Supporting vehicular emergency applications requires fast access to infrastructure so vehicles can call for help. Because of their environment's poor wireless qualities, vehicle infrastructure communication paths lack security. Modern authentication systems used to close security vulnerabilities require a lot of computing and storage power from the vehicle's OBU. Thus, anovel5G automobile network emergency situationsbased onChebyshev polynomial using fog computing and authentication is proposed.This is the first study to useChebyshev chaotic mapping algorithmthatuses a chaotic map, rotation, and XOR to generate a one-way hash and eliminate modular multiplication index or scalar multiplication on the elliptic curve. The proposed scheme hasfivestages: installation system, initialization, enrollment, mutual authentication, and emergency request. Critical to emergency services, the suggested protocol works better in resource-limited settings like car systems. Theformalsecurity analysis reveals that the suggested approach guarantees message authenticity and integrity, non-repudiation, traceability, unlinkability, identity privacy, certificate independence, and emergency response.The evaluation showed thatthe proposed method cannot execute forgery, impersonation, or replay attacks.Theproposed method has lower computational, communication, and storage overhead than earlier efforts.

Recent advancements in computation and communication technologies and the increasing adoption of the Internet of Things (IoT) and Artificial Intelligence (AI) technologies have paved the way to tremendous developments in modern transportation systems. Driven by the massive number of connected vehicles and the stringent requirements of the public traffic management system, the transportation of data to and from the centralized cloud servers poses a great challenge. As a result, to meet the computational requirements and handle the massive amount of sensory data efficiently, the potential solution is to process/analyze the data at the edge of the network. Motivated by the challenges mentioned above, in this paper, we design a new empirically intelligent XGboost (EIXGB)-enabled logistic transportation system at the edge network for analyzing the data efficiently. Besides that, the proposed EIXGB technique intends to obtain real-time results based on the monitoring parameters of the public traffic management system with higher accuracy and minimum error. Extensive simulation results demonstrate the efficiency of the proposed EIXGB technique over the standard machine learning techniques using a set of parameters. The proposed technique achieves 87-97% accuracy over the different sets of features of a real-time dataset as per the simulation results.