Energy-efficient multicast routing protocol based on SDN and fog computing for vehicular networks

Vehicular networks have been expanding significantly to perform several applications and strategies related to vehicles, ambulances, traffic jam, drivers, and even passengers. The most important challenge in this network is routing data among vehicles. Therefore, there is a need to design efficient routing protocols for unicast, Geocast, multicast and broadcast transmission modes. The multicasting can be used in many application fields such as emergency, police, and firefighting. There is a large body of studies on multicast routing in vehicular networks. However, safety applications in vehicular networks require a special multicast routing protocol that takes into account the deadline and existing bandwidth constraints. On the other hand, there has been a growing tendency towards electric cars in recent years. Therefore, energy consumption is one of major parameters that should be considered in the design of this routing protocol. The goal of this paper is to present a new Energy Efficient Multicast routing protocol based on Software Defined Networks and Fog computing for Vehicular networks called EEMSFV including deadline and bandwidth constraints. Multicast routing with multiple constraints of QoS has been proved to be a NP complete problem. The proposed architecture consists of four layers: vehicles, fog computing, OpenFlow switches and SDN controller. Moreover, a priority based scheduling algorithm and a classification algorithm to schedule the multicast requests based on their application type and deadline constraint after classifying them are proposed. The partitioning concept is used to decrease time complexity and overhead in the SDN controller. From the simulation results, we concluded that EEMSFV is better than MABC and CVLMS in terms of successfully transmitted ratio, average end to end delay, normalized overhead load, multicast energy consumption, packet loss ratio and percentage of critical multicast sessions that meet the deadline. (C) 2018 Elsevier B.V. All rights reserved.