# Deadline-Aware Scheduling for Transmitted RSU Packets in Cooperative Vehicle-Infrastructure Systems
## 3 System Model  
On the highway section of L-length, transferring data packets to \(RSU^{\prime}\) through a single RSU may cause the following problems:  
1. Considering the maximum capacity of the data cache queue of the RSU, when the monitoring data of the road section is stored in the data cache queue of the RSU, the data cache queue of the RSU will be full quickly, and the data packets that arrive later will overflow, resulting in the loss of available data packets.
2. When the arrival rate of vehicles on the road section is high, a single RSU only sends data packets to one vehicle in each time slot, which may cause multiple vehicles to have no data packets to carry, so the stability of the data cache queue can't be guaranteed.
3. When the data packet arrival rate is high, considering that there are fewer data packets that can be sent in each time slot of a single RSU, the data cache queue length of the RSU will remain in a higher state, and the higher data queue length will lead to larger average queuing delay, which will affect the overall performance of the system.  
In this paper, we consider that N RSUs are evenly distributed on the highway section of L-length. each RSU observes its coverage and collects data and stores it in its data cache queue. DDPS makes data packet scheduling decisions according to the corresponding states of N RSU in each time slot to avoid the above problems.  
As shown in Figure 1, RSUs are deployed on highways. N RSUs in remote areas, such as mountains and forests, which consider economic benefits and other factors, cannot access the backbone network. Therefore, many RSUs deployed in remote areas use mobile vehicles as transmission media to forward data packets from mobile vehicle carriers to backbone networks. The data cache queue of RSUs is divided into emergency and non-emergency data cache queues. Emergency data mainly include extreme weather, forest fires, and static obstacles on the road. Non-emergency data are mainly daily traffic, environmental data, and other monitoring data.  
Based on the speed and queue status, scheduling determines whether data packets are sent, how many, and what type of data packets are sent to vehicles. The data should be transmitted to the backbone before the deadline to ensure the relevant departments can handle the consequences of emergency and non-emergency data. In remote areas, \(l\) is the distance between two RSUs, and \(L^{\prime}\) is the shortest distance between the destination node \(RSU^{\prime}\) and the isolated RSUs. Since \(L^{\prime}\) is much larger than the communication range of RSU, the latter can be ignored. RSU indexes of \(n\in\{1,2,\ldots,\text{N}\}\) are deployed in the scenario of L-length highway sections. The system time is discretized, the index is \(t\in\{0,1,\ldots,\text{T}\}\), and the length of each slot is represented by \(\tau\). When the vehicle enters the coverage area of \(RSU_{n}\), \(RSU_{n}\) unloads the collected data packets onto it. Using the data scheduling controller, \(RSU_{n}\) determines a scheduling strategy for processing data packets in the data queues. Figure 2 shows that the data transmission control model comprises vehicle speed state \(y_{n}(t)\), data queues length \(Q_{n}^{H}(t),Q_{n}^{L}(t)\), packet arrival number \(h_{n}(t)\), queue selection decision \(b_{n}(t)\), and data packet transmission decision \(d_{n}(t)\).