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Sensor localization using received signal strength measurements for obstructed wireless sensor networks with noisy channels

机译:使用接收信号强度测量的传感器定位,用于受噪声干扰的受阻无线传感器网络

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This paper proposes a new approach to cope with the challenges in sensor localization in an obstructed environment even in the presence of channel noise. In particular, a progressive localization scheme is proposed that does not necessitate the need for any cluster as most existing schemes do. Initialization is done by generating a quadrilateral of sensor nodes where the connectivity weight between them is computed based on the estimated distance from a reference point, which may be any vertex of the quadrilateral. Distances between sensor nodes are mapped from a path-loss model that is governed by the NLOS and Rayleigh fading models, considering noisy communication channel. With the initial quadrilateral characterized, the other sensor nodes are localized in a progressive manner based on the same mapping model. Errors due to presence of obstacles and noisy channel are reduced by studying the estimated distances contributed from the neighboring sensor nodes. Efficiency of our proposed scheme is measured in terms of total power dissipation for localization and the total degree of neighboring nodes required for error reduction. Apart from simulation experiments, we verify our proposed scheme using real hardware deployment in both indoor and outdoor environments. Results reveal that the proposed scheme improves localization precision substantially.
机译:本文提出了一种新方法来应对在受阻环境中甚至在存在信道噪声的情况下传感器定位的挑战。特别是,提出了一种渐进式定位方案,该方案不需要像大多数现有方案那样需要任何群集。通过生成传感器节点的四边形来完成初始化,其中传感器节点之间的连接权重是基于与参考点的估计距离来计算的,该参考点可以是四边形的任意顶点。传感器节点之间的距离是从路径损耗模型映射的,该模型由NLOS和瑞利衰落模型控制,并考虑了嘈杂的通信信道。具有初始四边形的特征后,其他传感器节点将基于相同的映射模型以渐进方式进行定位。通过研究相邻传感器节点贡献的估计距离,可以减少由于障碍物和噪声通道的存在而导致的错误。我们提出的方案的效率是根据用于定位的总功耗和减少错误所需的相邻节点的总程度来衡量的。除了仿真实验,我们还使用室内和室外环境中的实际硬件部署来验证我们提出的方案。结果表明,该方案大大提高了定位精度。

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