## 5 Energy Optimization Approach Based on MAC/Routing Cross-Layer (EOAMRCL)
### Clusters Formation Phase (Network Layer)
This pattern indicates that PSO-C is relatively more efficient in conserving node energy initially but struggles to maintain node survivability in the long term. In contrast, the EOAMRCL protocol that implements GWO shows the best performance among the four protocols. The number of live nodes begins to decrease after about 100 iterations, similar to the other protocols, but the rate of decline is much slower. Even at 500 iterations, EOAMRCL manages to retain a significant number of live nodes, highlighting its superior capability in preserving node energy and maintaining network functionality over an extended period. ### Discussion

The analyses of the seven figures collectively highlight the advantages and disadvantages of different Energy Optimization Approaches for WSNs. DWEHC consistently shows the poorest performance across all metrics, indicating inefficient energy management and high overhead costs. The rapid depletion of energy, early node deaths, high clustering overhead, and low packet reception rates all underscore DWEHC's inadequacies in sustaining network performance. EEUC performs better but still experiences significant limitations in maintaining energy efficiency and network performance. While it shows improvements in energy consumption and node longevity compared with DWEHC, the protocol's steady decline in live nodes and moderate clustering overhead reveal its insufficient optimization for extended network operations. CGA-GWO demonstrates notable improvements in energy management, node lifetimes, clustering overhead, and data transmission efficiency. The integration of the GWO algorithm allows for more efficient clustering and routing decisions, enhancing overall network performance. This is evidenced by the balanced energy consumption, delayed node deaths, and higher packet reception rates, indicating that CGA-GWO is effective in extending network life and reliability. Figure 14: Comparison of live nodes of different optimization techniques. However, EOAMRCL outperforms all other protocols, showcasing the benefits of its cross-layer optimization approach. By integrating the MAC and network layers and leveraging GWO, EOAMRCL achieves superior energy efficiency, prolonged node lifetimes, reduced clustering overhead, and improved data transmission efficiency. The protocol maintains the highest average remaining energy, delays node deaths significantly, and ensures the highest percentage of live nodes across all iterations. Its low clustering overhead highlights its efficient resource management, while the high packet reception rates demonstrate its robustness in data transmission. The duty-cycle scheduling at the MAC layer, which alternates nodes between active and sleep modes, further conserves energy and extends the network's operational life. This multifaceted optimization ensures that EOAMRCL not only addresses the limitations of single-layer protocols but also sets a new standard for energy-efficient management in WSNs. Overall, EOAMRCL proves to be the most effective solution for energy optimization in WSNs. Its comprehensive approach, which combines cross-layer optimization with advanced algorithms like GWO, highlights the importance of integrating multiple layers and techniques to achieve optimal network performance. The protocol's ability to manage energy consumption efficiently, maintain network stability, and ensure reliable data transmission underscores its superiority. The findings from the analyses reinforce the critical role of advanced optimization methods in enhancing the sustainability and performance of Wireless Sensor Networks, making EOAMRCL a benchmark for future developments in this field. In terms of comparing GWO with other optimization techniques, the analysis shows that while traditional techniques like FL and DV provide initial efficiency, they fail to maintain node survivability in the long term. Similarly, PSO shows better initial performance but ultimately faces significant node depletion issues.