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A Novel Clustering Algorithm by Using Mobile Gateways in Densely Deployed Sensor Networks

机译:一种新的聚类算法通过在密集部署的传感器网络中使用移动网关

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Clustering is an efficient and important method to facilitate energy conservation. Aiming to alleviate the high over-heads of migrating management information in the re-clustering process of a clustering algorithm, an energy efficient, Complete Graph-based Clustering Algorithm (CGCA) is proposed in a densely deployed sensor network. CGCA divides the network into a few complete graphs, each complete graph independently being a cluster. CGCA is only executed at the system activation time and the cluster head role needs only to be rotated among the internal nodes in each cluster at the subsequent re-clustering phase, which incurs greatly reduced communication and computation overheads. Another contribution of our work is to add some mobile nodes to act as gateways so as to join the disconnected cluster heads, which puts the heavy inter-cluster traffic burden on the resource-rich mobile nodes thus saving much energy in the energy constrained sensor nodes. Extensive simulation experiments demonstrate that the number of exchanged messages produced by CGCA is only about 20% that of the traditional identifier based clustering algorithm in a densely deployed case. Furthermore, our proposed achieves an improvement in system lifetime of factor 2 that of the LEACH in a dense sensor network.
机译:聚类是一种有效和重要的方法,可促进节能。旨在缓解聚类算法的重新聚类过程中迁移管理信息的高度头,在浓密部署的传感器网络中提出了节能,基于图形的聚类算法(CGCA)。 CGCA将网络划分为几个完整的图形,每个完整的图形都独立于群集。 CGCA仅在系统激活时间执行,并且只能在随后的重新聚类阶段的每个群集中的内部节点中旋转群集头角色,这引发了大大减少了通信和计算开销。我们的工作的另一个贡献是添加一些移动节点以充当网关,以加入断开连接的群集头,这将富裕的移动节点上的群集间群集流量负担施加,从而节省了能量受限传感器节点中的大量能量。广泛的仿真实验表明,CGCA产生的交换消息的数量仅为浓密部署的情况下的传统标识符基于群集算法的约20%。此外,我们的提议实现了在密集传感器网络中的因子2的系统寿命的改善。

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