Artificial-Intelligence-Based Performance Enhancement of the G3-PLC LOADng Routing Protocol for Sensor Networks

Marcuzzi, Francesco; Tonello, Andrea M.

doi:10.1109/isplc.2019.8693390

Cited by 11 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The load density has been shown to strongly influence the signal attenuation due to high interference because of presence of reflecting elements [9]. A fundamental requirement This intuitive and heuristic approach to the generation of a network allows to reduce considerably the variance of the final result.…”

Section: B Density Of Loadsmentioning

confidence: 99%

Topology-Based Machine Learning: Predicting Power Line Communication Quality in Smart Grids

Marcuzzi

Tonello

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Smart Grids (SG) envision the exchange of both power and data, enabling system and customers to generate and transfer energy in a more efficient and balanced way. Among the relevant communication technologies, we find Power-Line Communications (PLC), which allow for data transmission on the electrical cables used for power delivery. Despite the hostile medium, PLC offer reliability and data rates to support exchange of control traffic, smart metering and polling applications. The distribution portion of the power delivery network, which we focus on in this work, is the most topologically complex, which makes channel prediction complicated. We show how random realistic topologies can be generated and then used to train a Machine Learning (ML) algorithm to infer PLC link quality (based on channel response) based solely on topology descriptors. We eventually show how precisely the communication quality can be inferred from the SG topology through ML. In doing so, we also discuss how the ML approach offers the common ground between top-down and bottom-up approaches for network characterization and how it enables smart decision making in the SG.

show abstract

Section: B Density Of Loadsmentioning

confidence: 99%

Topology-Based Machine Learning: Predicting Power Line Communication Quality in Smart Grids

Marcuzzi

Tonello

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Bumiller (2009), Bumiller et al (2010), Ngcobo and Ghayoor (2019), the effects of unforeseen changes of topology on the reliability of PLC networks have been studied, and the concept of single-frequency network transmission is presented for flooding of messages. The recent shift in improving routing in the G3-PLC is towards employing artificial intelligence methods (Cui et al, 2018;Marcuzzi and Tonello, 2019).…”

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

View full text Add to dashboard Cite

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.

show abstract

“…• artificial intelligence (Khadse et al, 2019;Marcuzzi & Tonello, 2019;Wei et al, 2019;Panchamathan et al, 2019;Bui & Jung, 2019;Hu & Jiang, 2019);…”

Section: Smart Technologymentioning

confidence: 99%

Smart city concept in the light of the literature review

Winkowska

Szpilko

Pejić

2019

Engineering Management in Production and Services

146

View full text Add to dashboard Cite

A B S T R A C T Nowadays, the transformations of metropolises into smart cities is a crucial factor in improving the living conditions of the inhabitants. The goal of the smart city concept is modern urban management using technical tools that offer state-of-the-art technologies, considering the applicable ecological standards while saving resources and achieving the expected results. The purpose of this article is to identify the areas of research analysed in the international literature in the field of smart cities. The bibliometric analysis was carried out to achieve the purpose. The analysis covered publications on smart cities published in Scopus and Web of Science databases from January 2009 to May 2019. Based on the bibliometric analysis, a bibliometric map was developed using the mapping technique VOS -the visualisation of similarities. Original clusters were created using the VOSviewer software. The bibliometric map visualises the results of the analysis that targeted the word coexistence. K E Y W O R D S smart city, smart city subareas, bibliometric map

show abstract

Artificial-Intelligence-Based Performance Enhancement of the G3-PLC LOADng Routing Protocol for Sensor Networks

Cited by 11 publications

References 12 publications

Topology-Based Machine Learning: Predicting Power Line Communication Quality in Smart Grids

Topology-Based Machine Learning: Predicting Power Line Communication Quality in Smart Grids

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

Smart city concept in the light of the literature review

Contact Info

Product

Resources

About