Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC-Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy-efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC-Gauss). The results show that the proposed DEEC-Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.

中文翻译：

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无线传感器网络中高能效优化的创新超启发式高斯聚类方案

无线传感器网络（WSN）中传感器节点的能量稳定性始终是一个重要的挑战，尤其是在数据捕获和数据包传输过程中。现有文献中为提高能效而提出的分布式聚类算法的最新进展表明，传感器节点的部署，网络持续时间稳定性以及通过信道传输到基站的信息数据的吞吐量都得到了改进。但是，在异构WSN环境中，仍然存在很大的能源改进范围。本研究使用高斯消去方法与分布式能效聚类（称为DEEC-Gauss）相结合，以确保无线环境中的能效优化。使用新颖的DEEC-Gauss聚类算法的基本原理是，它填补了文献中的空白，因为研究人员在拥有100个节点（1,000到5000个）的WSN中无法进行节能优化之前，无法使用该方案。回合仍然可以实现快速的时间输出。在这项研究中，通过仿真，将高度发达的聚类算法DEEC，EDEEC_E和DDEEC的性能与提出的高斯消除聚类算法（DEEC-Gauss）进行了比较。结果表明，提出的DEEC-Gauss算法使第一个节点失效（FND）的平均百分比提高了4.2％，第十个节点失效（TND）的平均百分比提高了2.8％，交付的总时间增加了，与其他现代算法相比进行了优化。