Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/136647
Title: Edge computing and IoT analytics for agile optimization in intelligent transportation systems
Author: Peyman, Mohammad
Copado Méndez, Pedro Jesús
Tordecilla Madera, Rafael David
Do Carmo Martins, Leandro
Xhafa, Fatos
Juan Pérez, Ángel Alejandro
Others: Universitat Politècnica de Catalunya
Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Keywords: fog
edge computing
Internet of things
intelligent transportation systems
smart cities
machine learning
agile optimization
Issue Date: 2-Oct-2021
Publisher: Energies
Citation: Peyman, M. [Mohammad], Copado, P. [Pedro ], Tordecilla, R.D. [Rafael D.], Do Carmo Martins, L.[Leandro], Xhafa, F. [Fatos] & Juan, A.A. [Angel A.]. (2021). Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems. Energies, 14(19), 1-26. doi: 10.3390/en14196309
Project identifier: info:eu-repo/grantAgreement/PID2019- 111100RB-C21/AEI/10.13039/501100011033
info:eu-repo/grantAgreement/2019-I-ES01-KA103-062602
info:eu-repo/grantAgreement/RED2018-102642-T
Also see: https://doi.org/10.3390/en14196309
Abstract: With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens' mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.
Language: English
URI: http://hdl.handle.net/10609/136647
ISSN: 1996-1073MIAR
Appears in Collections:Articles cientÍfIcs

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