## 4 Discussion
This is also the case for DUCISCA and DE-SEP, where they started the simulation with an identical fitness value, but their performance gap widens, and a decline in their performance value is observed across rounds. The performance of each algorithm with an increasing number of sensor nodes across different wireless technologies for both static and dynamic node setups demonstrates their scalability across various application areas. The result presented in Tables 3-10 shows that the proposed CWGO achieves the highest unified metric value across all node configurations for each wireless technology. This is particularly noteworthy in the LoRa and Wi-Fi setups, where CWGO's unified metric value increases with larger node counts, indicating its superiority in handling larger network sizes. The same trend is observed in the dynamic node setup, where CWGO continues to show its scalability and adaptability in changing network conditions. In contrast, EECHIGOW consistently underperforms across all wireless technologies, especially in larger-sensor-node configurations. For instance, in Zigbee and BLE setups, EECHIGOW shows a significant drop in performance, with its fitness reaching as low as \(-1.4397\) for 100 nodes in Zigbee. Similarly, in the dynamic Zigbee setup, EECHIGOW starts poorly, with a fitness value of \(-1.0884\) for 100 nodes, and only slightly improves to \(0.7101\) for 1000 nodes. DUCISCA and DE-SEP perform moderately across most technologies, but their fitness values fluctuate more as the number of sensor nodes increases, suggesting they may not be as reliable for larger or more complex WSN setups. This fluctuation can be observed in both static and dynamic setups. For example, in the dynamic Zigbee setup, DUCISCA starts with \(-1.0144\) for 100 nodes but improves to \(0.8249\) for 1000 nodes. Although this shows better performance over time, it still lags behind CWGO's dynamic performance, which reaches \(1.3055\) for 1000 nodes in Zigbee. DE-SEP similarly shows moderate improvement but cannot surpass CWGO's consistent lead in dynamic environments. E-CERP demonstrates competitive performance across all node configurations, particularly in Wi-Fi and BLE setups; however, it still falls short of surpassing CWGO performance, especially in larger-node setups. In addition to assessing the overall performance trends in the proposed CWGO algorithm, a deeper examination of the factors contributing to the performance degradation of the comparison algorithms--DE-SEP, E-CERP, DUCISCA, and EECHIGOW--highlights their respective strengths and limitations in various WSN configurations and environments. Specifically, the performance decline of EECHIGOW across all wireless technologies can be attributed to its limited energy conservation mechanisms, particularly in energy-constrained environments like Zigbee and BLE. This algorithm's lack of efficient energy management results in faster energy depletion, leading to a more rapid decrease in fitness value as the simulation rounds progress. Furthermore, both DUCISCA and DE-SEP demonstrate moderate but inconsistent performance, with noticeable fluctuations in fitness values as the number of sensor nodes increases. These fluctuations are more prominent in dynamic WSN setups, where node mobility and network density changes increase the complexity of maintaining optimal CH selection and routing paths. The observed fluctuations suggest that DUCISCA and DE-SEP may be less adaptable to environments with high variability, which could limit their applicability in highly dynamic WSN scenarios. For E-CERP, although it performs competitively in static setups with fewer nodes, its fitness value begins to lag behind CWGO in larger or more dynamic configurations. This may be due to E-CERP's limited scalability and reduced adaptability when dealing with larger networks and complex routing requirements. The degradation in performance over time suggests that while E-CERP is suitable for moderate-scale WSNs, it faces challenges in maintaining high efficiency in larger, resource-intensive setups.