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Article

Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing

Department of Computer Engineering, Inha University, #100 Inha-ro, Nam-gu, Incheon 22212, Korea
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Symmetry 2017, 9(12), 311; https://doi.org/10.3390/sym9120311
Received: 30 September 2017 / Revised: 5 December 2017 / Accepted: 7 December 2017 / Published: 10 December 2017
(This article belongs to the Special Issue Advanced in Artificial Intelligence and Cloud Computing)
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. View Full-Text
Keywords: spatial cloaking; R-tree; large scale; spatial crowdsourcing spatial cloaking; R-tree; large scale; spatial crowdsourcing
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MDPI and ACS Style

Li, Y.; Shin, B.-S. Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing. Symmetry 2017, 9, 311. https://doi.org/10.3390/sym9120311

AMA Style

Li Y, Shin B-S. Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing. Symmetry. 2017; 9(12):311. https://doi.org/10.3390/sym9120311

Chicago/Turabian Style

Li, Yan, and Byeong-Seok Shin. 2017. "Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing" Symmetry 9, no. 12: 311. https://doi.org/10.3390/sym9120311

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