Quality of Service Aware Routing Protocol in Software-Defined Internet of Vehicles

作     者：Ghafoor, Kayhan Zrar Kong, Linghe Rawat, Danda B. Hosseini, Eghbal Sadiq, Ali Safaa 

作者机构：Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 44045 Peoples R China Howard Univ Dept Elect Engn & Comp Sci Washington DC 20059 USA Cihan Univ Fac Sci Dept Comp Sci Erbil Iraq Monash Univ Sch Informat Technol Subang Jaya 47500 Malaysia 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2019年第6卷第2期

页      面：2817-2828页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China National Key Research and Development Program [2016YFE0100600] China 973 Project [2014CB340303] U.S. National Science Foundation [CNS-1658972, HRD-1828811, CNS-1650831] 

主　　题：Laying chicken algorithm (LCA) quality of service routing algorithm software-defined Internet of Vehicles (SDIoV) 

摘      要：Software-defined Internet of Vehicles (SDIoV) has emerged as a promising field of study as it could overcome the shortcomings of traditional vehicular networks, such as offering efficient data transmission and traffic shaping in different vehicular scenarios to satisfy all the requirements of applications on the fly. Although routing solutions are lightly addressed for SDIoV, there are many limitations of routing protocols unaddressed in such environment. More precisely, shortest path routing algorithms are mostly focused in the state of the arts. This paper presents quality of service aware routing algorithm that forwards packets toward the most reliable and connected path to the destination. Particularly, candidate routes should satisfy metrics, such as signal to interference and noise ratio (SINR) constraint and have the highest probability of connectivity. To address these issues, we have formulated a discrete optimization problem to favor the best route among candidate paths and proposed the modified laying chicken algorithm (LCA) that results better results than the traditional approaches. We have mathematically analyzed the probability of connectivity along with the SINR metric. Moreover, a multiscore function based on traffic density and greediness factor is proposed to make intelligent decision at the intersections. Simulation results are used to validate the superiority of the proposed routing approach over the existing solutions.