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2020 | OriginalPaper | Buchkapitel

Modeling Users’ Performance: Predictive Analytics in an IoT Cloud Monitoring System

verfasst von : Rosa Di Salvo, Antonino Galletta, Orlando Marco Belcore, Massimo Villari

Erschienen in: Service-Oriented and Cloud Computing

Verlag: Springer International Publishing

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Abstract

We exploit the feasibility of predictive modeling combined with the support given by a suitably defined IoT Cloud Infrastructure in the attempt of assessing and reporting relative performances for user-specific settings during a bike trial. The matter is addressed by introducing a suitable dynamical system whose state variables are the so-called origin-destination (OD) flow deviations obtained from prior estimates based on historical data recorded by means of mobile sensors directly installed in each bike through a fast real-time processing of big traffic data. We then use the Kalman filter theory in order to dynamically update an assignment matrix in such a context and gain information about usual routes and distances. This leads us to a dynamical ranking system for the users of the bike trial community making the award procedure more transparent.

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Titel: Modeling Users’ Performance: Predictive Analytics in an IoT Cloud Monitoring System
verfasst von: Rosa Di Salvo
Antonino Galletta
Orlando Marco Belcore
Massimo Villari
Verlag: Springer International Publishing
Buch: Service-Oriented and Cloud Computing
Print ISBN: 978-3-030-44768-7

Electronic ISBN: 978-3-030-44769-4

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-44769-4_12

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