## 5 Performance Evaluation
### Experiment Setup  
In this study, we deployed a real MEC network environment. As shown in Figure 4, this edge network consists of two APs, three UEs, and two MEC servers. The APs and UEs were all modeled by a Raspberry Pi 4B, which consumes an average of 6.4 W in operation and 2.7 W in idle mode [35]. All UEs were equipped with RPi Camera V2 model 8-megapixel external cameras and were randomly dispersed in an environment with a radius of approximately 5 m centered on the AP. The UEs had a local computational power of 1.5 GHz. The two MEC servers were simulated by personal laptops, which were configured with an 11 th Intel(R) Core (TM) [EMAIL_ADDRESS] GHz CPU, NVIDIA 1650 Ti GPU, and 16 GB RAM. On top of putting the MEC environment in place, we introduced a real-time surveillance recognition system based on the YoloV5 algorithm [36] as a computational task for each UE, with the size of a computational task ranging from 370 kb to 450 kb, generating 20 unprocessed 256 \(\times\) 256 images per second as a pending task. After testing, all UEs and MEC servers fulfilled the requirements for running the YOLOv5 object detection algorithm. Subsequently, we analyzed the actual performance of the random scheme, DQN scheme, A2C scheme, and PPO scheme in edge computing. The network environment parameters and the training parameters of the four schemes are shown in Table 1. The user devices were uniformly distributed in an area with a radius of approximately 5 m. Our proposed PPO Scheme consists of the actor, a policy network responsible for generating actions, together with the critic, a value function network for estimating the value or the advantage of a state to guide the policy updates. The structure of the actor network consists of an input layer consisting of 15 neurons, two hidden layers containing (128, 64) neurons, and an output layer containing a fully connected layer. The weights of the actor network are updated by maximizing the agent's objective function, whereas the weights of the critic network are updated by minimizing the loss of the mean-square error between the estimate of the value function and the true return. The activation function was ReLU, the current empirical data and the Adam optimizer were sampled during the weight update process, and the learning rates of the actor network and the critic network were set to \(1\times 10^{-4}\) and \(5\times 10^{-4}\), respectively. We compared our PPO-based approach against three baseline algorithms: random scheme, DQN scheme, and A2C scheme. These comparisons aimed to demonstrate the superiority of our approach in various MEC scenarios. We compared the proposed PPO algorithmic scheme with a random offloading strategy, DQN strategy, and A2C strategy. * Random Scheme: Each task randomly selects the execution device, including edge servers, migration to other edge server computation, D2D computation, and local computation. * DQN Scheme: The DQN (deep Q-network) algorithm [37] is a deep reinforcement learning method that combines traditional Q-learning and deep learning techniques. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Parameters** & **Values** \\ \hline The bandwidth of the wireless channel & \(W\) & 10 MHz \\ The maximum transmission power of UEs & \(P_{in}^{\mathrm{ULE}}\) & 14.5 dBm \\ The maximum transmission power of the MEC server & \(P_{in}^{\mathrm{ULE}}\) & 20 dBm \\ The data size of the task & \(m_{i}\) & 370–450 kb \\ The computational power of UEs & \(f_{in}^{\mathrm{ULE}}\) & 1.5 GHz/s \\ The computational power of the MEC server & \(f_{in}^{\mathrm{ULE}}\) & 4.2 GHz/s \\ Transmission rate of UEs & \(R_{i,b}\) & - \\ Transmission rate of the MEC server & \(R_{i,c}\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Environment settings. Figure 4: Experimental environment. Its core features include the use of deep neural networks to approximate the Q-function so as to learn strategies directly from the complex.