Abstract
With the development of sensor technology and the popularization of the data-driven service paradigm, spatial crowdsourcing systems have become an important way of collecting map-based location data. However, large-scale task management and location privacy are important factors for participants in spatial crowdsourcing. In this paper, we propose the use of an R-tree spatial cloaking-based task-assignment method for large-scale spatial crowdsourcing. We use an estimated R-tree based on the requested crowdsourcing tasks to reduce the crowdsourcing server-side inserting cost and enable the scalability. By using Minimum Bounding Rectangle (MBR)-based spatial anonymous data without exact position data, this method preserves the location privacy of participants in a simple way. In our experiment, we showed that our proposed method is faster than the current method, and is very efficient when the scale is increased.
Highlights
With the development of sensor and data communication technologies, smart devices can collect real-time environmental information
We propose an R-Tree spatial cloaking-based task assignment method as a spatial crowdsourcing system
We introduce our proposed method: the R-tree spatial cloaking-based task assignment method
Summary
With the development of sensor and data communication technologies, smart devices can collect real-time environmental information. As real-time data and data analysis have become important in the data-driven paradigm, various methods of data collection have been studied in recent research [1]. In existing sensor networks, when collecting real-time environmental data, we must deploy many sensors in the area where the measurement is needed. A crowdsourcing system can collect the necessary data directly from volunteer participants with smart devices and deliver it to a crowdsourcing server. This makes it easy to collect data once or over a short period with little expense and greater accuracy compared to current sensor networks [2]
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