# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 4 V2V Communication Architectures
### AI-Driven Dynamic Network for V2V Communication  
The infrastructure features an AI controller that dynamically allocates communication resources based on real-time conditions using machine learning algorithms and predictive analytics. Each vehicle is equipped with V2V communication modules, such as Dedicated Short-Range Communication (DSRC) or C-V2X technologies. The AI controller analyzes real-time data, including traffic patterns, vehicle density, and communication requirements, to make informed decisions about network resource allocation. It uses predictive modeling based on historical data to anticipate future communication demands, proactively  
Figure 6: Hybrid Cellular and Ad-Hoc Network-based V2V.  
allocating resources for upcoming traffic conditions and events. The system dynamically allocates resources like bandwidth, frequency channels, and transmission power based on evolving V2V communication needs and the traffic landscape. Machine learning algorithms identify peak hours, congestion points, and areas with frequent communication demands, guiding resource allocation strategies [69]. The AI controller learns from historical performance and adjusts its strategies to enhance V2V communication efficiency. Decentralized communication ensures low latency and instantaneous data exchange between vehicles.  
Figure 7 shows an AI-driven dynamic network allocation system for V2V communication, enhancing efficiency, reliability, and performance. AI algorithms optimize network resources and manage communication paths in real-time, allowing mobile units like cars, trucks, and drones to exchange data, share information, and collaborate on applications like traffic management and collision avoidance. The road infrastructure, including roads, intersections, and traffic signals, affects communication propagation, signal strength, and network topology. The AI controller, a computer or cloud server equipped with AI algorithms and machine learning models, is the core component of the system. The AI controller uses real-time data from the V2V communication network to make intelligent decisions about network resource allocation and communication management. It dynamically adjusts parameters like transmission power, frequency channels, and routing patterns to optimize performance, eliminate interference, and increase network throughput. The controller also uses a feedback loop to monitor and adapt to network conditions, ensuring responsive and adaptive communication management. The infrastructure may support the formation of a dynamic mesh network among vehicles, enhancing reliability even in limited direct line-of-sight scenarios. Security considerations are integrated into the allocation process, ensuring resources adhere to protocols like encryption and authentication. The system incorporates anomaly detection mechanisms to identify unusual patterns or potential security threats in communication behavior, triggering adaptive responses to mitigate risks. The AI-driven system optimizes communication resources to address traffic congestion, prioritizes and allocates resources during emergencies, and adapts to dynamic traffic scenarios. The system dynamically reallocates resources to address emerging communication demands in real-time.  
Figure 7: AI-Driven Dynamic Network for V2V Communication [70].