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Spatial crowdsourcing based on Web mapping services

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Abstract

With the rapid development of mobile Internet and smartphones, crowdsourcing marketplaces has extended to spatial crowdsourcing (SC), where crowd workers perform spatial tasks (i.e., tasks related to a location) in the physical world using their mobile phones. Currently, most of existing spatial crowdsourcing algorithms (e.g., task assignment) assume the underlying road network is given or simply base on Euclidean space. However, in fact, not every spatial crowdsourcing platform has enough resources to possess map data (e.g., the road network and live traffic information) by itself, especially for these that are small or startup companies, while the Euclidean distance is usually not accurate enough for SC processing. To overcome these limitations, we propose a spatial crowdsourcing system based on Web mapping services, i.e., the spatial crowdsourcing platform can subscribe distance, live travel time and detailed route information from Web mapping services through their APIs, and utilize these retrieved map data for SC processing; furthermore, workers and task requesters can also snap their real locations to the locations on the road for privacy protection through the APIs. As retrieving map data from Web mapping services is much more expensive than accessing local data, we take the advantage of the pruning and route sharing approaches to reduce the number of external requests to Web mapping services, and our experimental results have proved their effectiveness.

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Notes

  1. https://developers.google.com/maps/documentation/roads/intro

  2. https://msdn.microsoft.com/en-us/library/mt814926.aspx

  3. https://developers.google.com/maps/documentation/roads/intro

  4. https://docs.microsoft.com/en-us/bingmaps/rest-services/routes/calculate-a-distance-matrix

  5. https://developers.google.com/maps/documentation/directions/start

  6. https://docs.microsoft.com/en-us/bingmaps/rest-services/routes/calculate-a-route

  7. https://developers.google.com/maps/terms?hl=en

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Project 61702227, 61802344, 61632016, 61572335 and 61602214, in part by Zhejiang Provincial Natural Science Foundation of China with No.LY16F030012, in part by Humanities and Social Science Foundation of Ministry of Education of China with No.16YJCZH112, in part by the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003), in part by the Natural Science Foundation of Jiangsu Province under Project BK20160191, in part by the Ningbo Natural Science Foundation under Grant 2017A610118, and in part by the Ningbo Innovative Team project under Grant 2016C11024.

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Authors and Affiliations

  1. Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Suzhou, China

    Detian Zhang, Zhixu Li & Lei Zhao

  2. Ningbo Institute of Technology, Zhejiang University, Ningbo, China

    Shiting Wen

  3. College of Computer Science Technology, Qingdao University, Qingdao, China

    Fei Chen

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  1. Detian Zhang
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Correspondence to Shiting Wen.

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This article belongs to the Topical Collection: Special Issue on Trust, Privacy, and Security in Crowdsourcing Computing

Guest Editors: An Liu, Guanfeng Liu, Mehmet A. Orgun, and Qing Li

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Zhang, D., Wen, S., Chen, F. et al. Spatial crowdsourcing based on Web mapping services. World Wide Web 23, 631–648 (2020). https://doi.org/10.1007/s11280-019-00708-7

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