## 2 Related Work
The goal is to reduce the network traffic and to use bandwidth more efficiently. The leader will collect all information from other nodes and share it with roadside units. Complementary, [PERSON] et al. [40] propose a protocol to increase the global data transfer. Authors observe that when roadside units are shared by more than one vehicle, the vehicle with the lowest transmission rate reduces the effective transmission rate of all other vehicles. Observing that every vehicle eventually receives good performance when it is near the roadside unit, the authors propose a medium access protocol that opportunistically grants access to vehicles with maximum transmission rate. The overall system throughput is improved by a factor of four. Deployment based on the V2I contact probability is addressed by [PERSON] et al. [41]. Authors discuss the system design and address the cellular offloading issue in urban scenarios through the deployment of WAVE/IEEE 802.11p devices on vehicles and roadside units. The work shows the impact of the percentage of equipped vehicles, of the number of deployed roadside units, and of the adopted routing protocols on the amount of data delivered. Results, obtained through an integrated simulation platform taking both realistic vehicular environments and wireless network communication aspects into account, show that the deployment of few road side units and the use of low complexity routing protocols leads to a significant reduction of cellular resource occupation, even approaching 100% with a high density of equipped vehicles. Deployment for content delivery is addressed in [42]. Authors present a mixed-integer quadratic programming based optimum roadside units deployment scheme to provide Internet access services for the maximum road traffic volumes with limited number of roadside units. Additionally, [PERSON] et al. [43] investigate the application of Content Delivery Networks (CDN) to the vehicular scenario, modeling the distribution of several contents within distinct levels of QoS. Since a given content may be meaningful only to a given region of interest, they assume that each content type is related to a target region where it must be made available. Deployment based on evolutionary approaches is proposed by [PERSON] et al. [44]. Authors study how the infrastructure should be used to improve data travel time over very large distances. They present a multi-layer aggregation scheme defining landmarks. Cars passing landmarks record the time travel, which is aggregated to infer the time travel between more distant landmarks. These aggregation steps are performed by the cars themselves in a completely decentralized basis. The minimal initial deployment of roadside units is handled by a genetic algorithm based on the travel time savings. Complementary, [PERSON] et al. [45] apply genetic programming to solve the deployment as a Maximum Coverage Problem. Linear Programming Models are also considered. [PERSON] et al. [46] use binary integer programming to solve the allocation of roadside units. They eliminate minor roads and model major roads as a grid. Authors present two different optimization methods for placement of a limited number of roadside units in an urban region: (a) Analytical Binary Integer Programming (BIP); (b) novel Balloon Expansion Heuristic (BEH). The BIP method utilizes branch and bound approach to find an optimal analytical solution whereas BEH method uses balloon expansion analogy to find an optimal or near optimal solution. Authors conclude that the BEH method is more versatile and performs better than BIP method in terms of the computational cost and scalability. Furthermore, [PERSON] et al. [47] study the deployment of the roadside infrastructure by formulating an optimization problem and solving it using Integer Linear programming. The proposed optimization framework takes into account the effect of buildings on signal propagation, LAN lines and road topology. The formulation assumes a grid-like road network.