## 4 Evaluation and Results  
PureEdgeSim simulator 1.0 stands as the pinnacle solution for simulating large-scale Internet of Things (IoT) environments. With its distributed and dynamic infrastructure, researchers can examine the complexities of highly heterogeneous applications in IoT networks. The simulator boasts realistic infrastructure models that facilitate the in-depth exploration of mist-to-cloud interactions. Moreover, PureEdgeSim covers all layers of \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Population size & 100 \\ \hline Iterations & 10 \\ \hline loudness\_min: A\_min—loudness & 0 \\ \hline loudness\_max: A\_max—loudness & 0.8 \\ \hline pr\_min: pulse rate/emission rate min & 0 \\ \hline pr\_max: pulse rate/emission rate max & 0.95 \\ \hline pf\_min: pulse frequency min & 0.0 \\ \hline pf\_max: pulse frequency max & 10 \\ \hline Lower bound of values in the population & 0 \\ \hline Upper bound of values in the population & The count of VMs \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IMBA parameters. computing modelling and simulation, as depicted in Figure 1[18], providing researchers with a holistic platform to investigate various aspects of IoT systems. In this study, we conducted a simulation employing mist nodes ranging from 50 to 300, serving as internal resources, while external resources comprised one edge data centre and one cloud data centre. The performance evaluation of our proposed bat algorithm--IMBA --was carried out under escalating task loads, starting from 5000 tasks up to 30,000 tasks. Several key parameters were scrutinised to assess performance, encompassing the number of completed tasks, CPU utilisation, processing time, LAN and WAN usage, and energy consumption. The performance study analysed various metrics and compared them against fuzzy logic-based algorithms and round robin scheduling. Initially, the task completion count was scrutinised as the primary metric, revealing that the proposed IMBA achieved the highest count of completed tasks, as illustrated in Figure 2. Furthermore, the IMBA exhibited a notably shorter processing time compared to the fuzzy logic-based algorithm, while its processing time closely aligned with that of the round robin algorithm, as depicted in Figure 3. Notably, Figure 2 shows that the IMBA completed approximately 700 more tasks than the round robin algorithm. This observation unequivocally highlights the superior performance of the IMBA in terms of processing time, thereby outperforming both the fuzzy logic-based and round robin algorithms. Figures 4 and 5 provide a comprehensive analysis of the load reduction achieved over the cloud and edge, thereby enhancing the system's performance. Notably, the proposed IMBA demonstrated remarkable effectiveness in alleviating the load burden on both the central cloud server and edge when compared to the fuzzy logic and round robin algorithms. Figure 1: End point to cloud infrastructure. Figure 4: Total number of tasks successfully completed in cloud. Figure 3: Processing time in s.  
Figure 2: Total number of tasks successfully completed. However, it is worth noting that the fuzzy logic approach exhibited a lower count of completed cloud tasks, primarily attributable to task failures amounting to approximately 700 tasks. This failure resulted in an elevated CPU usage within the cloud infrastructure for the fuzzy logic approach. In contrast, the IMBA successfully minimised CPU usage in both the cloud and edge, aligning closely with one of the primary objectives of the IMBA approach, as evidenced by Figures 6 and 7. This outcome underscores the efficacy of the IMBA in optimising resource utilisation across diverse computing environments in IoT networks. The research findings underscore the effectiveness of our proposed IMBA in minimising energy consumption across external resources in both the cloud and edge layers. This achievement is illustrated in Figures 8 and 9.