Improving Both Quantity and Quality: Incentive Mechanism for Social Mobile Crowdsensing Architecture
Mobile crowdsensing has emerged as an efficient paradigm for performing large-scale sensing tasks. Improving both the quantity and quality of users is still the pivotal problem for mobile crowdsensing system. This paper gives a comprehensive solution to improve the quantity and quality of users simu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8421568/ |
| _version_ | 1818913124881793024 |
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| author | Jia Xu Weiwei Bao Huayue Gu Lijie Xu Guoping Jiang |
| author_facet | Jia Xu Weiwei Bao Huayue Gu Lijie Xu Guoping Jiang |
| author_sort | Jia Xu |
| collection | DOAJ |
| description | Mobile crowdsensing has emerged as an efficient paradigm for performing large-scale sensing tasks. Improving both the quantity and quality of users is still the pivotal problem for mobile crowdsensing system. This paper gives a comprehensive solution to improve the quantity and quality of users simultaneously through the social mobile crowdsensing architecture. To incentive the users based on the novel architecture, we first propose a universal initial diffuser selection algorithm to accommodate two widely studied diffusion models and, then a lightweight, multi-metric comprehensive, and parameter-free user quality evaluation method is presented. Finally, we propose a reverse auction to optimize the new criterion, which takes both social cost and user quality into consideration. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Meanwhile, the proposed incentive mechanisms show prominent advantage in total unit quality cost and running time. |
| first_indexed | 2024-12-19T23:25:30Z |
| format | Article |
| id | doaj.art-5097c9f5e1c14de8befa7cfc9fc17d88 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T23:25:30Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5097c9f5e1c14de8befa7cfc9fc17d882022-12-21T20:01:52ZengIEEEIEEE Access2169-35362018-01-016449924500310.1109/ACCESS.2018.28609008421568Improving Both Quantity and Quality: Incentive Mechanism for Social Mobile Crowdsensing ArchitectureJia Xu0https://orcid.org/0000-0002-0523-9673Weiwei Bao1Huayue Gu2Lijie Xu3Guoping Jiang4https://orcid.org/0000-0003-1133-2222Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, ChinaJiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, ChinaJiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, ChinaJiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, ChinaJiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, ChinaMobile crowdsensing has emerged as an efficient paradigm for performing large-scale sensing tasks. Improving both the quantity and quality of users is still the pivotal problem for mobile crowdsensing system. This paper gives a comprehensive solution to improve the quantity and quality of users simultaneously through the social mobile crowdsensing architecture. To incentive the users based on the novel architecture, we first propose a universal initial diffuser selection algorithm to accommodate two widely studied diffusion models and, then a lightweight, multi-metric comprehensive, and parameter-free user quality evaluation method is presented. Finally, we propose a reverse auction to optimize the new criterion, which takes both social cost and user quality into consideration. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Meanwhile, the proposed incentive mechanisms show prominent advantage in total unit quality cost and running time.https://ieeexplore.ieee.org/document/8421568/Influence diffusioncrowdsensingincentive mechanismsocial network |
| spellingShingle | Jia Xu Weiwei Bao Huayue Gu Lijie Xu Guoping Jiang Improving Both Quantity and Quality: Incentive Mechanism for Social Mobile Crowdsensing Architecture IEEE Access Influence diffusion crowdsensing incentive mechanism social network |
| title | Improving Both Quantity and Quality: Incentive Mechanism for Social Mobile Crowdsensing Architecture |
| title_full | Improving Both Quantity and Quality: Incentive Mechanism for Social Mobile Crowdsensing Architecture |
| title_fullStr | Improving Both Quantity and Quality: Incentive Mechanism for Social Mobile Crowdsensing Architecture |
| title_full_unstemmed | Improving Both Quantity and Quality: Incentive Mechanism for Social Mobile Crowdsensing Architecture |
| title_short | Improving Both Quantity and Quality: Incentive Mechanism for Social Mobile Crowdsensing Architecture |
| title_sort | improving both quantity and quality incentive mechanism for social mobile crowdsensing architecture |
| topic | Influence diffusion crowdsensing incentive mechanism social network |
| url | https://ieeexplore.ieee.org/document/8421568/ |
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