Internet of Vehicles and Real-Time Optimization Algorithms: Concepts for Vehicle Networking in Smart Cities

Achieving sustainable freight transport and citizens’ mobility operations in modern cities are becoming critical issues for many governments. By analyzing big data streams generated through IoT devices, city planners now have the possibility to optimize traffic and mobility patterns. IoT combined wi...

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Bibliographic Details
Published inVehicles Vol. 4; no. 4; pp. 1223 - 1245
Main Authors Adelantado, Ferran, Ammouriova, Majsa, Herrera, Erika, Juan, Angel A., Shinde, Swapnil Sadashiv, Tarchi, Daniele
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2022
Subjects
agile optimization
Algorithms
Automobile industry
Autonomous vehicles
Car sharing
data analytics
Data transmission
distributed machine learning
Freight transportation
Internet of Things
Internet of Vehicles
IoT analytics
Machine learning
Optimization
Optimization algorithms
R&D
Real time
Research & development
Smart cities
Standardization
Travel time
vehicle networking
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Summary:Achieving sustainable freight transport and citizens’ mobility operations in modern cities are becoming critical issues for many governments. By analyzing big data streams generated through IoT devices, city planners now have the possibility to optimize traffic and mobility patterns. IoT combined with innovative transport concepts as well as emerging mobility modes (e.g., ridesharing and carsharing) constitute a new paradigm in sustainable and optimized traffic operations in smart cities. Still, these are highly dynamic scenarios, which are also subject to a high uncertainty degree. Hence, factors such as real-time optimization and re-optimization of routes, stochastic travel times, and evolving customers’ requirements and traffic status also have to be considered. This paper discusses the main challenges associated with Internet of Vehicles (IoV) and vehicle networking scenarios, identifies the underlying optimization problems that need to be solved in real time, and proposes an approach to combine the use of IoV with parallelization approaches. To this aim, agile optimization and distributed machine learning are envisaged as the best candidate algorithms to develop efficient transport and mobility systems.
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ISSN:2624-8921
2624-8921
DOI:10.3390/vehicles4040065