## 6 Simulation Results and Discussions
### Simulation Scenarios and Results  
This subsection of the research paper discusses and evaluates the simulation results. We analyze the behavior of various protocols, including LDIWPSOC [40], PSO-EEC [39], OFCA [41], NPSOP [42], and our proposed protocol, both without routing and with multiple hops. Considering the parameters listed in Table 9, two scenarios are examined for the simulation. Specifically, we deployed 100 SNs within a 100 \(\times\) 100 m\({}^{2}\) area and 300 SNs within a 500 \(\times\) 500 m\({}^{2}\) area. In both scenarios, 10% and 5% of the total SNs are used as the number of DFGs, the same as the comparable protocols. However, our protocol has a dynamic number of DFGs. A comparison is performed using three performance metrics: network lifespan, energy consumption, and throughput assessments. 3.1 Simulation Scenario 1: Alive Nodes Versus Diversity of Network Sizes (Network Scalability) Evaluation  
Network scalability is crucial in IoT networks, which are widely used for extensive monitoring and data collection across various applications. Data fusion grouping and routing protocols with strong scalability can effectively manage network growth and changes while maintaining performance, energy efficiency, and data transmission quality. In this study, we examine how network size affects the network lifetime of our proposed protocol. We consider various network sizes, i.e., 100 \(\times\) 100 m\({}^{2}\) and 500 \(\times\) 500 m\({}^{2}\), and with different network densities, i.e., 100, 300, and 600 nodes. Figure 10 illustrates how network lifetime varies with HND and LND across different IoT network sizes and node densities. The comparison includes LDIWPSOC, PSO-EEC, OFCA, NPSOP, our proposed protocol without routing, and our proposed protocol with multi-hop routing. As illustrated in Figure 10, the network lifetime decreases as the network size increases. This result is expected, as larger network sizes lead to greater communication distances between SNs and their PDFHs, as well as between PDFHs and the sink node. Consequently, the network's energy is depleted more quickly. All the protocols fall short when compared to the proposed protocol. This difference can be attributed to the exceptional contributions of our proposed protocol, beginning with dividing the network into a dynamic number of hexagonal DFGs, which offers several benefits, particularly in enhancing the efficiency and coverage of the network area. Unlike traditional square or rectangular grids, hexagonal data fusion grouping allows for more uniform coverage with fewer overlaps or gaps, thanks to the geometric properties of hexagons. This layout is advantageous as it reduces interference between SNs, as each one in the DFG is equidistant from its neighbors, optimizing the transmission range and minimizing signal loss. Furthermore, hexagonal data fusion grouping can facilitate energy savings, as SNs in the DFG can more effectively manage resources and coordinate communication, leading to improved network longevity. This arrangement is especially favorable in large-scale IoT deployments, such as smart cities, where efficient data transmission, energy conservation, and reliable connectivity are critical. Another contribution is the strengths of the MPA used in the proposed protocol. It excels at balancing exploration and exploitation, effectively navigating the PDFH selection problem. It also has a strong ability to escape local optima, a challenge for many traditional optimization methods, as mentioned in Table 2. Its adaptability is especially useful in the PDFH selection process, where it evaluates key factors to choose the best PDFHs. By prioritizing high-energy SNs and avoiding low-energy SNs, the method helps to prevent premature failures and ensures a more balanced network load. The proposed protocol reduces energy consumption during network message exchange by integrating the data fusion grouping and routing phases.