Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing
Ubiquity of mobile devices with rich sensory capabilities has given rise to the mobile crowdsensing (MCS) concept, in which a central authority (the platform) and its participants (mobile users) work collaboratively to acquire sensory data over a wide geographic area. Recent research in MCS highlights the following facts: 1) a utility metric can be defined for both the platform and the users, quantifying the value received by either side; 2) incentivizing the users to participate is a non-trivial challenge; 3) correctness and truthfulness of the acquired data must be verified, because the users might provide incorrect or inaccurate data, whether due to malicious intent or malfunctioning devices; and 4) an intricate relationship exists among platform utility, user utility, user reputation, and data trustworthiness, suggesting a co-quantification of these inter-related metrics. In this paper, we study two existing approaches that quantify crowd-sensed data trustworthiness, based on statistical and vote-based user reputation scores. We introduce a new metricā?? collaborative reputation scoresā??to expand this definition. Our simulation results show that collaborative reputation scores can provide an effective alternative to the previously proposed metrics and are able to extend crowd sensing to applications that are driven by a centralized as well as decentralized control.
Mobile crowd-sensing (MCS), smart city, reputation systems, collaborative sensing, user incentives, reputation score, data trustworthiness, auction theory, social network theory, statistical methods.