AK-GPSR: An Adaptive K-Medoids-Based Greedy Perimeter Stateless Routing Algorithm for Multi-Channel Vehicular Network Communication

As a direct application of 5G communications and computer technology, Vehicular Ad-Hoc Networks (VANETs) are already having a profound impact on all sectors of society. However, frequent changes in the topology of VANETs have resulted in poor vehicle communication quality, highly susceptible to communication link breaks and data transmission reliability decreases, and the cost of vehicular communication increases continuously. In this paper, an Adaptive K-medoids based on Greedy Perimeter Stateless Routing (AK-GPSR) algorithm is proposed in multi-channel vehicular network communication of urban scenario. It is an unsupervised learning algorithm, aiming to form high-quality link communication, more stable network topology, and improved data information transmission reliability. First, the proposed AK-GPSR algorithm applies Gap statistic to evaluate the K-medoids algorithm and select the best K value. Further, the K-medoids algorithm clusters the vehicles in the simulation area by the optimal K-value to divide the K clusters. Finally, the packets are forwarded from the source vehicle to the destination vehicle or Road Side Unit (RSU) using the forwarding method of the Greedy Perimeter Stateless Routing (GPSR) algorithm. The experimental results show that our proposed AK-GPSR algorithm has good performance and applicability in multi-channel vehicular network communication.