Credibility on Crowdsensing Data Acquisition

A Systematic Mapping Study

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 years, we have seen a variety of approaches to leverage credibility on the crowdsensing platforms. Here we carried out a systematic mapping study that examined 140 credibility-related articles on crowdsensing. The results show that the absence of standardization 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 instrument that impacts credibility on these platforms. Our analysis showed that credibility solutions are not limited to reputation mechanisms, hardware-based solutions or direct computation of truth scores, but rather an ecosystem acting at different levels to ensure the credibility of the data. Elements created for purposes other than credibilities such as privacy and incentive mechanisms indirectly leverage data reliability by working cooperatively with other components of the crowdsensing ecosystem.