# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 3 Model of VEN with Privacy Preservation  
### Scenario Description  
In this work, we implemented a vehicular edge network (VEN) including an MEC server, a set of RSUs denoted by \(\mathcal{R}=\{1,\,2,\ldots,\,R\}\), and a set of ICVs, which are denoted as \(\mathcal{V}=\{1,\,2,\ldots,\,V\}\), on a length L multi-lane road. As shown in Figure 1, a regional MEC server receives and transmits workloads between several RSUs via the optical fiber link. Each RSU's coverage range is assumed as \(r\), which equally divides the road into \(R\) segments. The ICVs are randomly and independently distributed in lanes on the road with an arrival rate \(\lambda\). Within the coverage of the RSU, ICV transmits task data between RSU by V2I communication. RSU is assumed as a traditional communication node in the VEN without computation capacity, which is considered as an equipment for message forwarding between the MEC server and the ICVs. Equipped with an onboard unit (OBU) and massive sensors such as cameras, mmWave radar, and lidar, ICV has a certain amount of computation capacity, collecting information from the surrounding environment and sending different types of messages periodically to the RSU or other ICVs within its communication range. Suppose that the speed of the ICVs follows a truncated Gaussian distribution, which is more appropriate with the practical environment to avoid dealing with the negative speeds [29]. In addition, the ICVs in the single lane follow the Poisson spatial distribution with density \(\frac{V}{L}\)[17], which makes the ICVs' equivalent speed \(\overline{s}=\frac{L\lambda}{V}\)[30]. The driving characteristics \(\{p_{i},\,s_{i}\}\) for ICV-\(i\) are periodically broadcasted to RSU and other ICVs with wireless links, where \(p_{i}\) represents the position coordinates of ICV-\(i\) on the road space and \(s_{i}\) is the immediate speed of ICV-\(i\) at its current position; \(\min\{s_{i}\}\ \leq\ \overline{s}\leq\max\{s_{i}\},\ i\in V\). In our proposed road segment, the distance between different lanes is relatively small compared with the road length; therefore, the distance difference between different lanes is not considered in this work. Here, we assume that \(p_{i}\) is a one-dimensional position coordinate and that the starting point of the road is zero. In this case, \(p_{i}\) represents the distance between the position of ICV-\(I\) and the starting point of the road. Assume that each ICV produces a computation task in a certain time period. The vehicular task developed by ICV-\(i\) is denoted as \(T_{i}\), and the task arrival probability is denoted as \(P_{task}^{arr}\). The key parameters of \(T_{i}\) can be characterized by a quintuple \(\{C_{i},D_{i}^{in},D_{i}^{out},t_{i}^{max},Q_{i}\}\) to characterize the profile of a mobile application, where \(C_{i}\) is the computing resource required to finish the vehicular task. The sizes of input and output data generated by the task execution \(D_{i}^{in}\) and \(D_{i}^{out}\) are related to the data bits for the task's input and output. The maximum completion deadline \(t_{i}^{max}\) denotes the maximum number of successive time slots before the vehicular task must be completed. The priority variable of the vehicular task \(Q_{i}\) is associated with the security level and urgency of the vehicular task. According to the existing standards and use cases of the vehicular network, we divided the vehicular tasks into three categories: traffic-safety-related tasks, traffic-efficiency-related tasks, and other vehicular entertainment tasks. Tasks related to ICV safety concern the lives of drivers and occupants; therefore, these vehicular tasks have the highest priority. Although  
Figure 1: The system model of the VEN with privacy preservation. traffic-efficiency-related tasks have strict delay requirements, which may not involve security threats, we define these vehicular tasks as having normal priority.