# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 2 Related Work
The CARRO protocol, similar to DRIVE [24], selects vehicles located in a high-priority geographic region to disseminate a message. In order to create network topology knowledge about the neighboring vehicles at one hop, each vehicle periodically transmits beacons with its position, velocity, and direction. When CARRO detects the network partition problem, the store-carry-forward mechanism is used. However, thismechanism can increase the network delay. In addition, the high number of beacons necessary for the solution work can increase the overhead. [PERSON] et al. (2017) introduced the Clustering Coefficient and node DEGREE protocol (CC-DEGREE), which uses two social metrics to choose the most reliable vehicles to retransmit the message. The first metric is the clustering coefficient, which is defined by the number of connections between the vehicle neighbours divided by the total number of possible connections between vehicle neighbours. The second metric is the node degree, which is defined by the number of one-hop neighbours. The CC-DEGREE protocol operates independently of the road traffic density. However, the CC-DEGREE computes the clustering coefficient metric based only on the position of an individual vehicle, and as such has low variability for dense scenarios and an impact on the choice of nodes to retransmit. This characteristic causes similar wait time assignments to several vehicles, increasing the probability of message collision. [PERSON] et al. (2015) proposed a data dissemination protocol for VANETs based on complex network metrics in an urban scenario, which they named DDRX. In DDRX, vehicles maintain local knowledge of their one-hop and two-hop neighbors, which is used to build a subgraph. Using complex metrics (i.e., betweenness centrality and degree centrality), DDRX selects the best vehicles to retransmit the message. DDRX provides data dissemination with low overhead and delay, maximizing coverage and minimizing the number of packet collisions. [PERSON] et al. (2016) proposed DESTINy (DEcentralized System for Traffic Management), a distributed solution to traffic management that uses the vehicle's computational power to detect congestion and calculate new routes when necessary. To this end, each vehicle receives information about the traffic characteristics of the nearby roads through an auxiliary communication infrastructure (RSU). This RSU is responsible for updating and sending the road information (e.g., the time it took the vehicle to travel on each road) within the defined region. Periodically, vehicles request road characteristics from the nearest RSU; based on this information, the onboard application performs the routing process. Road processing is distributed among vehicles, while the RSU has responsibility for collecting and aggregating road characteristics and sending this information to the vehicles. DESTINy uses a V2V communication protocol based on destination distance. [PERSON] et al. (2019) proposed an efficient vehicular relay selection scheme for Vehicular Delay-Tolerant Networks (VDTNs) to address the problem of dropped packets in these networks. Their paper highlighted the importance of VDTNs in emergency situations when traditional networks fail due to natural disasters or faulty infrastructure. The proposed scheme considers nodes' selfish nature and relay nodes' inefficiency in order to select the most efficient and cooperative relay node for packet forwarding. However, this solution can forward packet replicas to multiple best-choice relays, thereby flooding the network with replicated packages. [PERSON] et al. (2019) proposed the DDP4V protocol for VANET applications, which aims to improve data packet delivery time and reduce latency. This is achieved by suppressing broadcast messages and reducing dispersed packets using NFV (Node Forwarding Value) mechanism. The NFV mechanism assigns a value to each vehicle based on its position and connectivity within the network, and the next hop is chosen based on this value.