*4.2. Parameter Analysis*

The performance of PEBN is affected by some time-dependent parameters. First, we analyzed the parameters before comparing the algorithms. In order to improve the performance of the algorithm, we set the reasonable parameter values by experimentally analyzing the update period of the matrix and the node message delivery time interval.

Different time periods, *T*, have different effects on the results of model training. Too short a time period will increase the network resource consumption. If the time period is too long, it will not be able to timely capture the latest dynamic changes of the node, reducing the accuracy of prediction, and affecting the transmission success rate. Therefore, we need to select the appropriate time period to update the predicted target matrix. Through analysis, as shown in Figure 3a, we can observe the fluctuation of the curve. As the time period increases, the transmission success rate increases first and then decreases. The transmission success rate reaches a large value at 175 min. Due to the change of the time period, the average transmission hop count is also affected. If the time period is set too long, it will affect the value of the model training and will be not accurate enough, resulting in more hops. Therefore, it can be seen from Figure 3b that as the time period increases, the hop count decreases and then increases. The phenomenon is because the time period is too short to collect enough information to reduce the error. When the time period reaches 200 min, the hop count is small. We can use the tradeoff method to determine that the period, *T*, is 180 min and increase the number of hops to ensure the delivery ratio.

**Figure 3.** The relationship between delivery ratio and average hop count with time periods T: (**a**) delivery ratio with different T; (**b**) average hop count with different T.

We set the node to pass a message at Δ*t* intervals. When a node transmits a message, a copy of the message is sent from a node within the messaging range that filters EC values greater than those of itself and the destination node. If the interval, Δ*t*, is too short, it will cause the nodes in the network to send the same message copies to the same neighbor node more than once. The neighbor node receives the same message copy and refuses to receive the message, resulting in unnecessary network overhead. If the time interval, Δ*t*, is too long, it will cause the message in the network to spread slowly and

increase the transmission delay of the message. From Figure 4b, we can observe that as Δ*t* increases, the number of nodes covered by message copies transmitted by the node also decreases. During the 16 min to 18 min time period, the number of nodes covered by the message copy is large. However, the transmission of messages after Δ*t* interval also affects the transmission success rate of the nodes. Just because a larger number of nodes are covered by the message does not mean that the delivery ratio can be improved; it may cause route congestion and cause information loss. Therefore, we can observe from Figure 4a that when Δ*t* reaches 20 min, our delivery rate reaches the highest value. Since the value of Δ*t* is between 16 min and 18 min, the node coverage does not fluctuate, and the transmission success rate fluctuates less in the range of 18–20 min. Therefore, we set Δ*t* to 18 min.

**Figure 4.** The relationship between delivery ratio and node coverage ratio with different time interval Δ*t*: (**a**) node coverage ratio different Δ*t*; (**b**) delivery ratio with different Δ*t*.

To sum up, in the model with consideration of the node's transmission success rate and transmission delay variation, we set *T* = 180 min and Δ*t* = 18 min for the following experiments.

### *4.3. Analysis of Simulation Results*
