## 5 Performance Evaluation and Simulation
### TL Method  
We provide a DO2 QIEO approach that leverages the TL method in this section. To maximize learning outcomes, effective TL is required. Taking a model that has already been trained in one sector and applying it to a different one has a lot of benefits. Additionally, some of the primary advantages are less training data, higher model generalization, and enhanced DL usability. As a result, TL focuses on collecting and applying information obtained from overcoming these challenges to a distinct but interrelated issue, and this technique may define learning parameters. Offloading experience in similar contexts, such as many outdoor or indoor networks with typical edge nodes, initializes Q-values.  
Additionally, the strategy speeds up learning by reducing random exploration at the start of the dynamic computing process. Almost all of the tasks offloaded to the edge platform were well handled and returned to IoT objects since the edge platform satisfied user expectations. When the edge platform was unavailable or lacked the processing capability to handle new activities, the suggested technique determined that the cloud platform would be used instead. The most serious issue with IoT objects is the issue of power consumption. Data flow and computing are the two factors that determine power. A poor decision may drain a lot of energy. For example, 32-bit processing on an S-RAM chip consumes 5 pico-joules for reading operations, whereas the D-RAM on the exterior of the device consumes 640 pico-joules for the same amount. We must decrease data movements if we are to lower this massive quantity of exponential energy usage. It is here that the correct IoT offloading computation policy comes into play. Therefore, before the network starts, pre-trained data is imported using the TL approach. Although this strategy has some drawbacks, it will shorten network training time and improve network accuracy. Weights and rules can be supplied to the network, and the weights can then be fine-tuned to the data set. Except for specific formulations, TL cannot transfer the representation to the most substantially comparable domains. As previously stated, in any learning but specific problem formulation, there is a lack of reasoning or ability to generate understanding. The network's training time is reduced when this type of TL is used, and the system converges  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters/Hyper-Parameters** & **Value** & **Parameters/Hyper-Parameters** & **Value** \\ \hline Number of IoT devices & 600 & The block interval & 0.5 s \\ Number of controllers and cells & 12 & Each cell’s average transaction batch size & 2 MB \\ Number of users in each cell & 50 & Transaction size on average & 100 MB \\ Number of an edge server & 4 & Controllers’ transmission power & 400 mW \\ Blockchain nodes & 8 & Local edge computing power on average & 20 GHz \\ The workload arrival rate & 10, 20, \(\ldots\), 100 units/s & Controllers’ average computing power & 2 GHz \\ The network congestion & 20, 30, \(\ldots\), 60 ms/unit & Notice power & 1 MBytes \\ Two adjacent time slot decisions & 5 s & \(\lambda\) & 0.5 \\ Computational assignments generation & 100 kb/s & A & 0.9 \\ Bit required to complete & 1000 CPU cycles & E & 0.15 \\ The EH efficiency & 0.51 & \(\Gamma\) & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters Tuning.  
quickly. A network identical to the fundamental simulation environment is required to extract the target parameters. We tested a similar scenario in which the IoT and edge networks were in the same state. As a result, the targeted and extracted weights closely resembled the core simulation situation.