Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
Guo, W., Chang, Z., Su, Y., Guo, X., Hämäläinen, T., Li, J., & Li, Y. (2022). Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks. Applied Sciences, 12(21), Article 11049. https://doi.org/10.3390/app122111049
Julkaistu sarjassa
Applied SciencesTekijät
Li, Jian |
Päivämäärä
2022Oppiaine
TietotekniikkaSecure Communications Engineering and Signal ProcessingTekniikkaMathematical Information TechnologySecure Communications Engineering and Signal ProcessingEngineeringTekijänoikeudet
© 2022 by the authors
The sharing of high-quality traffic information plays a crucial role in enhancing the driving experience and safety performance for vehicular networks, especially in the development of electric vehicles (EVs). The crowdsourcing-based real-time navigation of charging piles is characterized by low delay and high accuracy. However, due to the lack of an effective incentive mechanism and the resource-consuming bottleneck of sharing real-time road conditions, methods to recruit or motivate more EVs to provide high-quality information gathering has attracted considerable interest. In this paper, we first introduce a blockchain platform, where EVs act as the blockchain nodes, and a reputation-based incentive mechanism for vehicular networks. The reputations of blockchain nodes are calculated according to their historical behavior and interactions. Further, we design and implement algorithms for updating honest-behavior-based reputation as well as for screening low-reputation miners, to optimize the profits of miners and address spatial crowdsourcing tasks for sharing information on road conditions. The experimental results show that the proposed reputation-based incentive method can improve the reputation and profits of vehicle users and ensure data timeliness and reliability.
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Julkaisija
MDPIISSN Hae Julkaisufoorumista
2076-3417Asiasanat
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https://converis.jyu.fi/converis/portal/detail/Publication/164722709
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Lisätietoja rahoituksesta
This work was supported in part by NSFC under Grant 62071105, Innovation Capability Improvement Plan Project of Hebei Province (22567626H), CSC funding (202108130129), the joint project of China Mobile Research Institute& X-NET (2022H002), the 131 project, the fund project of Intelligent Terminal Key Laboratory of Sichuan Province (SCITLAB-1015).Lisenssi
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