## 5 Results and Discussion
### Overall Performance Evaluation
#### 5.3.3 Throughput to Base Station  
In the context of heterogeneous wireless sensor networks (HWSNs) deployed within IoT systems, throughput is a crucial performance indicator. It represents the total volume of data successfully gathered by the sensor nodes and transmitted to the base station [58]. Given the integrity of data collected and transmission in IoT applications, evaluating throughput provides valuable insight into the overall efficiency of energy-conscious routing protocols, which are integral to maintaining network longevity and reliability.  
Figure 23 illustrates the comparison between the proposed balanced-energy routing scheme, which incorporates both cluster-head rotation and relay-node rotation, and state  
Figure 21: Number of live nodes vs. number of rounds.  
Figure 22: Comparison of overall network lifetime: (a) FND scale; (b) LND scale.  
of-the-art protocols such as RLEACH [55], CRPFCM [56; 57], and EERPMS [57]. The results demonstrate that the proposed method achieves a significant increase in throughput, outperforming its peers across all tested scenarios. Specifically, the proposed method shows a 17.63% higher data packet delivery rate compared to EERPMS [57], a protocol designed for energy efficiency and routing in heterogeneous networks. This substantial improvement can be attributed to the more dynamic role-rotation strategies employed by the proposed method, which ensure that cluster heads and relay nodes are selected based not only on their residual energy but also on their location and data generation characteristics, leading to optimal energy utilization.  
Moreover, when comparing the proposed method with CRPFCM [56; 57] and RLEACH [55], the throughput increase is even more noticeable. The proposed method delivers 51.75% more data packets than CRPFCM and 57.44% more than RLEACH. These enhancements can be critically examined in light of the shortcomings in the competing protocols. RLEACH, for instance, primarily focuses on random and periodic rotation of cluster heads without sufficient consideration of heterogeneity in node energy levels or traffic loads, which often leads to uneven energy depletion and reduced network effectiveness. CRPFCM, while incorporating fuzzy clustering techniques, lacks the fine-tuned balance between intra-cluster and inter-cluster communication, particularly in large-scale heterogeneous networks. This limitation results in suboptimal data transmission rates and premature exhaustion of energy in certain nodes, particularly cluster heads.  
The significant improvement in throughput achieved by the proposed method underscores the importance of a multi-layered approach to energy management in HWSNs. By balancing energy consumption not only within clusters but also across clusters through relay-node rotation, the method ensures that the network remains operational for longer periods, allowing more data to be transmitted to the base station before nodes start to fail. Furthermore, the incorporation of flexible relay-node selection criteria, based on both distance and energy considerations, minimises the energy overhead associated with multi-hop communication, further contributing to the enhanced throughput.  
These results highlight the efficacy of the proposed method in addressing the key challenges of energy efficiency and data transmission in heterogeneous IoT networks. The ability to deliver significantly more data packets while maintaining balanced energy consumption across the network is a critical advantage, particularly in scenarios where large-scale data collection and long network lifetimes are paramount.