Design and Evaluation of Wireless Power Monitoring IoT System for AC Appliances

Tsai, Huan‐Liang; Truong, Le Phuong; Hsieh, Wei-Hung

doi:10.3390/en16010163

Cited by 2 publications

(1 citation statement)

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“…Therefore, in this study, only two NNs (NN1, NN2) utilized, to reduce network resource usage and increasing the prediction accuracy of incomplete data for each attribute in the dataset. The imputation performance for the three methods is assessed by using two statistical methods using R 2 -score [36] and root mean square (RMSE) [37]. The R 2 is the coefficient of determination value that calculated by utilizing equation 5.…”

Section: Performance Evaluation Phasementioning

confidence: 99%

A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

Al-Jamali,

Al-Saedi,

Zarzoor

et al. 2023

IEEE Access

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Over the past decade, the Internet of Thing (IoT) devices have been deployed in wide-scale several applications to collect vast amount of data from different locations in a time-series manner. However, collected data may be missing or damaged due to several issues such as unreliable communications, faulty sensors and synchronization problem that decrees application accuracy. Therefore, a several imputation-based machine learning approaches have been suggested to handle this problem in IoT application. In this study, a new approach is proposed called impute missing data (IMD) based on multi-Spike Neural Network learning method called IMD-SNN, to increase the reliability of missing value imputation in IoT. The method consists of three phases: Inserting missing data, to evaluate the missing values based on the cumulative distribution function (CDF), the multi SNN phase to estimate missing data according to the timestamp and a performance evaluation phase to evaluate an imputation accuracy via made a comparison with two models: imputation based KNN (I-KNN) and Imputation based (I-MLP) model based on resource usage and imputation accuracy assessment metrics. The implementation results have been shown that IMD-SNN utilizes less energy usage in comparison with (I-MLP) model and I-KNN model and gives highest imputation accuracy in contrast with (I-MLP) model and I-KNN model. Also, the IMD-SNN model utilizes less memory usage and needs execution time less than I-MLP model. INDEX TERMS Internet of Thing (IoT), Spike Neural Network (SNN), Multilayer Perceptron (MLP), Root Mean Square Error (RMSE)

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Section: Performance Evaluation Phasementioning

confidence: 99%