Abstract
Mobile crowdsensing (MCS) is a new paradigm which takes advantage of pervasive mobile devices to collaboratively collect data and analyze physical phenomenon. As mobile devices are owned and controlled by individuals with various capabilities and intentions, a main challenge MCS applications face is to ensure the credibility of the crowd contributed data. Existed works attempt to increase confidence level of the sensory measurements by validating the location. However, the required infrastructure or neighbor support may not always be available, and the unreliable form containing false sensory data with a valid location is implicitly ignored. In this paper, we propose a novel Crowd-based Credibility Improving Scheme (CCIS) to improve the credibility of data in possible false forms leveraging crowd data property and crowd participants' reputation. Based on the data clusters generated using a lightweight fixed-width clustering algorithm, CCIS is able to adequately identify and filter out the clusters constituted mainly by false data using reputation information as the classifier. We conduct simulations on a publicly available trace with crowd contributed temperature measurements, the results show that CCIS yields an improvement of overall data credibility of around 1.2 with clustering accuracy over 96%.