Auto-adaptive multi-hop clustering for hybrid cellular-vehicular networks

In this paper, we consider a hybrid vehicular network, in which vehicles transmit data via the cellular network and dispose of a Vehicle-to-Vehicle (V2V) interface. In this context, we propose an auto-adaptive multi-hop clustering algorithm, which optimizes the usage of the cellular radio resource under the constraint of a maximum packet loss rate (PLR) in the V2V network. The larger the V2V-based clusters are, the higher the data compression ratio at the cluster head is, and the smaller the amount of required resource on the cellular link becomes. However, PLR becomes higher due to the collisions on the V2V channel when increasing the number of hops for cluster enlargement. The proposed algorithm thus dynamically adapts the maximum number of hops in clusters according to the vehicular traffic density. Through simulations, we show that it performs better in terms of aggregated cellular data and packet loss rate than any fixed-hop clustering algorithm in a dynamic scenario.