| Reference : Intelligent Gaming for Mobile Crowd-Sensing Participants to Acquire Trustworthy Big D... |
| Scientific journals : Article | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/34041 | |||
| Intelligent Gaming for Mobile Crowd-Sensing Participants to Acquire Trustworthy Big Data in the Internet of Things | |
| English | |
| Pouryazdan, Maryam [] | |
| Fiandrino, Claudio [] | |
| Kantarci, Burak [] | |
| Soyata, Tolga [] | |
| Kliazovich, Dzmitry [] | |
Bouvry, Pascal  [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| 11-Oct-2017 | |
| IEEE Access | |
| Institute of Electrical and Electronics Engineers (IEEE) | |
| 5 | |
| 22209 - 22223 | |
| Yes (verified by ORBilu) | |
| International | |
| 2169-3536 | |
| Piscataway | |
| NJ | |
| [en] Ambient intelligence, data acquisition, data analysis, distributed computing, intelligent sensors, Internet of Things, mobile computing, game theory, crowd-sensing, gami cation. | |
| [en] In mobile crowd-sensing systems, the value of crowd-sensed big data can be increased by
incentivizing the users appropriately. Since data acquisition is participatory, crowd-sensing systems face the challenge of data trustworthiness and truthfulness assurance in the presence of adversaries whose motivation can be either manipulating sensed data or collaborating unfaithfully with the motivation of maximizing their income. This paper proposes a game theoretic methodology to ensure trustworthiness in user recruitment in mobile crowd-sensing systems. The proposed methodology is a platform-centric framework that consists of three phases: user recruitment, collaborative decision making on trust scores, and badge rewarding. In the proposed framework, users are incentivized by running sub-game perfect equilibrium and gami cation techniques. Through simulations, we showthat approximately 50% and a minimum of 15% improvement can be achieved by the proposed methodology in terms of platform and user utility, respectively, when compared with fully distributed and user-centric trustworthy crowd-sensing. | |
| http://hdl.handle.net/10993/34041 | |
| 10.1109/ACCESS.2017.2762238 | |
| http://ieeexplore.ieee.org/document/8064624/ |
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