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Title	Internet Of Vehicles And Real-Time Optimization Algorithms: Concepts For Vehicle Networking In Smart Cities
ID_Doc	33099
Authors	Adelantado F.; Ammouriova M.; Herrera E.; Juan A.A.; Shinde S.S.; Tarchi D.
Year	2022
Published	Vehicles, 4, 4
DOI	http://dx.doi.org/10.3390/vehicles4040065
Abstract	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. © 2022 by the authors.
Author Keywords	agile optimization; data analytics; distributed machine learning; Internet of Vehicles; IoT analytics; smart cities; vehicle networking

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