# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 4 V2V Communication Architectures
### Edge Computing-Based V2V Architecture  
This architecture uses edge computing nodes strategically located at the network edge, allowing real-time processing of V2V communication data. These nodes process time-sensitive data at the network edge, reducing latency for safety-critical applications [55]. They analyze incoming V2V communication data in real-time, extracting valuable insights for immediate decision-making. Edge computing distributes processing tasks closer to the data source, enhancing responsiveness and reducing the need to transmit large volumes of data to centralized cloud servers [56; 57]. The architecture incorporates dynamic load-balancing mechanisms to ensure the efficient distribution of processing tasks among edge computing nodes. Vehicles in proximity communicate directly using short-range protocols, supporting direct V2V communication without relying solely on centralized cloud resources. The edge computing-based architecture may support the formation of a dynamic mesh network among vehicles, enhancing reliability and allowing communication even in scenarios where direct line-of-sight is limited. Edge computing nodes use encryption and authentication to ensure the confidentiality and integrity of V2V communication data [58]. They also incorporate intrusion detection systems to identify potential security threats. At the edge, real-time processing allows for instantaneous collision warnings and avoidance  
Figure 3: Cloud-Integrated V2V Hub.  
strategies based on nearby vehicle proximity and speed. This technology supports quick analysis of V2V data at intersections, facilitating safer navigation and reducing collision risks in complex traffic scenarios. Edge-based insights contribute to traffic signal optimization by adjusting signal timing based on real-time vehicle flow to alleviate congestion. The architecture may incorporate a hybrid model, integrating edge computing with centralized cloud services for a balance between real-time processing, extensive analytics, and historical data storage. This decentralized approach optimizes resource utilization, improves real-time decision-making, and contributes to the overall effectiveness of connected vehicles in the IoT environment [59].  
Figure 4 illustrates an edge computing-based architecture for V2V communication, integrating edge computing resources into the communication infrastructure. This design enhances the performance, reliability, and efficiency of V2V communication systems. The network edge is represented by automobile icons with onboard communication devices like DSRC or Cellular Vehicle-to-Everything (C-V2X) units. Edge computer nodes are deployed near the cars, acting as localized sites of computation and data processing, reducing latency and bandwidth consumption. Communication links between vehicles, neighboring edge nodes, and other network components enable data exchange. The edge server, a centralized point for managing edge computing resources, handles tasks like resource allocation, load balancing, and job scheduling to maximize the performance and reliability of edge computing processes.