论文标题
基于重力搜索算法的大规模无线传感器网络的能量平衡的两级聚类
Energy Balanced Two-level Clustering for Large-Scale Wireless Sensor Networks based on the Gravitational Search Algorithm
论文作者
论文摘要
簇中的传感器节点是无线传感器网络（WSN）中能量保存的有效方法。在整个研究工作中，我们提出了一种新型的混合聚类方案，该方案将典型的梯度聚类方案与进化优化方法相结合，该方法主要基于重力搜索算法（GSA）。提出的方案旨在提高大型网络的性能，在大多数情况下，经典方案导致了非效率的解决方案。它首先会创建合适的平衡多台簇，其中传感器的能量越来越靠近簇头（CH）。在提议方案的下一阶段，基于GSA运行的合适协议将群集头的集合与特定的网关节点相关联，以最终将数据传递到基站（BS）。适当地选择了健身函数，考虑了从簇头到网关节点的距离和网关节点的剩余能量的距离，并进一步优化了它，以便在大型实例中获得更准确的结果。扩展的实验测量表明，在非常大的WSN上，提出的方法的效率和可扩展性以及其优于文献中其他已知聚类方法的优势。
Organizing sensor nodes in clusters is an effective method for energy preservation in a Wireless Sensor Network (WSN). Throughout this research work we present a novel hybrid clustering scheme, that combines a typical gradient clustering protocol with an evolutionary optimization method that is mainly based on the Gravitational Search Algorithm (GSA). The proposed scheme aims at improved performance over large in size networks, where classical schemes in most cases lead to non-efficient solutions. It first creates suitably balanced multihop clusters, in which the sensors energy gets larger as coming closer to the cluster head (CH). In the next phase of the proposed scheme a suitable protocol based on the GSA runs to associate sets of cluster heads to specific gateway nodes for the eventual relaying of data to the base station (BS). The fitness function was appropriately chosen considering both the distance from the cluster heads to the gateway nodes and the remaining energy of the gateway nodes, and it was further optimized in order to gain more accurate results for large instances. Extended experimental measurements demonstrate the efficiency and scalability of the presented approach over very large WSNs, as well as its superiority over other known clustering approaches presented in the literature.
