A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations

Siryani, Joseph; Tanju, Bereket; Eveleigh, Timothy

doi:10.1109/jiot.2017.2722358

Cited by 100 publications

(43 citation statements)

References 47 publications

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“…In this work, a Bayesian network model is compared against three other classifiers including Naïve Bayes, Random Forest and Decision Trees. The proposed method in [92] is validated using the network coverage data collected from commercial settings.…”

Section: Iot Data Analytics In Energy Systemsmentioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

192

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The Internet of Things (IoT) applications has grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e. cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data is processed for machine learning analysis, and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications, and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

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Section: Iot Data Analytics In Energy Systemsmentioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

192

View full text Add to dashboard Cite

show abstract

“…The results show that, if suitable models are used, the precision will exceed 68 per cent. Siryani et al [10] used machine learning to progress the competence of smart meter operation. Administrative must guarantee the cost efficiency of their activities with the massive rise in the number of smart meters.…”

Section: IIImentioning

confidence: 99%

A Survey of Machine Learning for IoT Networks

Thamaraiselvi¹,

Mary²

2020

IJCSMC

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In practice applications like Security Systems, intelligent infrastructure, traffic management and weather systems (among others), internet of things (IoT) is becoming more and more popular. Recently, the Internet of Things (IoT) has become more common because the number of intelligent devices used in everyday human life with minimal network life. The transfer of routing information plays a major role in providing communication between nodes. The IoT produces big data in addition to its increased volume, with a variety of multiple modalities and differing data quality, distinguished by its time and position dependence. Intelligent data processing and analysis are the main elements for the creation of intelligent IoT applications. This paper focuses on IoT network security aspects by exploring the serviceability of machine learning algorithms to identify anomalies in data from these networks. Taxonomy of machine learning algorithms is introduced to illustrate how various methods are applied to the data in order to obtain higher level knowledge. There will also be discussions on the ability and problems of Machine Learning for IoT data processing.

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“…The framework shows the effectiveness of methodology with a total Bayesian Network forecast model and contrast and three machine learning expectation demonstrate classifiers: Naïve Bayes, Random Forest and Decision Tree. Results show that approach creates factually critical estimations and that the DSS will enhance the cost effectiveness of Electric Smart Meter (ESM) arrange tasks and support [16].…”

Section: Related Workmentioning

confidence: 99%

TPR, PPV and ROC based Performance Measurement and Optimization of Human Face Recognition of IoT Enabled Physical Location Monitoring

Shitole¹,

Devare²

2019

IJRTE

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 Abstract: This paper describes the construction of Internet of Things (IoT) enabled system which not only captures the sensors data in textual and numeric form but also performs live human face recognition to monitor physical location effectively. The dataset used in order to apply supervised machine learning algorithms is the combination of automatically captured live sensor data along with name of the human face recognized or unknown and additional manually introduced class label. Performance measurement of face recognition is done with the help of Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Logistic Regression (LR). The results show that DT gives the best performance with respect to classifier's accuracy; True Positive Rate, Positive Predictive Value and area under curve of Receiver Operating Characteristics (ROC) for face recognition prediction whether the recognized face is true or false.

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A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations

Cited by 100 publications

References 47 publications

Machine learning and data analytics for the IoT

Machine learning and data analytics for the IoT

A Survey of Machine Learning for IoT Networks

TPR, PPV and ROC based Performance Measurement and Optimization of Human Face Recognition of IoT Enabled Physical Location Monitoring

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