In wireless sensor network (WSN), increasing the network life span remains as a crucial challenge yet to be resolved. The modeling of effectual methods is necessary for conserving the scarce energy resources in WSN. To overcome such issues, cross-layer protocols are exploited, which concerns routing the messages with increased lifetime. This study introduces a new cross-layer design routing model under a clustering-based approach. More importantly, the cluster head is optimally selected by a new hybrid algorithm termed as moth flame integrated dragonfly algorithm. Moreover, the optimal selection of cluster head is carried out based on parameters such as energy consumption, delay, distance, throughput, security, and overhead. Finally, the supremacy of the presented model is proved over existing models in terms of alive node analysis and network lifetime analysis. The experimental outcomes show that the proposed algorithm for test case 3 has accomplished a higher value of 66.229, which is 29.07%, 13.33%, 26.36%, and 9.67% better than conventional ant lion optimisation approach, grouped grey wolf search optimisation, firefly replaced position update in da, and alpha wolf-assisted whale optimization algorithm, respectively, for median case scenario.

中文翻译：

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密集无线传感器网络中的安全跨层跨域路由：一种新的基于混合的聚类方法

在无线传感器网络 (WSN) 中，增加网络寿命仍然是一个尚未解决的关键挑战。有效方法的建模对于保护 WSN 中稀缺的能源资源是必要的。为了克服这些问题，利用了跨层协议，这涉及以增加的生命周期路由消息。本研究在基于聚类的方法下引入了一种新的跨层设计路由模型。更重要的是，簇头由一种新的混合算法优化选择，称为蛾焰集成蜻蜓算法。此外，簇头的最优选择是根据能耗、时延、距离、吞吐量、安全性和开销等参数进行的。最后，在活动节点分析和网络生命周期分析方面，所提出模型的优越性被证明优于现有模型。实验结果表明，所提算法在测试用例3中取得了更高的值66.229，比传统的蚁狮优化方法、分组灰狼搜索优化、萤火虫替换方法分别提高了29.07%、13.33%、26.36%和9.67% da 中的位置更新和 alpha wolf 辅助的鲸鱼优化算法，分别适用于中值情况。