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

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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Conference papers
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Contributor : Hal Ifip <>
Submitted on : Tuesday, April 20, 2021 - 4:47:20 PM
Last modification on : Tuesday, April 20, 2021 - 5:13:55 PM


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Rosa Salvo, Antonino Galletta, Orlando Marco Belcore, Massimo Villari. Modeling Users’ Performance: Predictive Analytics in an IoT Cloud Monitoring System. 8th European Conference on Service-Oriented and Cloud Computing (ESOCC), Sep 2020, Heraklion, Crete, Greece. pp.149-158, ⟨10.1007/978-3-030-44769-4_12⟩. ⟨hal-03203278⟩



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