Reputation-Aware Recruitment and Credible Reporting for Platform Utility in Mobile Crowd Sensing with Smart Devices...

Area of Science:

• Computer Science
• Ubiquitous Computing
• Data Science

Background:

• The Internet of Things (IoT) relies on mobile workers (MWs) for data sensing.
• Ensuring the credibility of sensing reports from MWs is crucial for rewarding them accurately.
• False reporting by MWs to reduce effort degrades data quality and platform utility.

Purpose of the Study:

• To propose a novel platform, Reputation, Quality-aware Recruitment Platform (RQRP), for recruiting MWs in IoT scenarios.
• To enhance the quality and truthfulness of sensing data through reputation-based recruitment.
• To maximize platform profit by optimizing MW selection and incentive mechanisms.

Main Methods:

• Developed a two-phase recruitment strategy: MW filtration and task credibility verification.
• Filtration phase selects MWs based on reputation, bid, expected quality, and platform utility.
• Verification phase assesses sensing quality, reputation scores, and incentive distribution.

Main Results:

• The RQRP platform demonstrated superior performance compared to existing methods.
• Significant improvements were observed in data truthfulness and overall sensing quality.
• The platform achieved higher profit maximization due to efficient recruitment and quality control.

Conclusions:

• RQRP effectively addresses the challenge of false reporting in mobile crowd sensing.
• Reputation-based recruitment is a viable strategy for enhancing data quality and platform profitability in IoT.
• This work is the first to analyze the impact of truthful reporting on platform utility in IoT sensing.