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Energy consumption and lifetime analysis in clustered multi-hop wireless sensor networks using the probabilistic cluster-head selection method

机译:使用概率簇头选择方法的集群多跳无线传感器网络中的能耗和寿命分析

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Clustering sensor nodes into groups is an effective way of reducing the transmission of duplicated information in energy-constraint wireless sensor networks (WSNs). The performance of clustering is greatly influenced by the selection of cluster-heads, which are in charge of creating clusters and controlling member nodes. In selecting cluster-heads, a probabilistic method where each sensor node selects itself as a cluster-head with a given probability is often used in large-scale and dense WSNs because it enables all nodes to independently decide their roles while keeping the signaling overhead low. In this method, the probability of being a cluster-head should be optimally chosen to maximize the energy efficiency of the nodes. In this article, we propose a novel energy model to estimate the energy consumed in a multi-hop WSN clustered with probabilistic cluster-head selection. Then, based on our model, we determine optimal probability that maximizes the lifetime of a network. Simulation results achieved by the Monte Carlo method show that our model estimates well in energy consumption from a network and also predicts the optimal clustering probability accurately.
机译:将传感器节点分为几组是减少在能量受限的无线传感器网络(WSN)中重复信息传输的有效方法。集群头的选择极大地影响了集群的性能,集群头负责创建集群和控制成员节点。在选择簇头时,每个传感器节点以给定概率将自己选择为簇头的概率方法经常用于大规模和密集的WSN,因为它使所有节点能够独立决定其角色,同时保持较低的信令开销。在这种方法中,应最佳选择成为簇头的概率,以使节点的能量效率最大化。在本文中,我们提出了一种新颖的能量模型,以估计在概率簇头选择聚类的多跳WSN中消耗的能量。然后,基于我们的模型,我们确定使网络寿命最大化的最佳概率。蒙特卡罗方法获得的仿真结果表明,我们的模型可以很好地估计网络的能耗,并且可以准确预测最佳的聚类概率。

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