TY - JOUR
T1 - A Fluid Mechanics-Based Model to Estimate VINET Capacity in an Urban Scene
AU - Cheng, Jiujun
AU - Yuan, Guiyuan
AU - Zhou, Meng Chu
AU - Gao, Shangce
AU - Liu, Cong
AU - Jiang, Changjun
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB2100801, in part by the U.S. National Science Foundation (NSF) under Grant CMMI-1162482, in part by the NSFC under Grant 61872271 and Grant 61902222, in part by the Fundamental Research Funds for the Central Universities under Grant 22120190208, and in part by the Open Foundation of State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications under Grant SKLNST-2020-1-20
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - Accurate estimation of network capacity is very important for Vehicular Infrastructure-based NETwork (VINET) in an urban scene that may involve greatly dynamic typology and complex driving conditions. The node mobility, network behavior, and network scale of a VINET are different from those of a wireless network, and, therefore, the existing capacity estimation methods of wireless networks cannot be used to estimate VINET capacity. In addition, most existing studies on VINET capacity only derive asymptotic descriptions when the number of nodes is large enough. In this work, a novel approach is proposed for the modeling and calculating VINET capacity. More specifically, we first analyze communication characteristics in a VINET, and introduce two transmission modes, i.e., a vehicle-based mode and a Road Side Unit (RSU)-based one. Then, we propose a probability-based transmission mode selecting strategy with which vehicle nodes can choose either transmission mode independently and such choice is probabilistic. Next, we analyze the characteristics of an RSU-based mode, divide a VINET into a number of communities according to the position and communication range of RSUs, and derive the capacity contributed by an RSU-based mode. Then, we calculate the capacity contributed by a vehicle-based mode based on fluid mechanics. Finally, the VINET capacity can be calculated. The proposed VINET capacity estimation approach is validated to be consistent with simulation results.
AB - Accurate estimation of network capacity is very important for Vehicular Infrastructure-based NETwork (VINET) in an urban scene that may involve greatly dynamic typology and complex driving conditions. The node mobility, network behavior, and network scale of a VINET are different from those of a wireless network, and, therefore, the existing capacity estimation methods of wireless networks cannot be used to estimate VINET capacity. In addition, most existing studies on VINET capacity only derive asymptotic descriptions when the number of nodes is large enough. In this work, a novel approach is proposed for the modeling and calculating VINET capacity. More specifically, we first analyze communication characteristics in a VINET, and introduce two transmission modes, i.e., a vehicle-based mode and a Road Side Unit (RSU)-based one. Then, we propose a probability-based transmission mode selecting strategy with which vehicle nodes can choose either transmission mode independently and such choice is probabilistic. Next, we analyze the characteristics of an RSU-based mode, divide a VINET into a number of communities according to the position and communication range of RSUs, and derive the capacity contributed by an RSU-based mode. Then, we calculate the capacity contributed by a vehicle-based mode based on fluid mechanics. Finally, the VINET capacity can be calculated. The proposed VINET capacity estimation approach is validated to be consistent with simulation results.
KW - VINET capacity
KW - Vehicular infrastructure-based networks
KW - fluid mechanics
KW - interference community
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U2 - 10.1109/TITS.2021.3083812
DO - 10.1109/TITS.2021.3083812
M3 - Article
AN - SCOPUS:85111049747
SN - 1524-9050
VL - 23
SP - 8606
EP - 8614
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 7
ER -