TY - JOUR
T1 - CPA-MAC
T2 - A Collision Prediction and Avoidance MAC for Safety Message Dissemination in MEC-Assisted VANETs
AU - Liu, Bingyi
AU - Deng, Dongxiao
AU - Rao, Wenbi
AU - Wang, Enshu
AU - Xiong, Shengwu
AU - Jia, Dongyao
AU - Wang, Jianping
AU - Qiao, Chunming
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61802288, in part by Major Technological Innovation Projects in Hubei Province China under Grant 2019AAA024, in part by the Key Research, and Development Program of Hainan Province, China under Grant ZDYF2021GXJS014, in part by Hong Kong Research Grant Council under Grant NSFC/RGC N_CityU 140/20, and in part by National Science Foundation under Grant 1737590.
Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - Vehicular ad hoc networks (VANETs) have been widely recognized as a promising solution to improve traffic safety and efficiency for the ability to provide situation awareness even though the potential dangers and traffic anomalies are out of the visual range. In VANETs, time-division multiple access (TDMA) based overlay protocols can prevent transmission collisions and play an important role in providing an efficient communication channel. However, due to high vehicle mobility and time-varying traffic flow, the existing TDMA-based slot allocation approaches cannot fully utilize the channel resources, resulting in high transmission delay and packet collision. To overcome these shortcomings, we propose a collision prediction and avoidance MAC (CPA-MAC) protocol that utilizes the capability of mobile edge computing (MEC) and machine learning in this paper. Specifically, we propose a new slot assignment method that aims to guarantee the high channel utilization and low delay of safety message under dynamic traffic conditions. Furthermore, we propose a new same-direction collisions prediction algorithm that combines the V2R communication and LSTM-based trajectory prediction algorithm. Finally, we conduct extensive experiments to demonstrate the effectiveness of the proposed protocol.
AB - Vehicular ad hoc networks (VANETs) have been widely recognized as a promising solution to improve traffic safety and efficiency for the ability to provide situation awareness even though the potential dangers and traffic anomalies are out of the visual range. In VANETs, time-division multiple access (TDMA) based overlay protocols can prevent transmission collisions and play an important role in providing an efficient communication channel. However, due to high vehicle mobility and time-varying traffic flow, the existing TDMA-based slot allocation approaches cannot fully utilize the channel resources, resulting in high transmission delay and packet collision. To overcome these shortcomings, we propose a collision prediction and avoidance MAC (CPA-MAC) protocol that utilizes the capability of mobile edge computing (MEC) and machine learning in this paper. Specifically, we propose a new slot assignment method that aims to guarantee the high channel utilization and low delay of safety message under dynamic traffic conditions. Furthermore, we propose a new same-direction collisions prediction algorithm that combines the V2R communication and LSTM-based trajectory prediction algorithm. Finally, we conduct extensive experiments to demonstrate the effectiveness of the proposed protocol.
KW - Collision prediction
KW - Medium access control
KW - Mobile edge computing
KW - Slot assignment
KW - VANETs
UR - http://www.scopus.com/inward/record.url?scp=85121377252&partnerID=8YFLogxK
U2 - 10.1109/TNSE.2021.3133480
DO - 10.1109/TNSE.2021.3133480
M3 - Article
AN - SCOPUS:85121377252
SN - 2327-4697
VL - 9
SP - 783
EP - 794
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
IS - 2
ER -