Zhang Benhong, Yuan Baorui, Bi Xiang, Wei Zhenchun, Zhang Mingyue
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China.
Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei 230009, China.
Sensors (Basel). 2020 Oct 1;20(19):5612. doi: 10.3390/s20195612.
The Internet of Vehicle (IoV) technology is one of the most important technologies of modern intelligent transportation. The data transmission scheduling method is a research hotspot in the technology of IoV. It is a challenge to ensure the stability of data transmission due to fast network topology changes, high data transmission delays, and some other reasons. Aiming at the above problems, a multi-channel data transmission cooperative scheduling algorithm is proposed. First, construct a feasible interference map based on the data items sent and received by vehicles in the road scene. Second, assign channels to the nodes in the interference map based on the Signal-to-Interference-Noise-Ratio (SINR). Finally, the optimal multi-channel data transmission cooperative scheduling scheme is achieved through the ISing model. Simulation results show that compared with the traditional algorithm, the network service capacity is increased by about 31% and the service delay is reduced by about 20%. It ensures that emergency data is preferentially transmitted to the target vehicle and the maximum weighted service capacity of the network.
车联网(IoV)技术是现代智能交通最重要的技术之一。数据传输调度方法是车联网技术中的一个研究热点。由于网络拓扑变化快、数据传输延迟高以及其他一些原因,确保数据传输的稳定性是一项挑战。针对上述问题,提出了一种多通道数据传输协同调度算法。首先,基于道路场景中车辆收发的数据项构建可行干扰图。其次,基于信号与干扰加噪声比(SINR)为干扰图中的节点分配信道。最后,通过伊辛模型实现最优的多通道数据传输协同调度方案。仿真结果表明,与传统算法相比,网络服务容量提高了约31%,服务延迟降低了约20%。它确保了紧急数据优先传输到目标车辆,并实现了网络的最大加权服务容量。