An Edge-Fog-Cloud computing architecture for IoT and smart metering data

Oprea, Simona-Vasilica; Bârã, Adela

doi:10.1007/s12083-022-01436-y

Cited by 13 publications

(10 citation statements)

References 54 publications

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“…Short- and very short-term EC prediction refers to between one minute and one week ahead, and important for economic dispatch, load adjustment & control, and demand & pricing in real time. Although each forecasting horizon has certain applications such as, short-term electricity load forecasting has particularly attracted researchers due to its wide range of applications such as electricity planning, economic dispatch, adjustment, and control of electricity loads in real time over IoT enable edge [16] . Incorrect predictions of short-term electricity load cause huge losses of energy [17] .…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda

Khan

Hussain

Ullah

et al. 2023

Energy and Buildings

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Section: Literature Reviewmentioning

confidence: 99%

Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda

Khan

Hussain

Ullah

et al. 2023

Energy and Buildings

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“…The authors in [39] proposed an IoT and SM data processing framework based on Edge-Fog-Cloud computing environment in order to extract meaningful patterns from metering data to monitor and control the SMs, IoT appliances and develop applications for consumers, prosumers, aggregators, retailors, and grid operators. The computational and processing applications are distributed among the Edge, Fog, and Cloud layers such that the communication latency, response time, resource utilization and load is minimized.…”

Section: Related Workmentioning

confidence: 99%

“…In the aforementioned discussion, various network models such as clustering ( [18], [19], [21], [23], [26], [30], [31], [37]), star ( [22], [25], [36], [38]), tree-based ([27], [28], [34]), and hierarchical ( [24], [29], [32], [39]) models were proposed for AMI network design problem using different data aggregation and data collection techniques as needed for different AMI applications data. In particular, cluster-based network topology uses CH (here, SM and/or DCs) for data collection, processing, data aggregation and relaying purposes in the AMI network.…”

Section: Related Workmentioning

confidence: 99%

A QoS-Aware Data Aggregation Strategy for Resource Constrained IoT-Enabled AMI Network in Smart Grid

Khan,

Shirazi,

Adeel

et al. 2023

IEEE Access

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Emerging Internet of Things (IoT) technologies and applications have enabled the Smart Grid Utility control center to connect, monitor, control and exchange data between the smart appliances, smart meters (SMs), data concentrators (DCs) and control center server (CCS) over the Internet. In particular, DC receives different Advanced Metering Infrastructure (AMI) applications data from multiple SMs for processing, queuing, aggregation, and forwarding onward towards the CCS over the things networking. However, DCs are expensive component of the AMI network. Recently, SMs are used as relay-devices to accomplish a cost-effective AMI network infrastructure and avoid the DC placement and bottleneck problem. However, SMs are recourse constrained (limited CPU, RAM, storage, and network capacity) intelligent devices which faces numerous communication challenges during outage conditions and summer peak hours where bulk amount of data with different traffic rates and latency are exchanged with the Utility control center. Therefore, an efficient data aggregation at relay-devices is required to deal with high volume of data exchange rates in order to optimize the constrained-resources of the AMI network. In this article, we propose a hybrid data aggregation strategy implemented on an aggregator-head (AH) in the clustering topology which performs data aggregation on the Interval Meter Reading (IMR) application data. AH induction greatly reduce the workload of the cluster-heads (CHs), and efficiently utilizes the constrained-resource of AMI devices in a cost effective-manner. The proposed strategy is evaluated for different existing approaches using the CloudSim simulation tool. Experimental and simulation results are obtained and compared which show the effectiveness of the proposed strategy such that limited resources are optimized, CH workload is minimized, and QoS of AMI applications are maintained.

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“…The self-sustainable ratio increased by 12%, or 13%, thanks to the discovered module. Furthermore, the implementation of IoT in energy communities has been further extended by the authors in [12,13].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Domenteanu,

Cibu,

Delcea

2024

Sustainability

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Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.

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An Edge-Fog-Cloud computing architecture for IoT and smart metering data

Cited by 13 publications

References 54 publications

Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda

Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda

A QoS-Aware Data Aggregation Strategy for Resource Constrained IoT-Enabled AMI Network in Smart Grid

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

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