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A comparative study on indoor localization based on RSSI measurement in wireless sensor network

机译:无线传感器网络中基于RSSI测量的室内定位比较研究

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This paper studies on the localization techniques using in wireless sensor network (WSN) for an indoor environment. We compare two main categories of localization techniques: range-based and fingerprinting-based techniques on a single experimental environment. The received signal strength indicator (RSSI received at sensor nodes are used for all localization techniques. For the range-base techniques, the location estimation approach based on the lateration estimation and the min-max approach are employed. For the fingerprinting based techniques, two pattern matching approaches are used: one is the simple nearest neighbor algorithm and another one is the k-nearest-nearest neighbor algorithm. The experiments are conducted in a meeting room where is a small number of obstacles inside to evaluate the performance of each technique. The results demonstrate that the location estimation algorithm using lateration estimation gives the best accuracy and also less computational time compared to other techniques.
机译:本文研究了在室内环境中在无线传感器网络(WSN)中使用的定位技术。我们比较了两种主要的本地化技术:在单个实验环境中的基于范围的技术和基于指纹的技术。接收信号强度指示符(在传感器节点处接收到的RSSI)用于所有定位技术。对于基于距离的技术,则采用基于延迟估计和最小-最大方法的位置估计方法。对于基于指纹的技术,两个使用模式匹配方法:一种是简单的最近邻算法,另一种是k近邻算法,实验是在一个会议室内进行的,室内有少量障碍物,用于评估每种技术的性能。结果表明,与其他技术相比,使用分层估计的位置估计算法可提供最佳精度,并且计算时间更少。

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