ABSTRACT Nowadays, vehicular applications for traffic safety, traffic efficiency, and entertainment have put forward higher requirements to vehicular communication systems. The fifth-generation (5G) cellular networks are potential to provide high-capacity low-latency communications for vehicles in highly mobile environments. In addition, the IEEE 802.11p-based dedicated short-range communication technology is proposed to form vehicular ad hoc networks (VANETs). Integrating cluster-based VANETs with 5G cellular networks is beneficial for saving scarce spectrum resources, preventing network congestion, and reducing packet loss. However, it is a challenging problem to find an effective clustering algorithm that has high stability adapting to dynamic VANETs. Considering the advantages of software-defined networking (SDN), in this paper, we present an SDN-enabled social-aware clustering algorithm in the 5G-VANET system, which exploits a social pattern (i.e., vehicles' future routes) prediction model in order to enhance the stability of clusters. Each vehicle's movement is modeled as a discrete time-homogeneous semi-Markov model, where the state transition probability and sojourn time probability distribution are inputs and each vehicle's social pattern is output. The predicted social patterns are subsequently used to create clusters so that vehicles in the same cluster tend to share the same routes. Cluster heads are selected based on the metrics of intervehicle distance, relative speed, and vehicle attributes. We evaluate our algorithm and the results show that it performs better in terms of cluster lifetime and clustering overhead compared with traditional clustering algorithms. INDEX TERMS 5G-VANET, software-defined networking, social pattern prediction, clustering.
The authors in REF proposed a social-aware cluster algorithm in the 5G-VANET system, which exploited a social pattern prediction model to enhance the stability of clusters.
SDN-Enabled Social-Aware Clustering in 5G-VANET Systems
IEEE Access
IEEE Access
Computer Science