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首页> 外文期刊>International Journal of Networking and Virtual Organisations >Optimisation of energy efficient cellular learning automata algorithm for heterogeneous wireless sensor networks
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Optimisation of energy efficient cellular learning automata algorithm for heterogeneous wireless sensor networks

机译：异构无线传感器网络高效节能的蜂窝学习自动机算法的优化

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摘要

Wireless sensor networks is an effective sensing network consisting of a large number of small sensors and small embedded devices each with sensing, computation and communication capabilities for gathering data in various environments. Energy consumption is considered to be an important issue in the design of wireless sensor networks. To overcome the above limitation, efficient method like cellular learning automata (CLA) and heterogeneous-hybrid energy efficient distributed (H-HEED) technique have been used in distributed dynamic clustering networks. The existing method will be the cellular learning automata in which cluster heads will be selected through several stages by considering various parameters with homogeneous nodes. The proposed method selects the cluster head in a similar way and based on the residual energy of the nodes with heterogenous nodes. Their performance is observed using NS2 simulator and comparison has been made to find the best efficient method.
机译：无线传感器网络是一种有效的传感网络，它由大量的小型传感器和小型嵌入式设备组成，每个设备都具有用于在各种环境中收集数据的传感，计算和通信功能。能量消耗被认为是无线传感器网络设计中的重要问题。为了克服上述局限性，在分布式动态聚类网络中使用了诸如蜂窝学习自动机（CLA）和异构混合节能高效分布式（H-HEED）技术之类的高效方法。现有的方法将是细胞学习自动机，其中通过考虑具有均质节点的各种参数，通过多个阶段选择簇头。所提出的方法以相似的方式并且基于具有异构节点的节点的剩余能量来选择簇头。使用NS2模拟器观察了它们的性能，并进行了比较以找到最佳的有效方法。

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