Quality Estimation for Scarce Scenarios within Mobile Crowdsensing Systems

Abstract

Mobile crowdsensing (MCS) is a paradigm that exploits the presence of a crowd of moving human participants to acquire, or generate, data from their environment. As a part of the Internet-of-Things (IoT) paradigm, MCS serves the quest for a more efficient operation of a smart city. Big data techniques employed on this data produce inferences about the participants' environment, the smart city. However, sufficient amounts of data are not always available. Sometimes, the available data are scarce as it is obtained at different times, locations, and from different MCS participants who may not be present. As a consequence, the scale of data acquired may be small and susceptible to errors. In such scenarios, the MCS system requires techniques that acquire reliable inferences from such limited data sets. To that end, we resort to small data (SD) techniques that are relevant for scarce and erroneous scenarios. In this article, we discuss SD and propose schemes to tackle the problems associated with such limited data sets, in the context of the smart city. We propose two novel quality metrics: 1) MAD quality metric (MAD-Q) and 2) MAD bootstrap quality metric (MADBS-Q), to deal with SD, focusing on evaluating the quality of a data set within MCS. We also propose an MCS-specific coverage metric that combines the spatial dimension with MAD-Q and MADBS-Q. We show the performance of all the presented techniques through closed-form mathematical expressions, with which simulation results were found to be consistent.