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Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks

机译:无线传感器网络中基于时空分层图的协作定位

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It has been shown that cooperative localization is capable of improving both the positioning accuracy and coverage in scenarios where the global positioning system (GPS) has a poor performance. However, due to its potentially excessive computational complexity, at the time of writing the application of cooperative localization remains limited in practice. In this paper, we address the efficient cooperative positioning problem in wireless sensor networks. A space-time hierarchical-graph based scheme exhibiting fast convergence is proposed for localizing the agent nodes. In contrast to conventional methods, agent nodes are divided into different layers with the aid of the space-time hierarchical-model and their positions are estimated gradually. In particular, an information propagation rule is conceived upon considering the quality of positional information. According to the rule, the information always propagates from the upper layers to a certain lower layer and the message passing process is further optimized at each layer. Hence, the potential error propagation can be mitigated. Additionally, both position estimation and position broadcasting are carried out by the sensor nodes. Furthermore, a sensor activation mechanism is conceived, which is capable of significantly reducing both the energy consumption and the network traffic overhead incurred by the localization process. The analytical and numerical results provided demonstrate the superiority of our space-time hierarchical-graph based cooperative localization scheme over the benchmarking schemes considered.
机译:已经表明,在全球定位系统(GPS)性能不佳的情况下,协作定位能够提高定位精度和覆盖范围。但是,由于其潜在的过大的计算复杂性,在撰写本文时,协作定位的应用在实践中仍然受到限制。在本文中,我们解决了无线传感器网络中的有效协作定位问题。提出了一种基于空时分层图的快速收敛方案,用于对代理节点进行定位。与传统方法相反,借助时空分层模型,将代理节点分为不同的层,并逐步估计其位置。特别地,考虑位置信息的质量来构想信息传播规则。根据规则,信息总是从上层传播到某个下层,并且消息传递过程在每一层都得到了进一步优化。因此,可以减轻潜在的错误传播。另外,位置估计和位置广播均由传感器节点执行。此外,构想了一种传感器激活机制,其能够显着降低定位过程所引起的能量消耗和网络流量开销。提供的分析和数值结果证明了我们基于时空层次图的协作式本地化方案优于所考虑的基准方案。

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