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Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system

机译:基于IOT-BIG数据分析在实时系统中的智能环境监测和分析的分类扩展

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摘要

Monitoring water conditions in real-time is a critical mission to preserve the water ecosystem in maritime and archipelagic countries, such as Indonesia that is relying on the wealth of water resources. To integrate the water monitoring system into the big data technology for real-time analysis, we have engaged in the ongoing project named smart environment monitoring and analytic in real-time system (SEMAR), which provides the IoT-big data platform for water monitoring. However, SEMAR does not have an analytical system yet. This paper proposes the analytical system for water quality classification using Pollution Index method, which is an extension of SEMAR. Besides, the communication protocol is updated from REST to MQTT. Furthermore, the real-time user interface is implemented for visualisation. The evaluations confirmed that the data analytic function adopting the linear SVM and decision tree algorithms achieves more than 90% for the estimation accuracy with 0.019075 for the MSE.
机译:实时监测水条件是保留海上和群体国家的水生态系统的关键任务,例如印度尼西亚,依靠水资源的财富。 要将水监测系统集成到实时分析的大数据技术中,我们从事正在进行的项目名为SMART环境监测和实际系统(SEMAR)的分析,这为水监测提供了IOT-BID数据平台 。 但是,Semar还没有分析系统。 本文提出了利用污染指数法进行了水质分类的分析系统,是各种延伸。 此外,通信协议从静止到MQTT更新。 此外,实现了实时用户界面以进行可视化。 评估证实,采用线性SVM和决策树算法的数据分析函数实现了MSE 0.019075的估计精度超过90%。

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