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Modeling of the modified SSLE in OPNET for large scale wireless sensor networks

机译:用于大型无线传感器网络的OPNET中修改的SSLE建模

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Location information along with sensed data is important for most of the applications in wireless sensor networks (WSNs). This paper presents a modified sectoral sweeper based localization estimation (SSLE) method which is improved version of our previously studied SSLE method and it's modeling in OPNET. This technique requires simplified message formats. Only a central node which needs to have smart antenna processing capability estimates locations of sensor nodes. The modified SSLE is modeled using OPNET modeler with log-normal shadowing effects. The present OPNET wireless module uses standard log-distance model without any shadowing effects. We add log-normal shadowing effects by providing a user with ability to choose variance of shadowing effects from 0 dB to 5 dB according to the wireless environment. The detailed implementation methodology in OPNET is presented in terms of node and process models. The performance of modified SSLE is evaluated through different network and channel parameters in terms of localization error and average throughput. The simulation results show that the modified SSLE method can achieve with 9.5% average localization error under log normal shadowing channel conditions.
机译:位置信息以及感测到的数据对于无线传感器网络(WSN)中的大多数应用都很重要。本文提出了一种改进的基于扇区清扫器的定位估计(SSLE)方法,它是我们先前研究过的SSLE方法的改进版本,并在OPNET中进行了建模。此技术需要简化的消息格式。只有需要具有智能天线处理能力的中央节点才能估计传感器节点的位置。修改后的SSLE使用具有对数法线阴影效果的OPNET建模器建模。当前的OPNET无线模块使用标准的对数距离模型,没有任何阴影效应。通过为用户提供根据无线环境选择从0 dB到5 dB的阴影效果方差的能力,我们添加了对数正态阴影效果。 OPNET中的详细实现方法论以节点和过程模型的形式给出。可以通过不同的网络和通道参数根据定位错误和平均吞吐量来评估修改后的SSLE的性能。仿真结果表明,改进的SSLE方法在对数法线阴影信道条件下平均定位误差可达9.5%。

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