## 4 Blockchain-Based Solutions on the Internet of Vehicles
The evaluation of this scheme was made using the Veins network simulator. The purpose was to simulate the average time and communication costs of the system. Veins provide a Mean operation that allows for checking time consumption in the various phases of authentication. The costliest phase was concluded to be the encryption in the key distribution center, with an average of 9 ms. The average time overhead for each layer was lower than 9 ms, which proves that the scheme can respond to requests promptly. The communication costs are, on average, 17 Kb for vehicle registration and 8 Kb for RSU verification, which is within the bounds of reasonability of technology for large-scale traffic networks. [PERSON] et al. [78] presented a system architecture for IoV where Blockchain, Software-Defined Networks (SDNs), and Fog computing. Blockchain solves security issues and settles trust between entities without a centralized trusted authority, as explained before. SDN technologies separate the control and data planes in a network to reduce structural complexity making nodes simply transmit data. At the same time, an SDN controller manages resource allocation, mobility, and rule generation. The fog computing approach helps reduce handovers in the network, which increases performance, and the RSUs and OBUs (onboard units) that are present in these fog zones, are SDN-enabled which means the SDN controller also controls them. The authors also define RSUH (RSU hubs), which manages the control overhead between the controller and RSUs. A reputation-based trust system was implemented between vehicles to detect falsified information and help vehicles transmit only useful information while reducing the impact of attackers. The evaluation was made with MATLAB combined with the NS-3 network simulator, using Hyperledger Fabric as the Blockchain platform, and the nodes were virtual machines deployed in Ubuntu containers. The two metrics that were taken into consideration for measurements were Packet Delivery Ratio and Transmission Delay. The former metric showed to be influenced by distance (optimal results were between 200 m and 500 m, due to route discovery and an increase in hops), the propagation model is chosen, the number of packets sent (i.e., more packets meant more collisions and a lower ratio), the number of vehicles and their speed (i.e., dense networks cause packet loss and a lower ratio). The Transmission Delay was influenced by distance (optimal results between 200 m and 500 m, due to MAC and the number of hops to the destination), the number of packets (i.e., the higher the number, the higher the contention and, consequently, the interference between nodes) and the density of vehicles in the network (i.e., more vehicles leads to more congestion). Due to these results, it was deemed to be a viable solution to Blockchain-enabled IoV communication and trust problems. [PERSON] et al. [79] tackle the computation and energy power consumption in IoV during communications. They present changes to already existing protocols and software with no additional hardware requirements. The authors then propose low complexity solutions for operations like connectivity check, Blockchain development, and data provenance and forensics, including lightweight algorithms. The experiments were performed with three MICAz motes [80], a wireless measurement system, each placed in a different car. The results obtained were then transferred to MATLAB to produce a simulation. The Received Signal Strength Indicator (RSSI) was measured to check the performance of the solution. Tests were made for Adversary Detection: first without an adversary (with and without a filter applied to the results) and then with an adversary (man-in-the-middle), and the Pearson Correlation Coefficient was calculated to check for value correlations (-1 for anticorrelation and 1 for high correlation). The filter test showed smoother results and value changes than the unfiltered one, with a high correlation in both cases (0.9754 and 0.9349, respectively).