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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/19/6309 |
| _version_ | 1827680842755866624 |
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| author | Mohammad Peyman Pedro J. Copado Rafael D. Tordecilla Leandro do C. Martins Fatos Xhafa Angel A. Juan |
| author_facet | Mohammad Peyman Pedro J. Copado Rafael D. Tordecilla Leandro do C. Martins Fatos Xhafa Angel A. Juan |
| author_sort | Mohammad Peyman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T07:03:05Z |
| format | Article |
| id | doaj.art-ae29b941d1e44885af7e76292ce923f5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T07:03:05Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ae29b941d1e44885af7e76292ce923f52023-11-22T16:02:26ZengMDPI AGEnergies1996-10732021-10-011419630910.3390/en14196309Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation SystemsMohammad Peyman0Pedro J. Copado1Rafael D. Tordecilla2Leandro do C. Martins3Fatos Xhafa4Angel A. Juan5IN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, SpainIN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, SpainIN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, SpainIN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, SpainComputer Science Department, Universitat Politècnica de Catalunya, 08034 Barcelona, SpainIN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, SpainWith 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.https://www.mdpi.com/1996-1073/14/19/6309fogedge computingInternet of Thingsintelligent transportation systemssmart citiesmachine learning |
| spellingShingle | Mohammad Peyman Pedro J. Copado Rafael D. Tordecilla Leandro do C. Martins Fatos Xhafa Angel A. Juan Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems Energies fog edge computing Internet of Things intelligent transportation systems smart cities machine learning |
| title | Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems |
| title_full | Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems |
| title_fullStr | Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems |
| title_full_unstemmed | Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems |
| title_short | Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems |
| title_sort | edge computing and iot analytics for agile optimization in intelligent transportation systems |
| topic | fog edge computing Internet of Things intelligent transportation systems smart cities machine learning |
| url | https://www.mdpi.com/1996-1073/14/19/6309 |
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