# Deadline-Aware Scheduling for Transmitted RSU Packets in Cooperative Vehicle-Infrastructure Systems
## 6 Conclusions  
In this paper, we proposed an optimization strategy to minimize end-to-end delays in multi-RSU-distributed packet scheduling based on vehicle carrier relays in CVIS. This strategy determines packet scheduling decisions according to the double buffer queue lengths and vehicle speed status of multiple RSUs on the highway. The advantage of Lyapunov theory is that it can maximize the network performance on the premise of ensuring the stability of the whole network, so Lyapunov optimization theory was used to transform the problem. Lyapunov drift parameters and weights were also introduced to the average  
Figure 11: Comparison of low-priority queues under different algorithms.  
Figure 10: Comparison of high-priority queues under different algorithms.  
Figure 9: Comparison of average time delays under different algorithms.  
system delay and length of high- and low-priority packet queues, which were treated as knapsack problems. This method can dynamically adjust the packet scheduling strategy through the real-time changes of multiple RSUs' high- and low-priority queue lengths and vehicle speed status on the road, ensuring that the packet is transmitted to the destination before the deadline and minimizing end-to-end delays.  
The simulation results show that the average end-to-end delay of the DDPS strategy proposed in this paper is 39% lower than the ESA strategy and 50% lower than the QS strategy when the queue length of the proposed ESA policy is higher and lower than that of the QS strategy and is significantly lower than that of the QS strategy. With the continuous change of parameters, the end-to-end average delay of the DDPS strategy does not change much, so it can be known that the DDPS strategy has good robustness.  
In the assumption of this paper, a single lane of vehicle arrival in a highway section obeys a Poisson distribution with an arrival rate of \(\lambda\), considering that the vehicle arrival rate is independently and identically distributed in multiple lanes, that is, the number of vehicle arrivals in multiple lanes can be expressed as multiple superpositions of a single lane. In the simulation, the DDPS proposed in this paper has little change in system performance when the arrival rate is high, so the DDPS strategy is also suitable for multi-lane scenarios.  
In the hypothetical scenario of this paper, the transmission of data packets does not take into account the influence of the weather environment, and in the real-world scenario, vehicles may be affected by rainy weather and road conditions in the process of data store-carry-forward. Therefore, we will further study this limitation in future work.  
Conceptualization, L.D.; methodology, B.S.; validation, L.D., Y.J. and B.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (2021 YFB2601401), the Natural Science Foundation of Shaanxi Province, China (2019Q-264) and the Fundamental Research Funds for the Central Universities, CHD(300102323201).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.