# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
## 4 Case Study
### Deployment Plans of the Homogeneous Highways  
In this case study, 100 cells are divided for deployment, and this is labeled as scenario I. It is assumed that each cell is identical and the accuracy rate of monitoring traffic is only influenced by the number of cells. The deployment plan is shown in Figure 7. The total number of cells selected to install sensors is 23, where 20 cells are for cameras and 3 cells are for RSUs. In Figure 7, the green block represents the position of deploying MEC servers. It can be seen that cells 7, 23, 31, 73, 74, and 93 are picked to place servers. The service scope of each server is also depicted. For example, the server placed at cell 7 is used to connect sensors of cell 1 to 13, and the server at cell 31 is mainly used to serve sensors from cell 25 to 36. With the constraint of capacity and communication range, the number of cells associated with each server is limited. The goal of this paper is to maximize the accuracy rate. As a result, the optimized plan intends to choose the most efficient combination type, i.e., [camera, camera], which makes the number of cameras more than that of RSUs. Due to the limited budget, three RSUs were arranged in this scheme. If the maximum budget is increased, RSUs might not be used to monitor traffic. In Figure 7, most sensors and servers are deployed at the front and tail cells of the researched highway. There is no budget for installing sensors from cells 37 to 64. Since the cost of the sensor-deployment plan illustrated in Figure 7 is USD \(2.1\times 10^{5}\) (CNY 1.5 million), adding extra sensors from cells 37 to 64 will make the total cost exceed the maximum budget W. This indicates that some cells could not be monitored under this maximum budget, which might be not limited to this region. So Figure 7 shows one of the optimal plans for scenario I. It should be noted that cells 73 and 74 are both selected to install servers. It seems that two servers placed at cell 73 or 74 are more reasonable compared with the optimized plan. Although this might not be considered a profitable plan,  
Figure 6: The accuracy rate distribution of 100 cells. Figure 7: The sensor deployment plan for homogeneous road cells. it is in fact acceptable with preset parameters. This is because the communication range is set to be eight. Cell 73 cannot be connected with cell 82 and cell 65 will fail to deliver the message to cell 74. In short, it needs two servers to receive the message delivered from cell 65 to 82. The goal of increasing the accuracy rate might narrow the distance between two adjacent sensors, which leads to the objective value of some parts being relatively high. This is not beneficial for monitoring the whole region. Hence, the objective function in Equation (11) is modified to balance the accuracy rate and covered regions as follows. \[max\quad\alpha\sum_{i,j\in\mathbf{N}\cup\{0,n+1\},k\in\mathbf{S}}\pi^{k}_{ij }x^{k}_{ij}+(1-\alpha)\sum_{i,j\in\mathbf{N}\cup\{0,n+1\},k\in\mathbf{S}} \rho^{k}_{ij}x^{k}_{ij}, \tag{17}\]  
where \(\rho^{k}_{ij}\) denotes the coverage rate and \(\alpha\) denotes the weight coefficient. The layouts of sensors in eleven groups of tests when parameter \(\alpha\) is changed from 0 to 1 are depicted in Figure 8. The horizontal axis represents the location of sensors (which also can be regarded as labels of road cells), and the vertical axis denotes the value of parameter \(\alpha\). A filled dot indicates a sensor placed at that position. It is obvious that the number of total used sensors is not varied in a wild fluctuation, which is changed over the interval [21; 25]. This implies that parameter \(\alpha\) has little impact on the total number of required sensors. Nevertheless, the consistency of deployment plans of these sensors is not high. For example, the beginning and ending cells of \(\alpha=0.4\) are 12 and 100, while occupied cells for \(\alpha=1.0\) range from 66 to 100.