## 5 Conclusions  
This paper presents a comprehensive evaluation of different CH selection and routing optimization methods, particularly the Chronological Wild Geese Optimization (CWGO) algorithm, in wireless sensor networks using a novel unified performance metric that integrates key factors such as energy, delay, distance, and trust. The unified metric was developed using Lagrange interpolation, which provides a more straightforward approach to evaluating the performance of CWGO and other algorithms across static and dynamic sensor node configurations in various wireless communication technologies, including LoRa, Wi-Fi, Zigbee, and BLE. The unified metric plays an important role since it offers a more comprehensive and balanced assessment of WSN performance, as traditional approaches often focus on individual metrics that fail to capture the network's overall efficiency. The results demonstrate that CWGO consistently achieves the highest unified metric scores across all node configurations and technologies, indicating its superior ability to balance energy efficiency, transmission delay, trust, and data-routing distance. The use of the unified metric reveals CWGO's robustness, not only in small-scale networks but also in large, complex WSN scenarios, particularly in dynamic node setups where environmental changes and mobility are prevalent. The superior performance of CWGO, as revealed by the unified metric, positions it as a highly effective algorithm for optimizing WSNs, particularly in scenarios requiring scalability, energy efficiency, and adaptability to dynamic environments. Despite CWGO's advantages, certain limitations should be acknowledged. One potential drawback is the algorithm's computational complexity, which could impact its performance in resource-constrained environments where processing power and memory are limited. This constraint may lead to reduced efficiency when deploying CWGO in large-scale WSNs, especially in real-time applications. Additionally, while CWGO adapts well in moderately dynamic environments, its performance in highly unpredictable or extreme conditions--such as highly mobile or rapidly changing network topologies--has not been thoroughly explored. Future research could optimize CWGO's computational demands to enhance its applicability in low-resource settings and extend its adaptability to more challenging environments. Introducing adaptive weight adjustments during the optimization process could also provide better flexibility and further enhance the algorithm's performance under varied scenarios. Additionally, validating CWGO's performance in diverse real-world environments would provide practical insights and ensure the algorithm's robustness in a wide range of WSN applications. Exploring hybrid approaches that integrate CWGO with other optimization techniques, such as machine learning or evolutionary algorithms, could offer further improvements in performance, particularly for scenarios with unique constraints or specific application requirements. These hybrid solutions may enhance CWGO's adaptability, scalability, and overall effectiveness in diverse WSN applications. Lastly, future research should expand the range of simulated scenarios to include more varied and extreme network conditions, such as highly mobile sensor nodes and rapidly evolving topologies. Such efforts would allow a more comprehensive understanding of CWGO's strengths and limitations, enabling it to address the unique challenges posed by different application areas. The feasibility of implementing the proposed CWGO in real-world environments, such as smart city infrastructure or industrial IoT systems, also remains an exciting direction for future study. This unified approach to performance evaluation is crucial for future research in WSNs, as it offers a better understanding of algorithmic behavior across diverse applications and network conditions. The findings of this study hold significant practical implications for wireless communication applications.