## 4 Performance Evaluation  
This section presents the results of the simulation of the proposed scheme. We used the latest real-time routing protocol known as MK-AG [33] with the OPNET Modeler to simulate and analyze the proposed scheme. MK-AG selects a path based on multiple criteria, which are combined by the Analytical Hierarchical Process (AHP) in conjunction with the Gray Relational Analysis (GRA).  
Figure 2: Overview of the proposed real-time model.  
However, MK-AG considers the DBP value of the stream without regard to the requirements of the particular application.  
Table 1 provides details of the values we used in our simulation. The nodes are placed in a terrain sized 1000 \(\times\) 1000 m. One hundred nodes were used, of which 10 nodes were placed in the form of a grid, and the remaining 90 were randomly placed. Thus, the node density is determined arbitrarily. The results took the simulation environment into consideration. The transmission and receiving power consumption of a sensor node is 24.92 and 19.72 mJ, respectively, per byte. In order to generate traffic, we employ video traffic model in OPNET. A source node is selected randomly. A source node generate video traffic during random period. After period is over, next node in the source's transmission range is chosen. If the selected node cannot find the next source node, node is selected in backward direction continuously.  
The sink is located in the lower middle region of the field such that the end-to-end hop-count ranges from 4 to 9 hops with an average of 6 hops. The deadline for a real-time packet on each node was set to the average-link-delay \(\times\) the number of shortest hops, respectively. We conduct the separate simulation to obtain average-link-delay. The evaluation result is presented as the stream dynamic success ratio (SDSR) based on stream dynamic failure ratio (SDFR) of the application. SDFR is the ratio sum of dynamic failures to total group of packets as much as \(k\) in the stream (total_number_of_packets/\(k\)). A dynamic failures happens if fewer than \(m\) out of any \(k\) consecutive packets are not delivered within their deadlines. We compute SDSR in following equation  
\[\begin{array}{l}SDSR=(1-SDFR)\\ where\\ SDFR=\frac{sum\_of\_dynamic\_failures}{number\_of\_groups}\end{array} \tag{3}\]  
Additionally, we express the efficiency of the proposed scheme in terms of the network lifetime if the lifetime is prolonged.  
For the (\(m\),\(k\))-firm stream for each \(S_{i}\), the number of available stream sets, \(s\), is set to 3. In addition, the value of \(t\), which is used to monitor the DBP values, is set to 3. The parameter for reducing the packet size, \(k\) is set to 2 in Algorithm 2 such that a payload of 64 bytes is reduced to 32. As the background traffic, we employed three Constant Bit Rate (CBR) traffic streams and varied the amount of traffic to ensure the network is congested. Moreover, the source and destination of the background traffic was randomly chosen and was continuously changed after the predetermined duration. We calculate 95% confidence interval of average SDSR for 20 times evaluations with different 206 seed numbers for source and destination selection. Each simulation is conducted during 1000 s.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value(s)** \\ \hline Terrain & (1000 m, 1000 m) \\ Number of nodes & 100 Nodes \\ Node placement & Uniform \& Random placement \\ Transmission range (m) & 40 m \\ PHY and MAC protocol & 802.15.4 PHY \& MAC \\ Bandwidth & 250 Kb/s \\ Payload size & 64 bytes \\ Reduced payload size & 32 bytes \\ Energy consumption (Tx) & 24.92 mJ per 1 byte \\ Energy consumption (Rx) & 19.72 mJ per 1 byte \\ Traffic model & Video traffic \\ Frame Interval Time & 10 frame/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation environment setting.