摘要
The ubiquitous deployment of GPS-equipped devices and mobile networks has spurred the popularity of spatial crowdsourcing. Many spatial crowdsourcing tasks require crowd workers to collect data from different locations. Since workers tend to select locations nearby or align to their routines, data collected by workers are usually unevenly distributed across the region. To encourage workers to choose remote locations so as to avoid imbalanced data collection, we investigate the incentive mechanisms in spatial crowdsourcing. We propose a price adjustment function and two algorithms, namely DFBA (Dynamic Fixed Budget Allocation) and DABA (Dynamic Adjusted Budget Allocation), which utilize price leverage to mitigate the imbalanced data collection problem. Extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed incentive mechanisms are able to effectively balance the popularity of different locations.
The ubiquitous deployment of GPS-equipped devices and mobile networks has spurred the popularity of spatial crowdsourcing. Many spatial crowdsourcing tasks require crowd workers to collect data from different locations. Since workers tend to select locations nearby or align to their routines, data collected by workers are usually unevenly distributed across the region. To encourage workers to choose remote locations so as to avoid imbalanced data collection, we investigate the incentive mechanisms in spatial crowdsourcing. We propose a price adjustment function and two algorithms, namely DFBA (Dynamic Fixed Budget Allocation) and DABA (Dynamic Adjusted Budget Allocation), which utilize price leverage to mitigate the imbalanced data collection problem. Extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed incentive mechanisms are able to effectively balance the popularity of different locations.
