Quality Estimation for Scarce Scenarios within Mobile Crowd Sensing Systems
Mobile Crowd Sensing , MCS , Participant Selection , Participant Recruitment , Crowdsourcing , Crowd sourcing , Crowdsensing , Crowd Sensing , Human in the loop , Quality Estimation , Quality Metric , Median Absolute Deviation (MAD) , Bootstrap , Jackknife , Entropy , Internet of Things , MCS Coverage , Coverage Quality , Operations Research , Data Analysis
Mobile Crowd Sensing (MCS) is a paradigm that exploits the presence of a crowd of moving human participants to acquire, or generate, data from their environment. As part of the Internet of Things (IoT) paradigm, MCS serves the quest for 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 is 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 maybe 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 techniques that are relevant for scarce and erroneous scenarios. In this thesis, we discuss small data and propose schemes to tackle the problems associated with such limited data sets, in context of the smart city. We propose two novel quality metrics, MAD-Q and MADBS-Q, to deal with small data, 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 simulations results were found to be consistent.