Special Issue "Spatial Crowdsourcing"
Deadline for manuscript submissions: 31 December 2023 | Viewed by 559
Interests: spatial crowdsourcing; data mining; machine learning; deep learning; trajectory analytics
Interests: spatiotemporal databases; trajectory computing; spatial crowdsourcing; recommendation systems
Crowdsourcing is a computing paradigm where humans actively or passively participate in the procedure of computing, especially for tasks that are intrinsically easier for humans than for computers. Many successful crowdsourcing platforms exist, e.g., Amazon Mechanical Turk (MTurk) and Wikipedia. Along with the ubiquity of GPS-equipped networked devices such as smartphones, a new class of crowdsourcing, called spatial crowdsourcing (SC), has drawn increasing attention in both academia and industry. With SC, requesters can issue spatial tasks (e.g., monitoring traffic conditions or picking up passengers) to SC servers that then assign workers to these tasks (called task assignment). The workers complete their tasks by physically moving to the specified locations. Spatiotemporal information (e.g., location, mobility, and the associated contexts) plays a crucial role in SC. Due to its natural connection to the physical world, SC is relevant to a wide spectrum of daily applications, including real-time ride-hailing services (e.g., Uber), on-wheel meal-ordering services (e.g., GrubHub), and public transportation services (e.g., Flex Traffic). These applications need specialized algorithms to accomplish effective and efficient task assignment.
This Special Issue on “Spatial Crowdsourcing” aims to engage with active researchers from spatiotemporal data management and spatial crowdsourcing communities to deliver state-of-the-art research insights into the effective and efficient task assignment process in spatial crowdsourcing. The Special Issue welcomes outstanding research papers, as well as review articles, devoted to innovative suggestions for AI-based or advanced technologies in the field of spatial crowdsourcing.
In particular, the topics of interest include, but are not limited to:
- Task assignment methodologies: methods and methodologies used for effective and efficient task assignment.
- Privacy-aware task assignment: areas include, but are not limited to, federated learning, new privacy protection mechanisms for trustworthy spatial crowdsourcing, and differentially private applications.
- Behavior analysis: understanding worker or task-requester behaviors in the context of spatial crowdsourcing.
- Fairness: a fair spatial crowdsourcing system is designed to balance its effective task assignment with potential biases caused by factors such as worker demographics, task exposure/popularity, etc.
- Surveys, evaluations, or benchmarking: surveys, evaluations, or benchmarking on state-of-the-art research in the area of spatial crowdsourcing.
Dr. Yan Zhao
Prof. Dr. Kai Zheng
Dr. Yawen Li
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- spatial crowdsourcing
- task assignment
- privacy protection
- behavior analysis