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Multi-sensor data fusion for low power transmission of wireless sensor network in a greenhouse

机译：温室无线传感器网络低功耗的多传感器数据融合

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摘要

The Wireless Sensor Network (WSN) is widely used for the acquisition of distributed greenhouse micro-climate data, such as temperature, humidity, CO2 concentration and solar radiation. In this paper the communication reliability of a WSN was evaluatedbased on experimental data within the greenhouse. In order to reduce redundant and invalid data to achieve a low power transmission, this paper proposes two kinds of multi-sensor data fusion scheme depending on the characteristics of the parameters. Forparameters that change slowly (such as temperature, humidity, and CO2), a time fusion algorithm which can detect the time consistency of data is used to reduce the amount of data transmitted. The spatial fusion based on the supporting vector is used togive the final decision variable for the control system. For rapidly-changing parameters such as solar radiation, this paper proposes the Dual Prediction Scheme (DPS) based on an adaptive model selection to reduce the amount of data transmitted, with a spatial fusion algorithm based on GIA (Grey Incidence Analysis) for the aggregator. The data fusion schemes were implemented and tested in a greenhouse at the Tongji University Jiading Campus. The implementation results showed that the data fusion schemescan guarantee the sensor precision, and also significantly reduce the amount of data transmitted, which means prolonging the lifetime of the network.
机译：无线传感器网络（WSN）广泛用于采集分布式温室微观气候数据，例如温度，湿度，二氧化碳浓度和太阳辐射。在本文中，WSN的通信可靠性在温室内的实验数据中进行了评估。为了减少冗余和无效数据来实现低功率传输，本文提出了两种多传感器数据融合方案，具体取决于参数的特性。用于缓慢变化的分数（如温度，湿度和CO2），可以检测数据的时间一致性的时间融合算法用于减少传输的数据量。基于支持向量的空间融合用于控制系统的最终决策变量。对于诸如太阳辐射等快速变化的参数，本文提出了基于自适应模型选择的双预测方案（DPS），以减少传输的数据量，具有基于GIA（灰色入射分析）的空间融合算法为聚合器。数据融合方案是在同济大学嘉定校园的温室中实施和测试。实施结果表明，数据融合模式保证了传感器精度，并显着减少传输的数据量，这意味着延长网络的寿命。

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  • 来源
    《Acta Horticulturae》 |2017年第1期|共8页
  • 作者

    R. Wei; L. Xu; X. Wang;

  • 作者单位

    College of Electronics and Information Engineering Tongji University Shanghai China;

    College of Electronics and Information Engineering Tongji University Shanghai China;

    College of Electronics and Information Engineering Tongji University Shanghai China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 园艺;
  • 关键词

    time fusion; dual prediction scheme; grey incidence analysis;

    机译：时间融合;双预测方案;灰病发生率分析;

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