Cross-Layer and SDN Based Routing Scheme for P2P Communication in Vehicular Ad-Hoc Networks

Abstract

Conventional routing protocols proposed for Vehicular Ad-hoc Network (VANET) are usually inefficient and vulnerable for multi-hop data forwarding due to the unavailability of global information and inefficiencies in their route discovering schemes. However, with the recently emerged software defined vehicular network (SDVN) technologies, link stability can be better improved through the availability of global network information. Thus, in this paper, we present a novel software-defined network (SDN) based routing scheme for P2P connection under urban inter-vehicle networks that can find a global optimal route between source and destination. This is a cross-layer routing protocol in VANETs, which utilizes metrics not only considering the position and velocity of vehicles, but also channel allocation and link duration when selecting the relay vehicles. Consequently, it starts a route discovery process which can improve the network performance in terms of end-to-end delay and low overhead. Furthermore, packet loss is largely minimized by the relatively stable paths. With the help of realistic simulation, we show that the proposed routing framework performs better than other three latest SDVN and conventional VANET protocols in routing overhead, average end-to-end delay, packet drop ratio, and average throughput. Therefore, our routing scheme is more suitable for 5G-enabled vehicular ad-hoc networks in future.

A Mini Review of Peer-to-Peer (P2P) for Vehicular Communication

“…The simulation settings that is configured were compared between SDGR with SDN, SVAO with SDN and AODV without SDN. The results have shown that the average end-to-end delay, throughput, overhead and packet drop ratio are superior to the other three protocols [88]. Yang, L., et.…”

SDN-based VANETs, Security Attacks, Applications, and Challenges

“…2020, 10, x; doi: FOR PEER REVIEW 4 of 51 huge mechanical approaches appropriate to the development of associated and autonomous vehicles and unavoidable use of various new tangible gadgets introduced on-board vehicles that encourage in the different scope of agreeable vehicular security applications and programs (i.e., send the crash cautions, crisis vehicles help, (powerless) walker impact relief, overwhelm crossing point cautions and dangerous area cautions). These security applications are basic in nature, yet necessitate a low-dormancy foundation with the most extreme middle-of-the-road postponement running between 10 ms and 50 ms [8]. Moreover, advanced associated vehicles are outfitted with 120 sensors and this number is foreseen to increment up to 250 towards the end of 2020 [9].…”

“…Some of the time, it is likewise conceivable that the SDN controller is not reachable. The greater part of the exploration directed in earlier research did exclude such problems as they pursued the old style of the vehicular systems, for example, the specially ad-hoc on demand distance vector (AODV) [8] and global positioning system receiver (GPSR) [34]. Through an important association among SDN and the Internet of Vehicles (IoV), the idea of programming characterized SDN-IoV is developing.…”

“…Previously we have seen a few recommendations on the recently inferred territory vehicular correspondence systems, where a large portion of the investigations have concentrated on programming characterized organizing [8]. In the previous decade, a recently determined idea of region VANETs has pulled in a lot of research consideration.…”

Double Deep Q-Network with a Dual-Agent for Traffic Signal Control

“…The reward function is based on the difference between the number of vehicles that pass through the intersection during the last time interval and those that are staying. All used information is easily obtained from the traffic signal simulator (e.g., SUMO), which is based on a vehicular network [20,21]. Further, to improve the expansibility and stability of two agents in the traffic signal control, we build a deep neural network to map states to rewards.…”