A multi-tier architecture for data analytics in smart metering systems

Olivares-Rojas, Juan C.; Reyes-Archundia, Enrique; Gnecchi, José Antonio Gutiérrez; González-Murueta, Johan W.; Cerda-Jacobo, Jaime

doi:10.1016/j.simpat.2019.102024

Cited by 18 publications

(10 citation statements)

References 13 publications

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“…Olivares‐Rojas et al 14 proposed a new cloud‐based AMI network architecture for adaptive data analysis in a multilayer context, taking into account machine learning‐based models. The analysis aimed at detecting and predicting energy consumption, energy quality, and energy theft.…”

Section: Related Workmentioning

confidence: 99%

“…(a) A random number

{r}_{\mathrm{CSP}1}

is selected for the CSP and, similarly to Reference 14, a hash operation calculates the group's temporary key:

{\mathrm{GTK}}_i={h}_1\left({\mathrm{GK}}_i\Big\Vert {r}_{\mathrm{CSP}1}\right).

The temporary key generated prevents attacks such as known key and replay, and guarantees perfect forward secrecy (PFS).…”

Section: Proposed Protocolmentioning

confidence: 99%

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Cloud‐based authentication and key management protocol for advanced metering infrastructure in smart grid

Roman

Gondim

2022

Trans Emerging Tel Tech

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In smart grid networks, the advanced metering infrastructure (AMI) enables sensors and communication networks to measure electricity consumed or generated by users. However, among the challenges to be overcome related to security in the AMI system is resistance to computer attacks, such as impersonation, denial of service, man in the middle, and redirection, which can compromise privacy, confidentiality, and availability of electrical systems. An efficient and effective authentication system is necessary to guarantee the privacy and confidentiality of data without compromising system performance, thus characterizing a challenge (need for a tradeoff between security and performance), which involves limited computing power and bandwidth and a high number of meters to be installed in a certain geographic area. This article introduces a group authentication and key agreement protocol that ensures both confidentiality and privacy of communications and avoids several computer attacks. It showed better communication and computational cost‐based performance compared to other protocols. Automated validation of internet security protocols and applications formally validated it, while security protocol animator tool simulated an intrusion, with good results.

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Section: Related Workmentioning

confidence: 99%

“…(a) A random number

{r}_{\mathrm{CSP}1}

is selected for the CSP and, similarly to Reference 14, a hash operation calculates the group's temporary key:

{\mathrm{GTK}}_i={h}_1\left({\mathrm{GK}}_i\Big\Vert {r}_{\mathrm{CSP}1}\right).

The temporary key generated prevents attacks such as known key and replay, and guarantees perfect forward secrecy (PFS).…”

Section: Proposed Protocolmentioning

confidence: 99%

Cloud‐based authentication and key management protocol for advanced metering infrastructure in smart grid

Roman

Gondim

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…With the increasing deployment of AMI, the amount of electric power data increases sharply such that the centralized architecture for AMI will not be sustainable. By contrast, the distributed architecture with an advanced computing technology such as edge and fog computing will provide more powerful performance (Olivares-Rojas et al, 2020). In the 5G and IoT era, the advanced distributed architecture, the ICT, and the security mechanism are the key to the intelligent functionality of AMI.…”

Section: Smart Meter-based Advanced Metering Infrastructurementioning

confidence: 99%

Smarter Grid in the 5G Era: A Framework Integrating Power Internet of Things With a Cyber Physical System

et al. 2021

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As the energy infrastructure of smart cities, smart grid upgrades traditional power grid systems with state-of-the-art information and communication technologies. In particular, as the full deployment of the Internet of Things in the power grid (a.k.a. power Internet of Things or PIoT), the newly introduced information flow together with inherent energy flow makes it more efficient for power generation, transmission, distribution, and consumption. To further exploit the precious energy and the latest 5G technologies, this article boosts to add a value flow in the smart grid, mainly including the value created by innovative services and market mechanisms and the value added by the information flow. Specifically, by integrating PIoT with cyber-physical systems, this article sketches a conceptual framework of the cyber-physical power system (CPPS). The CPPS carries out holistic perception and ubiquitous connection of distributed energy sources and electrical facilities and builds up a smarter power grid with global information interaction, intelligent decision-making, and real-time agile control. Finally, for illustration purposes, we conduct a case study regarding an intelligent home management system.

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“…However, the events of downlink failure at the algorithm state diffusion substage, or uplink failure in the algorithm state uploading substage would make the feedback from that client fails. Accordingly, we have set a training timer, defined as D T max 2 , in the training stage to avoid the federated server waits infinitely for unreachable clients, as illustrated in Framework 1.…”

Section: ) Delay Of a Roundmentioning

confidence: 99%

“…In such smart grid environment, multitude of data is gathered from massive IoT based electricity meters, distribution transformers, as well as other metering devices [1]. Most of data is from computation intensive applications, such as forecasting energy consumption, prediction of power quality, analytics of energy consumption trends and prediction of energy theft [2], which require big data analytics with low latency. Take predicting energy theft as an example, the faster and the higher accurate in detecting the energy theft, the smaller loss.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Federated Learning for GMEC With Time-Varying Wireless Link

et al. 2021

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Smart grid applications, such as predicting energy consumption, grid user behavior analysis and predicting energy theft, etc., are data-driven applications that require machine learning with a wealth of data generated from Internet of Things (IoT) based metering devices. However, traditional methods of uploading this huge data to the remote cloud for data analytics may be low efficient due to the non-negligible network transmission delay. By deploying a number of computing-enabled devices at the network edge, edge computing supports the implementation of machine learning close to the power grid environment. Considering the limited computing resources of edge devices and non-independent and identical (non-IID) data source, federated learning is a feasible edge computing based machine learning model. In federated learning, distributed mobile clients and a federated server collaborate to perform machine learning. Generally, the more clients to join the federated learning, the faster to obtain learning convergence and the higher resource utility. However, the communications between clients and the server in training rounds of federated learning may fail due to time-varying link reliability properties in a wireless network of smart grid, which not only slows down the model convergence rate but also wastes resources, such as energy consumption for invalid local training. This paper studies a dynamic federated learning problem in a power grid mobile edge computing (GMEC) environment, considering the high dynamic of link reliability. We design a delay deadline constrained federated learning framework to avoid extremely long training delay, and then formulate a dynamic client selection problem for computing utility maximization in such learning framework. Two online client selection algorithms, including climax greedy and uti-positive guarantee, are proposed to address the problem. The theoretical analysis and simulation results are conducted to illustrate the efficiency of the proposal. INDEX TERMS Mobile edge computing, machine learning, federated learning, smart grid, link reliability.

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A multi-tier architecture for data analytics in smart metering systems

Cited by 18 publications

References 13 publications

Cloud‐based authentication and key management protocol for advanced metering infrastructure in smart grid

Cloud‐based authentication and key management protocol for advanced metering infrastructure in smart grid

Smarter Grid in the 5G Era: A Framework Integrating Power Internet of Things With a Cyber Physical System

Dynamic Federated Learning for GMEC With Time-Varying Wireless Link

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