## 4 Performance Evaluation  
In this section, we present the simulation environment and experiments discussion. The experiments were conducted in OMNET++ [38; 39], which is widely used by the research community to simulate network technologies and standards. We ran the simulations on a laptop with a 16 M cache, 256 GB RAM, and a 4.40 GHz Intel processor. The performance was evaluated against existing schemes over a 1000 \(\times\) 1000 m area. We considered the transmission range of each node to be 10 m. To evaluate the security significance, we deployed 10 malicious nodes randomly. Moreover, switches and routers were deployed with the POX controller. Initially, the energy resource of each sensor node was set to 2 j. We executed the simulation for 2000 s. The size of the data block was fixed to 32 bits. The experiments were conducted based on the delivery rate, network overhead, processing delay, and packet loss rate under varying network nodes and data receiving rates. The proposed model was compared with existing solutions, i.e., MaOEA-DRP and smart collaborative routing protocol, as explained in [30; 35]. The default parameters are listed in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulation area & 1000 \(\times\) 1000 m \\ \hline Sensor nodes & 100–500 \\ \hline Malicious nodes & 10 \\ \hline Data block, k & 32 bits \\ \hline Initial energy & 2 j \\ \hline Transmission power & 5 m \\ \hline Simulation interval & 2000 s \\ \hline Transmission radius & 5 m \\ \hline Data flow & Periodic \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Default parameters. Figure 2: Flow chart of the proposed model. In Figure 3, the experimental results illustrate that the proposed model improves the packet delivery rate by 30% and 40%, as compared to other solutions. Thus, the proposed model utilizes a multi-hop transmission system for routing the MIoT data and optimizes the forwarding decision. The intelligent decision determines the packet variability factor for the neighbor nodes and increases the packet delivery performance. Furthermore, the communication medium is protected in the existence of malicious packets and efficiently utilizes resource management. Accordingly, the proposed model minimizes the ratio of congestion on the wireless channels and achieves robust transmission. The proposed model makes use of mobile edge, computing high-performance nodes for collecting the MIoT data from sensors, and increases the efficacy of data management. Unlike other solutions, the proposed model decreases the exchange of control messages among the nodes and ultimately improves the throughput of the MIoT network. Figure 4 illustrates the performance of the proposed model for routing overheads in the comparison of existing solutions. It is noticed that the proposed model reduces the routing overheads by 38% and 44%, respectively, under a varying number of nodes. Unlike the existing solution that frequently exchanges control and route request messages among nodes in case of a larger network size, the proposed model explicitly avoids such practice. It only selects the MIoT node as a data forwarder when the selection criteria are less than a certain threshold. Moreover, the proposed model efficiently utilizes the energy resource of the MIoT nodes and decreases the rapidly routing messages. Moreover, due to the mobile edge computing nodes, the MIoT nodes enforce the least communication costs in forwarding and choosing the optimal route. Accordingly, the proposed model imposes fewer overheads on the part of MIoT nodes and improves the network performance by selecting the more reliable routes. Figure 5 demonstrates the performance results, in terms of processing time for the proposed model against other solutions. Based on the experiments, it is seen that the proposed model improves the time delay by 24% and 33% than the exiting work. The improvement is due to balancing the energy and data-forwarding load among the MIoT nodes using the packet variability factor.