Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics
Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mob...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/499 |
| _version_ | 1797431129575260160 |
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| author | Mohammad Peyman Tristan Fluechter Javier Panadero Carles Serrat Fatos Xhafa Angel A. Juan |
| author_facet | Mohammad Peyman Tristan Fluechter Javier Panadero Carles Serrat Fatos Xhafa Angel A. Juan |
| author_sort | Mohammad Peyman |
| collection | DOAJ |
| description | Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities. |
| first_indexed | 2024-03-09T09:40:29Z |
| format | Article |
| id | doaj.art-2209cb5fc3154bfe878444ca3f7abe53 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:40:29Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2209cb5fc3154bfe878444ca3f7abe532023-12-02T00:57:51ZengMDPI AGSensors1424-82202023-01-0123149910.3390/s23010499Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and SimheuristicsMohammad Peyman0Tristan Fluechter1Javier Panadero2Carles Serrat3Fatos Xhafa4Angel A. Juan5Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya, 08018 Barcelona, SpainSmurfit Business School, University College Dublin, Blackrock, D04 V1W8 Dublin, IrelandDepartment of Management, Universitat Politècnica de Catalunya, 08028 Barcelona, SpainDepartment of Mathematics, Universitat Politècnica de Catalunya, 08028 Barcelona, SpainDepartment of Computer Science, Universitat Politècnica de Catalunya, 08034 Barcelona, SpainDepartment of Management, Euncet Business School, 08225 Terrassa, SpainVehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.https://www.mdpi.com/1424-8220/23/1/499vehicular networkssmart citiesoptimizationheuristicsopen data |
| spellingShingle | Mohammad Peyman Tristan Fluechter Javier Panadero Carles Serrat Fatos Xhafa Angel A. Juan Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics Sensors vehicular networks smart cities optimization heuristics open data |
| title | Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics |
| title_full | Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics |
| title_fullStr | Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics |
| title_full_unstemmed | Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics |
| title_short | Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics |
| title_sort | optimization of vehicular networks in smart cities from agile optimization to learnheuristics and simheuristics |
| topic | vehicular networks smart cities optimization heuristics open data |
| url | https://www.mdpi.com/1424-8220/23/1/499 |
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