## 2 Related Work  
Connected vehicles are gaining increasing visibility. [PERSON] and [PERSON] [30] present an extensive discussion on the future of connected vehicles. Authors notice that _\"...rapid technological developments are occurring within two separate areas defined as Automated Vehicles and Connected Vehicles. The development of connected vehicles and automated vehicles is occurring largely independently, although convergence of the two areas will be required for full automation of vehicles\"_. [PERSON] et al. [31] notices that connected vehicles are expected to be the next frontier for automotive revolution and the key to the evolution to next generation intelligent transportation systems. Authors focus on wireless technologies and potential challenges to provide vehicle-to-x connectivity, discussing challenges and reviewing the state-of-the-art wireless solutions for vehicle-to-sensor, vehicle-to-vehicle, vehicle-to-Internet, and vehicle-to-road infrastructure connectivities. Vehicular communication has been extensively studied. A comprehensive survey on infrastructure-based vehicular networks is presented in works [7; 23]. Along this section, we overview a representative list of selected works in data dissemination, data scheduling, communication protocols, and deployment strategies. Data dissemination is at the core of most services enabled by vehicular networks. [PERSON] et al. [32] present the review of 23 different kinds of dissemination schemes highlighting the benefits and drawbacks associated with each one. [PERSON] and [PERSON] [33] propose push-based broadcast data dissemination in heavy traffic: messages are periodically broadcasted to passing vehicles. In light traffic scenarios, vehicles query on-demand for traffic information. Authors derive a mathematical model that shows the effectiveness of the solution and they conclude that data dissemination in vehicular networks should be adaptable to dynamic traffic environments. [PERSON] and [PERSON] [34] assume vehicles equipped with cameras and the problem is how to deliver the images to remote data collectors. Authors propose a data collection algorithm capable of eliminating the redundancy of data transmitted by moving vehicles. In a real situation, several vehicles may report the same event. Thus, data redundancy mitigation is necessary to improve the network efficiency. [PERSON] et al. [8] propose a novel data traffic aggregation and slicing model with algorithms in 5G uplink, based on classifying and measuring the data traffic to achieve quality of service for smart systems. Data scheduling is also addressed. [PERSON] et al. [35] propose a downlink scheduler to deliver high-quality video-on-demand over infrastructure-based vehicular networks. The scheduler is deployed in roadside units to coordinate the transmission of packets according to the importance of packet to video quality, the playback deadline, and also real-time information of vehicles. [PERSON] et al. [36] also devise a scheduling algorithm to coordinate the distribution of data files in vehicular networks. A collection of data files is stored at distributed locations and delivered to passing vehicles. According to the popularity of files, the proposed algorithm schedules the location of files through the selective upload and download of roadside units to maximize the delivery ratio of files to vehicles. Comlimentary, [PERSON] et al. [37] propose data traffic slicing model along with the comparison of packet traffic scheduling mechanisms such as weighted fair queuing, priority queuing, and first-in-first-out in the 5G mobile network. Communication protocols have also been proposed. [PERSON] et al. [38] propose a GreeDi based reactive routing protocol aimed at selecting the most efficient route in terms of energy consumption between two nodes in VANETs. [PERSON] et al. [39] propose a cross-Layer multi-hop data delivery protocol with fairness guarantees where vehicles do not communicate with roadside units individually, but through one leader.