Authors:
Jing Zhao
1
;
2
;
Hao Zhou
2
;
YanBin Wang
3
;
HuaLin Lu
4
;
Zhijuan Li
4
and
XiaoMin Ma
5
Affiliations:
1
Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen 518052, China
;
2
School of Software Technology, Dalian University of Technology, Dalian 116024, China
;
3
Department of Industrial Engineering, Harbin Institute of Technology, Harbin 150001, China
;
4
Department of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
;
5
College of Science and Engineering, Oral Roberts University, Tulsa, OK 74171, U.S.A.
Keyword(s):
Vehicle-to-Vehicle Communication, MPI, Signal-to-Interference-to-Noise, Capacity, QoS.
Abstract:
Vehicular Ad-hoc Networks (VANETs) have been proposed and investigated for road safety applications. Many safety applications are enabled by broadcasting basic safety message (BSM) periodically. Whether the current IEEE802.11p communication system can meet the stringent quality of service (QoS) requirement for safety applications is under discussion. Many analytical and simulation models have been proposed to study the reliability of DSRC (Dedicated Short Range Communication) IEEE802.11p broadcast services. However, most analyses assume a deterministic communication range, which is unpractical. In this paper, we propose an analytical model based on signal-to-interference-plus-noise ratio (SINR) to study of QoS and capacity of VANET for BSM based safety applications. The analytical model considers the context of the more practical vehicular communication environment: BSM broadcast, asynchronous timing between hidden terminals, Nakagami channel fading, and Non-Homogeneous Poisson Proce
ss vehicle distribution. For the proposed model, the computation complexity of QoS and capacity metrics by numerical solutions is so high that the computation time is intolerable. Thus the efficient numerical way together with a parallel approach is needed to evaluate these metrics. The Monte Carlo integration and MPI (Message Passing Interface) method are applied for accelerating the computing process. The analysis of QoS metrics are validated by NS2 simulation.
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