Fair Self-Adaptive Clustering for Hybrid Cellular-Vehicular Networks

Due to the increasing number of car-centered connected services, making efficient use limited radio resources is critical in vehicular communications. Hybrid networks dispose multiple Radio Access Technologies (RATs) like cellular and vehicle-to-vehicle (V2V) networks, with complementary characteristics that allow for developing smarter network traffic distribution methods. This paper proposes a self-adaptive clustering system ensuring suitable trade-off between data aggregation (over network) communication congestion due cluster management (within V2V network). The system's algorithms distributive justice approach selecting heads, improve fairness among car drivers hence help social acceptability clustering. Simulation results show this significantly improves over time without affecting performance. solution can thus optimize usage resources, reducing access costs, need uniformization different mobile operators' plans.