Trustworthiness of Context-Aware Urban Pollution Data in Mobile Crowd Sensing

Urban pollution is usually monitored via fixed stations that provide detailed and reliable information, thanks to equipment quality and effective measuring protocols, but these sampled data are gathered from very limited areas and through discontinuous monitoring campaigns. Currently, the spread of mobile devices has fostered the development of new approaches, like Mobile Crowd Sensing (MCS), increasing the chances of using smartphones as suitable sensors in the urban monitoring scenario, because it potentially contributes massive ubiquitous data at relatively low cost. However, MCS is useless (or even counter-productive), if contributed data are not trustworthy, due to wrong data-collection procedures by non-expert practitioners. Contextualizing monitored data with those coming from phone-embedded sensors and from time/space proximity can improve data trustworthiness. This work focuses on the development of an algorithm that exploits context awareness to improve the reliability of MCS collected data. It has been validated against some real use cases for noise pollution and promises to improve the trustworthiness of end users generated data.