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Intelligent Wireless Sensor Network for Gas Classification Using Machine Learning
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DOI：10.1109/JSYST.2023.3238357.png)
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Systems based on wireless sensor networks convey a prevailing tool to monitor data in complex environments. In this work, we present an intelligent integrated system that monitors various crucial environmental parameters, and applies machine learning to classify multiple gases. The system is a complete, sophisticated, interactive multiple parameters-observing, with two-way communication via wireless mobile network. It consists of a number of buoys distributed throughout the sea and integrated with a suite of sensors capable of measuring a range of essential air and underwater parameters. Four well-known machine learning algorithms are implemented and compared, namely multilayer perceptron, Naive Bayes, logistic regression, and support vector machines. We present a thorough investigation and deep analysis to evaluate the system performance. The experimental results show that the system achieves a correct classification rate of 92.66%, with a weighted average precision, recall, F-score, and MMC of 0.93, 0.93, 0.926, and 0.91, respectively.
Keyword:
Pollution measurement
Monitoring
Temperature measurement
Wireless sensor networks
Atmospheric measurements
Wireless communication
Gases
Environmental monitoring
gas classification
machine learning
sensors
wireless communication network
期刊
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IF:
2.4
论文数:
4.5K
被引数:
387
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