Efficient Cluster-Based Routing Protocol for VANET Traffic Forecasting with Hybrid Optimization Algorithm

Intelligent transportation system integration is becoming increasingly dependent on robust and energy-efficient protocols for communication between vehicles and infrastructure (V2V and V2I) networks. This study offers an energy-efficient cluster-based routing protocol for interaction between V2V and V2I, with a focus on traffic forecasting. To improve communication efficiency in dynamic vehicle contexts, the protocol makes use of the capabilities of vehicle ad-hoc networks (VANETs). A novel approach is provided to optimize the routing process and meet the issues posed by variable traffic situations. The suggested method combines two advanced hybrid optimization algorithms: The Adaptive Binary Bird Swarm Optimization Algorithm (ABBSOA) and the Adaptive Golden Eagle Optimization Algorithm (AGEOA). The goal of hybridizing these algorithms is to capitalize on their complementary capabilities, resulting in a synergistic impact that improves the protocol's adaptability and efficiency. Through its feeding, guarding, and flying imitation behaviors, ABBSOA, inspired by the collective behaviors of birds, focuses on global optimization, outperforming local optima. The golden eagle hunting patterns served as inspiration for AGEOA, which introduces indirect and direct effects for improved convergence and solution quality. For efficient communication management, the protocol adopts a cluster-based design, with cluster heads (CHs) dynamically determined using the hybrid optimization algorithm. The ABBSOA-AGEOA-based strategy improves the CH selection process by optimizing job distribution among less burdened CHs in changing traffic circumstances. The simulation results indicate that the proposed cluster-based energy-efficient. The routing protocol performs better than current protocols with regard to traffic forecasting accuracy, packet delivery rate (PDR), packet loss rate (PLR), throughput, network lifetime (NLT), cluster lifetime, cluster build time, and energy consumption end-to- end latency. Comparative simulations demonstrate the suggested method's advantage over current VANET routing techniques like SFSR, ICMFO, and FA-OLSR, as shown by metrics using the proposed systems ABBSOA and AGEOA, which work well together to get through the complicated networks of vehicles. This makes the protocol a new way for V2V and V2I to communicate in smart transportation systems that will last for a long time.