Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

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...

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Vydané v:Energies (Basel) Ročník 14; číslo 19; s. 6309
Hlavní autori: Peyman, Mohammad, Copado, Pedro J., Tordecilla, Rafael D., Martins, Leandro do C., Xhafa, Fatos, Juan, Angel A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.10.2021
Predmet:
Cloud computing
Datasets
edge computing
fog
Heuristic
intelligent transportation systems
Internet of Things
Machine learning
Open data
Optimization algorithms
Optimization techniques
Simulation
Smart cities
Traffic congestion
Traffic flow
Urban areas
ISSN:1996-1073, 1996-1073
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en14196309