Distributed Incentive Mechanism Based on Hyperledger Fabric

• Published in: Journal of physics. Conference series 2022-04, Vol.2224 (1), p.12130
• Main Authors: Li, Baohui, Fang, Yadong, Xu, Wei, Wang, Jiaxing, Rui, Lanlan, Lei, Min
• Format: Article
• Language: English
• Subjects: Algorithms; Contracts; Credibility; Cryptography; Data acquisition; Data analysis; Data storage; Geographical locations; Incentives; Outliers (statistics); Physics; Questions
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2224/1/012130

Crowd sensing is a new type of data acquisition method that can efficiently and diversify the realization of sensing tasks. However, this method currently has some problems, such as data storage being overly dependent on third-party platforms, and there is a lack of reliable data credible evaluation methods. To solve this problem, our paper proposes a distributed incentive mechanism based on Hyperledger Fabric (HF-DIM) in the Crowd sensing scenario. In particular, the following questions are studied: How to achieve distributed incentive to solve the traditional incentive that relies on a centralized platform? How to evaluate the credibility of the sensing data provided by the users? To the former question, we implement a multi-attribute auction algorithm based on smart contracts, and distributed incentives are implemented using blockchain deployed contracts. To the latter question, We propose a K-nearest neighbor outlier detection algorithm based on geographic location and similarity to evaluate the credibility of the data and establish a reputation index based on the credibility of the data. Through simulation experiments using real data set, the feasibility and effectiveness of the proposed framework and algorithm are verified.