###### 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. vehicle networking: Internet of Vehicles; IoT analytics; data analytics; agile optimization; distributed machine learning; smart cities +
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extremely fast, effective, and easily parallelizable optimization algorithms. These algorithms will allow for a coordinated and effective use of electric, unmanned, and self-driving vehicles in smart cities [7]. Agile optimization (AO) algorithms can be extremely fast in execution (thus allowing for real-time decision making), perfectly parallelizable (so they can be effectively executed on parallel threads), flexible (so they can be adapted to cope with different variants of routing and location problems, even those under dynamic or uncertain conditions), parameter-less (they should not require time-consuming fine-tuning processes), on-line (they have to be used iteratively as the data stream is updated), and still effective (they should be able to provide high-quality solutions to complex decision-making problems) [8, 9]. Another area gaining momentum in smart city scenarios is the implementation of machine learning (ML) algorithms [10]. ML embraces a big family of different algorithms, each one tailored to specific requirements, in terms of data availability, latency, dynamicity, and so on. Recently, a novel paradigm in ML has been introduced, breaking from the traditional centralized approach where a big resource-unlimited node is able to execute an ML algorithm while a distributed scenario is exploited to implement distributed machine learning (DML) algorithms where multiple nodes interact among them to exchange their knowledge, experience, and environmental data in order to implement a faster and dynamic-aware approach [11]. This could be particularly interesting in the IoV scenario, which is still relatively unexplored, mainly due to the several challenges introduced by such a highly dynamic scenario. The main contributions of this work are as follows:  
1. We analyze the main challenges associated with IoV and vehicle networking scenarios. 2. We identify the associated optimization problems, some of which need to be solved in real time. 3. We discuss how AO and DML approaches allow for the development of efficient transport and mobility systems. 4. We provide some numerical evidence of the gains that can be obtained by employing the aforementioned approaches. As previously mentioned, IoV and smart city scenarios are characterized by a high dynamicity and the generation of the significant amount of data required for their effective  
Figure 1: Vehicular networking in a smart city. management [12].