Road Roughness Crowd-Sensing with Smartphone Apps

Jean, Maxime; Chasse, Alexandre; Beng, William

doi:10.1109/itsc.2019.8917307

Cited by 2 publications

(1 citation statement)

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“…Processing is a fundamental element of the MCS campaign, as it is the means by which data becomes useful, and by which inferences -and thus insights -about the sensed phenomena are obtained. Pre-processing [54][55][56][57] refers to the preliminary treatment of the contributed data. The purpose of the preprocessing step is to ensure that the data is 2.1 The Communication Layer consistent in later steps.…”

Section: The Data Layermentioning

confidence: 99%

Quality Estimation for Scarce Scenarios Within Mobile Crowdsensing Systems

Azmy

Hassanein

2020

IEEE Internet Things J.

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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.

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Section: The Data Layermentioning

confidence: 99%