Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing
Ubiquity of mobile devices with rich sensory capabilities has given rise to the mobile crowd-sensing (MCS) concept, in which a central authority (the platform) and its participants (mobile users) work collaboratively to acquire sensory data over a wide geographic area. Recent research in MCS highlig...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/7835651/ |
| _version_ | 1818853744550346752 |
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| author | Maryam Pouryazdan Burak Kantarci Tolga Soyata Luca Foschini Houbing Song |
| author_facet | Maryam Pouryazdan Burak Kantarci Tolga Soyata Luca Foschini Houbing Song |
| author_sort | Maryam Pouryazdan |
| collection | DOAJ |
| description | Ubiquity of mobile devices with rich sensory capabilities has given rise to the mobile crowd-sensing (MCS) concept, in which a central authority (the platform) and its participants (mobile users) work collaboratively to acquire sensory data over a wide geographic area. Recent research in MCS highlights the following facts: 1) a utility metric can be defined for both the platform and the users, quantifying the value received by either side; 2) incentivizing the users to participate is a non-trivial challenge; 3) correctness and truthfulness of the acquired data must be verified, because the users might provide incorrect or inaccurate data, whether due to malicious intent or malfunctioning devices; and 4) an intricate relationship exists among platform utility, user utility, user reputation, and data trustworthiness, suggesting a co-quantification of these inter-related metrics. In this paper, we study two existing approaches that quantify crowd-sensed data trustworthiness, based on statistical and vote-based user reputation scores. We introduce a new metric - collaborative reputation scores - to expand this definition. Our simulation results show that collaborative reputation scores can provide an effective alternative to the previously proposed metrics and are able to extend crowd sensing to applications that are driven by a centralized as well as decentralized control. |
| first_indexed | 2024-12-19T07:41:41Z |
| format | Article |
| id | doaj.art-71b8eac69b3f4af18fc8787844d8b25d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:41:41Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-71b8eac69b3f4af18fc8787844d8b25d2022-12-21T20:30:26ZengIEEEIEEE Access2169-35362017-01-0151382139710.1109/ACCESS.2017.26604617835651Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-SensingMaryam Pouryazdan0Burak Kantarci1https://orcid.org/0000-0003-0220-7956Tolga Soyata2Luca Foschini3https://orcid.org/0000-0001-9062-3647Houbing Song4https://orcid.org/0000-0003-2631-9223Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY, USADepartment of Electrical and Computer Engineering, Clarkson University, Potsdam, NY, USADepartment of Computer Engineering, University at Albany, Albany, NY, USADepartment of Computer Science, University of Bologna, Bologna, ItalyDepartment of Electrical and Computer Engineering, West Virginia University, Montgomery, WV, USAUbiquity of mobile devices with rich sensory capabilities has given rise to the mobile crowd-sensing (MCS) concept, in which a central authority (the platform) and its participants (mobile users) work collaboratively to acquire sensory data over a wide geographic area. Recent research in MCS highlights the following facts: 1) a utility metric can be defined for both the platform and the users, quantifying the value received by either side; 2) incentivizing the users to participate is a non-trivial challenge; 3) correctness and truthfulness of the acquired data must be verified, because the users might provide incorrect or inaccurate data, whether due to malicious intent or malfunctioning devices; and 4) an intricate relationship exists among platform utility, user utility, user reputation, and data trustworthiness, suggesting a co-quantification of these inter-related metrics. In this paper, we study two existing approaches that quantify crowd-sensed data trustworthiness, based on statistical and vote-based user reputation scores. We introduce a new metric - collaborative reputation scores - to expand this definition. Our simulation results show that collaborative reputation scores can provide an effective alternative to the previously proposed metrics and are able to extend crowd sensing to applications that are driven by a centralized as well as decentralized control.https://ieeexplore.ieee.org/document/7835651/Mobile crowd-sensing (MCS)smart cityreputation systemscollaborative sensinguser incentivesreputation score |
| spellingShingle | Maryam Pouryazdan Burak Kantarci Tolga Soyata Luca Foschini Houbing Song Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing IEEE Access Mobile crowd-sensing (MCS) smart city reputation systems collaborative sensing user incentives reputation score |
| title | Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing |
| title_full | Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing |
| title_fullStr | Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing |
| title_full_unstemmed | Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing |
| title_short | Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing |
| title_sort | quantifying user reputation scores data trustworthiness and user incentives in mobile crowd sensing |
| topic | Mobile crowd-sensing (MCS) smart city reputation systems collaborative sensing user incentives reputation score |
| url | https://ieeexplore.ieee.org/document/7835651/ |
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