A blockchain-based creditable and distributed incentive mechanism for participant mobile crowdsensing in edge computing

Shiyou Chen; Baohui Li; Lanlan Rui; Jiaxing Wang; Xingyu Chen; Shiyou Chen; Baohui Li; Lanlan Rui; Jiaxing Wang; Xingyu Chen

doi:10.3934/mbe.2022152

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 4: 3285-3312. doi: 10.3934/mbe.2022152

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Research article Special Issues

A blockchain-based creditable and distributed incentive mechanism for participant mobile crowdsensing in edge computing

1.
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
2.
Cyberspace Security Academy, Beijing University of Posts and Telecommunications, Beijing 100876, China

Academic Editor: Yuan Tian

Received: 21 November 2021 Revised: 13 January 2022 Accepted: 16 January 2022 Published: 21 January 2022

With the popularization of portable smart devices, the advance in ubiquitous connectivity and the Internet of Things (IoT), mobile crowdsensing is becoming one of the promising applications to acquire information in the physical world of edge computing and is widely used in Smart Cities. However, most of the existing mobile crowdsensing models are based on centralized platforms, which have some problems in reality. Data storage is overly dependent on third-party platforms leading to single-point failures. Besides, trust issues seriously affect users' willingness to participate and the credibility of data. To solve these two problems, a creditable and distributed incentive mechanism based on Hyperledger Fabric (HF-CDIM) is proposed in this paper. Specifically, the HF-CDIM combines auction, reputation and data detection methods. First, we develop a multi-attribute auction algorithm with a reputation on blockchain by designing a smart contract, which achieves a distributed incentive mechanism for participants. Second, we propose a K-nearest neighbor outlier detection algorithm based on geographic location and similarity to quantify the credibility of the data. It is also used to update the user's reputation index. This guarantees the credibility of sensing data. Finally, the simulation results using real-world data set verify the effectiveness and feasibility of above mechanism.
- hyperledger fabric,
- blockchain,
- mobile crowdsensing,
- incentive mechanism,
- smart contract
Citation: Shiyou Chen, Baohui Li, Lanlan Rui, Jiaxing Wang, Xingyu Chen. A blockchain-based creditable and distributed incentive mechanism for participant mobile crowdsensing in edge computing[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 3285-3312. doi: 10.3934/mbe.2022152

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Abstract

With the popularization of portable smart devices, the advance in ubiquitous connectivity and the Internet of Things (IoT), mobile crowdsensing is becoming one of the promising applications to acquire information in the physical world of edge computing and is widely used in Smart Cities. However, most of the existing mobile crowdsensing models are based on centralized platforms, which have some problems in reality. Data storage is overly dependent on third-party platforms leading to single-point failures. Besides, trust issues seriously affect users' willingness to participate and the credibility of data. To solve these two problems, a creditable and distributed incentive mechanism based on Hyperledger Fabric (HF-CDIM) is proposed in this paper. Specifically, the HF-CDIM combines auction, reputation and data detection methods. First, we develop a multi-attribute auction algorithm with a reputation on blockchain by designing a smart contract, which achieves a distributed incentive mechanism for participants. Second, we propose a K-nearest neighbor outlier detection algorithm based on geographic location and similarity to quantify the credibility of the data. It is also used to update the user's reputation index. This guarantees the credibility of sensing data. Finally, the simulation results using real-world data set verify the effectiveness and feasibility of above mechanism.

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