## 3 Optimization Problems in Vehicular Networks
### Carsharing  
Carsharing offers a flexible alternative that meets a wide range of transportation needs around the world while mitigating the negative effects of private vehicle ownership. Extensive research has been conducted in the field of carsharing systems in recent years, which includes analyzing carsharing trip characteristics, evaluating its impact on society and the environment, and optimizing systems [73]. Figure 5 depicts the literature reviewed in this section on carsharing optimization via vehicular networks. [PERSON] et al. [74] proposed a carsharing service in VANET based on a dual SGA in order to improve the quality and robustness of carsharing services while also reducing passenger wait times and avoiding traffic congestion. After a successful match, the vehicle will respond to carpool matching requests via relay vehicles. The DSGA procedure will first calculate the geographic matching based on the driver and passenger destination correlation when the relay vehicle receives the request. The vehicle will then conduct a VSGA identity check. If the match fails, the request is forwarded by the relay vehicle. In the final match step, each relay vehicle merges and collects its traffic data with the help of nearby vehicles via the beacon package. They then distribute a certain amount of tokens to each relay vehicle to control the peripheral congestion message. The authors also proposed a layered congestion monitoring method to collect congestion information and improve matching accuracy. In this process, the relay vehicle first distributes some tokens to its neighbors, and then the neighbor vehicle that received the tokens collects the atomic congestion message in its driving region, such as acceleration, speed, and brake frequency, and then performs fuzzy clustering on the message. The fuzzy clustering method can reduce information and computation redundancy by extracting key information. [PERSON] et al. [75] combined vehicle mobility simulation and vehicular communication networks to create carsharing systems. They provided two models, one for taxi systems and one for the VANET configuration in NS-2. They used SUMO to generate traffic. Furthermore, classic microscopic traffic theory was used to develop the car following model, which calculates each car's trajectory to analyze its performance. They chose the ad hoc on-demand distance vector (AODV) routing protocol for the network layer after comparing AODV, DSR, and DSDV. They examined three parameters in carsharing application performance using a Manhattan map generated by TIGER: PSR, Maximum connectivity number (MCN), and vehicle count. As a result, the carsharing performance focuses on two scenarios: the impact of different PSR and MCN on communication performance when the number of cars remains constant and the impact of different numbers of cars when network parameters remain constant. They conclude that improving VANET performance in carsharing systems is possible with a greater number of equipped vehicles and proper control over the maximum connectivity number. Figure 5: Operation of Car Sharing. [PERSON] and [PERSON] [76] improved user service delivery by proposing an effective anonymous authentication scheme capable of detecting and preventing malicious entities from disrupting the carsharing system's operations. The scheme also includes a conditional identity-tracing approach for tracking and exposing a malicious entity by revoking the misbehaving entity's privacy. Their proposed scheme includes five entities: an autonomous vehicle taxi, a taxi user, an autonomous vehicle taxi service provider, a trusted registration center, and a taxi call roadside unit. Each entity in the carsharing system registers with a trusted registration center, which generates an entity-specific pre-private key. The entity later upgrades the pre-private key to a private key.