Semi-Markov model for vehicular green transmission

Abstract To integrate the Fifth Generation (5G) Vehicular ad hoc network (VANET) based clustering in cellular networks is useful for improving the throughput, saving scarce spectrum resources and preventing network congestion, as well as reducing the packet loss. However, it is a challenging problem to find an optimized clustering algorithm that has efficient stability adapting to dynamic VANETs. Considering the advantages of software-defined networking (SDN), an the 5G VANET system is employed with an SDN-enabled social-aware adaptive clustering algorithm (SESAC) which exploits a social pattern (i.e., future route) of every vehicle to optimize the stability of each cluster. To design the homogeneous Semi-Markov model for discrete time, which is based on the movement of each vehicle. The state transition probability and exact time probability dissemination are inputs while social patterns of each vehicle’s are output. The anticipated social patterns are generally used to make groups(cluster); where the vehicles are in a similar cluster, the cluster head (CH) will share a similar way (similar route). The cluster heads (CHs) are selected by some specific metrics such as inter vehicle distance, relative speed, and attributes of vehicle. Finally the SESAC algorithm is analyzed and compared with the conventional clustering algorithm.