SDN-enabled Traffic Alert System for IoV in Smart Cities

被引:0
|
作者
Raja, Gunasekaran [1 ]
Dhanasekaran, Priyanka [1 ]
Anbalagan, Sudha [2 ]
Ganapathisubramaniyan, Aishwarya [1 ]
Bashir, Ali Kashif [3 ]
机构
[1] Anna Univ, Dept Comp Technol, Chennai, Tamil Nadu, India
[2] SRM Inst Sci & Technol, Dept Comp Sci & Engn, Chennai, Tamil Nadu, India
[3] Manchester Metropolitan Univ, Dept Comp & Math, Manchester, Lancs, England
关键词
IoV; VANET; Vehicle-to-Vehicle; Vehicle-to-Infrastructure; Software Defined Network; Broadcast storm;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Intelligent Transportation System (ITS) are helping to enhance road safety and traffic management applications. Internet of Vehicles (IoV) plays a promising role in this field, which turns each vehicle into a smart object with its own compute, storage, and networking capabilities. Nowadays, accidents have been increased mainly due to un-notified alerts about other accidents, work-in-progress, and excessive motorized vehicles at peak times. This non-line of sight information can be efficiently delivered using vehicular communication. IoV network, however has its own challenges like high mobility and dynamic network topology. The above mentioned challenges are addressed with the assistance of a centralized Software Defined Network (SDN), which isolates the control plane from the data plane. In IoV, SDN provides logically centralized traffic management and improves the vehicular communication. In this paper, the Software Defined-Internet of Vehicles (SD-IoV) system is designed to manage heavy traffic and avoids broadcast storm problem with high packet delivery ratio. The proposed broadcast routing mechanism uses selective forwarding and neighbor awareness of the vehicle to efficiently broadcast emergency alert messages, thereby avoiding traffic jams and reducing travel time. On-Board Unit (OBU) in vehicles detects the accident and initializes the broadcast algorithm in SD-IoV system. The accident detection by OBU in vehicles is simulated using machine learning technique with an accuracy of 90%. Simulation performed in SUMO and OMNeT++ shows that with the help of the SDN controller, the IoV network achieves a high packet delivery ratio with minimal delay.
引用
收藏
页码:1093 / 1098
页数:6
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