal and must be completed at a specific location and time. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including sharing economy for urban services (Uber and Gigwalk) and spatiotemporal data collection (OpenStreetMap and Waze). This survey dives deep into the challenges and techniques brought by the unique characteristics of spatial crowdsourcing. Particularly, we identify four core algorithmic issues in spatial crowdsourcing: (1) task assignment, (2) quality control, (3) incentive mechanism design, and (4) privacy protection. We conduct a comprehensive and systematic review of existing research on the aforementioned four issues. We also analyze representative spatial crowdsourcing applications and explain how they are enabled by these four technical issues. Finally, we discuss open questions that need to be addressed for future spatial crowdsourcing research and applications.read more
Citations
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Proceedings ArticleDOI
Differentially Private Online Task Assignment in Spatial Crowdsourcing: A Tree-based Approach
TL;DR: A novel privacy mechanism based on Hierarchically Well-Separated Trees (HSTs) is designed and extensive experiments show that online task assignment under this privacy mechanism is notably more effective in terms of total distance than under prior differentially private mechanisms.
Journal ArticleDOI
Two-Sided Online Micro-Task Assignment in Spatial Crowdsourcing
TL;DR: A two-phase-based framework is proposed, based on which the TGOA algorithm with a -competitive ratio under the random order model is presented and the average performance of Greedy is considered, which has been considered as the worst due to its unbounded competitive ratio in the worst case.
Journal ArticleDOI
Last-mile delivery made practical: an efficient route planning framework with theoretical guarantees
TL;DR: This paper proposes a theoretically guaranteed solution framework for last-mile delivery made practical: an Efficient Route Planning Framework with Theoretical Guarantees that outperforms state-of-the-art methods for both objectives.
Top-k team Recommendation in Spatial Crowdsourcing
TL;DR: Zhang et al. as mentioned in this paper proved that the problem is NP-hard and designed a two-level-based framework, which includes an approximation algorithm with provable approximation ratio and an exact algorithm with pruning techniques to address it.
Journal ArticleDOI
Trajectory Outlier Detection: Algorithms, Taxonomies, Evaluation, and Open Challenges
TL;DR: Detecting abnormal trajectories is an important task in research and industrial applications, which has attracted considerable attention in recent decades and is studied in detail in this work.
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