Truthful Mechanism Design for Multiregion Mobile Crowdsensing. (19th August 2020)

• Record Type: Journal Article
• Authors: Qiao, Yu; Wu, Jun; Cheng, Hao; Huang, Zilan; He, Qiangqiang; Wang, Chongjun
• Other Names: Yu Wei Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2020(2020)
• Publication Date: 2020-08-19
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2020/8834983
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : In the age of the development of artificial intelligence, we face the challenge on how to obtain high-quality data set for learning systems effectively and efficiently. Crowdsensing is a new powerful tool which will divide tasks between the data contributors to achieve an outcome cumulatively. However, it arouses several new challenges, such as incentivization. Incentive mechanisms are significant to the crowdsensing applications, since a good incentive mechanism will attract more workers to participate. However, existing mechanisms failed to consider situations where the crowdsourcer has to hire capacitated workers or workers from multiregions. We design two objectives for the proposed multiregion scenario, namely, weighted mean and maximin. The proposed mechanisms maximize the utility of services provided by a selected data contributor under both constraints approximately. Also, extensive simulations are conducted to verify the effectiveness of our proposed methods.