Performances of G3 PLC-RF hybrid communication systems

Sanz, A.; Sancho, David; Ibar, José Carlos

doi:10.1109/isplc52837.2021.9628562

Cited by 7 publications

(1 citation statement)

References 5 publications

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“…Recently researchers have considered hybrid PLC-RF AMI networks to take advantage of both technologies (Sanz et al, 2021a). studied the performance of such systems and showed improvement in the AMI network coverage.…”

Section: Challenges and Research Opportunities For G3-plcmentioning

confidence: 99%

An overview of DLMS/COSEM and g3-plc for smart metering applications

Ngcobo

Ghayoor

2022

International Journal on Smart Sensing and Intelligent Systems

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A crucial part of the smart grid communication network is the Advanced Metering Infrastructure (AMI), connecting a utility company to end-users to support telemetry and remote-control applications. Although different technologies and standards for smart metering systems exist, Power-Line Communication is the preferred technology for AMI networks in dense urban areas, and the G3-PLC, which uses DLMS/COSEM to support data exchange, is the most used standard in PLC-based AMI networks. This paper provides a holistic view of G3-PLC in smart metering systems, including its interaction with the DLMS/COSEM. It also covers the challenges and research opportunities in G3-PLC smart metering systems.

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Section: Challenges and Research Opportunities For G3-plcmentioning

confidence: 99%

An overview of DLMS/COSEM and g3-plc for smart metering applications

Ngcobo

Ghayoor

2022

International Journal on Smart Sensing and Intelligent Systems

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A comparative study of predicting the availability of power line communication nodes using machine learning

Moussa

Amin

Darweesh

et al. 2023

Sci Rep

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Power Line Communication technology uses power cables to transmit data. Knowing whether a node is working in advance without testing saves time and resources, leading to the proposed model. The model has been trained on three dominant features, which are SNR (Signal to Noise Ratio), RSSI (Received Signal Strength Indicator), and CINR (Carrier to Interference plus Noise Ratio). The dataset consisted of 1000 readings, with 90% in the training set and 10% in the testing set. In addition, 50% of the dataset is for class 1, which indicates whether the node readings are optimum. The model is trained with multi-layer perception, K-Nearest Neighbors, Support Vector Machine with linear and non-linear kernels, Random Forest, and adaptive boosting (ADA) algorithms to compare between statistical, vector-based, regression, decision, and predictive algorithms. ADA boost has achieved the best accuracy, F-score, precision, and recall, which are 87%, 0.86613, 0.9, 0.8646, respectively.

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