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Modeling of the Modified Ssle in Opnet for Large Scale Wireless Sensor Networks

机译:用于大规模无线传感器网络OPNET中修改的SSLE的建模

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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:与所感测数据一起的位置信息对于无线传感器网络(WSN)中的大多数应用是重要的。本文介绍了一种基于修改的部门SWEEPER基于SWEEPER的定位估计(SSLE)方法,其改进了我们先前研究的SSLE方法的版本,并在OPNET中建模。该技术需要简化的消息格式。只需要具有智能天线处理能力的中心节点估计传感器节点的位置。修改后的SSLE是使用具有逻辑普通阴影效果的Opnet Modeler进行建模。本opnet无线模块使用标准日志距离模型而无需任何阴影效果。我们通过根据无线环境向用户提供能够选择从0 dB到5 dB的横向效果的方差来添加日志普通阴影效果。 opnet中的详细实现方法在节点和流程模型方面呈现。根据本地化误差和平均吞吐量,通过不同的网络和信道参数评估修改的SSLE的性能。仿真结果表明,修改后的SSLE方法可以在日志普通阴影通道条件下实现9.5%的平均定位误差。

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