Credibility on Crowdsensing Data Acquisition

Abstract

This paper focuses on the credibility of crowd sensed data. The ubiquity of crowdsensing platforms has enabled the capture of sensed information useful for several applications domains. However, one concern with crowdsensing is the information credibility and, over the last few years, we have seen a variety of approaches to leverage credibility on the crowdsensing platforms. Here, we carried out a systematic mapping study on 177 credibility-related articles about crowdsensing. The results show that the absence of standard models in the data capture process and the human factors such as individualism, inattention, and the possibility of errors (whether they are intentional or not), are the primary harmful to the credibility on these platforms. Additionally, we propose a three-level taxonomy that classifies crowdsensing credibility into two broad branches: direct and indirect approaches. To the best of our knowledge, we believe this is the first systematic mapping to address both credibility approaches and the factors that negatively impact the data credibility on crowdsensing platforms.