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

Area of Science:

• Computer Science
• Distributed Systems
• Blockchain Technology

Background:

• Mobile crowdsensing (MCS) is vital for data acquisition in edge computing and smart cities.
• Centralized MCS platforms face challenges like single-point failures and trust deficits, hindering user participation and data reliability.
• Existing models struggle with secure data storage and incentivizing genuine contributions.

Purpose of the Study:

• To propose a credible and distributed incentive mechanism for mobile crowdsensing.
• To address data storage vulnerabilities and user trust issues in MCS.
• To enhance the reliability and participation in crowdsensing networks.

Main Methods:

• Developed a creditable and distributed incentive mechanism based on Hyperledger Fabric (HF-CDIM).
• Implemented a multi-attribute auction algorithm with reputation management via smart contracts for distributed incentives.
• Utilized a K-nearest neighbor outlier detection algorithm for data credibility assessment and reputation updating.

Main Results:

• The proposed HF-CDIM effectively establishes a distributed incentive system.
• Data credibility is quantified and improved through outlier detection and reputation updates.
• Simulation results confirm the mechanism's effectiveness and feasibility using real-world datasets.

Conclusions:

• The HF-CDIM offers a robust solution to trust and data storage issues in mobile crowdsensing.
• Blockchain technology, combined with auction and reputation systems, enhances MCS reliability.
• The proposed system promotes user participation and ensures credible data collection in smart city environments.