Abstract
The Internet of things (IoT) comprises a huge collection of electronic devices connected to the Internet to ensure the dependable exchange of sensing information. It involves mobile workers (MWs) who perform various activities to support enormous online services and applications. In mobile crowd sensing (MCS), a massive amount of sensing data is also generated by smart devices. Broadly, in the IoT, verifying the credibility and truthfulness of MWs’ sensing reports is needed for MWs to expect attractive rewards. MWs are recruited by paying monetary incentives that must be awarded according to the quality and quantity of the task. The main problem is that MWs may perform false reporting by sharing low-quality reported data to reduce the effort required. In the literature, false reporting is improved by hiring enough MWs for a task to evaluate the trustworthiness and acceptability of information by aggregating the submitted reports. However, it may not be possible due to budget constraints, or when malicious reporters are not identified and penalized properly. Recruitment is still not a refined process, which contributes to low sensing quality. This paper presents Reputation, Quality-aware Recruitment Platform (RQRP) to recruit MWs based on reputation for quality reporting with the intention of platform profit maximization in the IoT scenario. RQRP comprises two main phases: filtration in the selection of MWs and verifying the credibility of reported tasks. The former is focused on the selection of suitable MWs based on different criteria (e.g., reputation, bid, expected quality, and expected platform utility), while the latter is more concerned with the verification of sensing quality, evaluation of reputation score, and incentives. We developed a testbed to evaluate and analyze the datasets, and a simulation was performed for data collection scenario from smart sensing devices. Results proved the superiority of RQRP against its counterparts in terms of truthfulness, quality, and platform profit maximization. To the best of our knowledge, we are the first to study the impact of truthful reporting on platform utility.
Highlights
The Internet of things (IoT) is a broad concept involving a huge number of online smart devices that can communicate with other devices
According to [53], a Bayesian estimation matrix can model mean based on a probability density function (PDF), and the prediction of credible contributions in the future from an mobile workers (MWs) can be made as presented in Equations (3) and (4)
Decisions are made in real time; (2) RQRP creates competition among MWs to have continuous effort, whereas most approaches in the literature only emphasize reducing the cost of hiring; (3) Reputation-aware recruitment provides the chance for the selection of suitable MWs with enhanced trustable quality reporting
Summary
The Internet of things (IoT) is a broad concept involving a huge number of online smart devices that can communicate with other devices. There are three main entities in MCS: requesters, who are the consumers of collected data; the platform, which acts as a service provider; and MWs, who perform the sensing tasks. Some approaches have considered weight- and vote-based mechanisms, but are criticized for providing the right-of-vote to a few dominating entities and for not penalizing the malicious MWs. Cross-validation is proposed, which may require extra monetary incentives, and may not be suitable for budget-limited tasks. The proposed mechanism is expected to ensure platform profitability with other task completion constraints while paying necessary incentives to the MWs. we developed a testbed using Windows Communication Foundation (WCF) services on Windows Azure cloud to evaluate and analyze the datasets containing MW reporting details.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call