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Cooperative localisation over small world WSN using optimal allocation of heterogeneous nodes

机译:使用异构节点的最佳分配在小世界WSN上进行协作定位

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Localisation of sensor nodes in a wireless sensor network (WSN) is an important problem in various applications like cyber-physical systems, Internet of things and context aware pervasive systems. Cooperative localisation using multidimensional scaling (MDS) has been used successfully in many localisation applications. A primary requirement in MDS is the computation of accurate distance estimates between pairs of nodes in a WSN. However, the estimated distances are erroneous in MDS especially for node pairs that are connected using multiple hops. This leads to an overall increase in error of location estimates. A recent development in social networks called small world phenomena can be introduced in a WSN leading to small world WSN (SW-WSN). SW-WSN exhibits low average path length and high average clustering coefficient and yields accurate distance estimates between pairs of nodes. In this work, a novel cooperative localisation method that uses heterogeneous nodes (H-nodes) is proposed over SW-WSN. In addition, two optimal H-node allocation methods are also developed for the cooperative localisation method. The significance of the proposed method in reducing localisation error, energy consumption, and bandwidth requirement is illustrated by simulations and extensive experiments on a real WSN testbed.
机译:无线传感器网络(WSN)中传感器节点的本地化是各种应用程序中的重要问题,例如网络物理系统,物联网和上下文感知普及系统。使用多维缩放(MDS)的协作式本地化已在许多本地化应用程序中成功使用。 MDS的主要要求是计算WSN中节点对之间的准确距离估计。但是,估计的距离在MDS中是错误的,尤其是对于使用多跳连接的节点对而言。这导致位置估计误差的整体增加。可以在导致小世界WSN(SW-WSN)的WSN中引入称为小世界现象的社交网络的最新发展。 SW-WSN具有较低的平均路径长度和较高的平均聚类系数,并且可以在节点对之间产生准确的距离估计。在这项工作中,在SW-WSN上提出了一种使用异构节点(H节点)的新型协作定位方法。此外,还为协作定位方法开发了两种最优的H节点分配方法。通过在真实的WSN测试台上进行的仿真和大量实验,说明了该方法在减少定位误差,能耗和带宽需求方面的意义。

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