Edge Computing Application, Architecture, and Challenges in Ubiquitous Power Internet of Things

Liu, Dongqi; Liang, Haolan; Zeng, Xiangjun; Zhang, Qiong; Zhang, Zidong; Li, Minhong

doi:10.3389/fenrg.2022.850252

Cited by 22 publications

(8 citation statements)

References 62 publications

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“…In recent years, more and more meters are being deployed in distribution systems with the rapid development of Internet of Things (IoT) technology (Deng et al, 2021). Since computing and storage resources of concurrent IoT terminals escalate notably, it is technically feasible to implement some of the complex functions in IoT terminals with edge computing (Li et al, 2020;Wang et al, 2020;Liu et al, 2022).…”

Section: Edge Computing Based Electricity Theft Detection In Distribu...mentioning

confidence: 99%

Edge Computing Based Electricity-Theft Detection of Low-Voltage Users

Zheng

Chen²,

Yang³

et al. 2022

Front. Energy Res.

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Electricity theft of low voltage (LV) users could result not only in the escalation of power loss but also in dangerous electric shock. Since LV users are served by distribution transformers, electricity theft of an LV user will cause line loss escalation of the associated distribution serving zone (DTSZ). Therefore, it seems promising to identify anomaly users of electricity theft with a Granger causality test to find out the user causing an escalation of line loss in DTSZ with time series of users’ usage and line loss. However, meters of LV users in severe environments occasionally suffer from communication failure to upload metering data to the head end of advanced metering infrastructure (AMI), which could distort the daily electricity usage of the associate user. Consequently, it could cause false alarms unavoidably once we detect electricity theft with these distorted data. Since the distribution transformer unit (DTU) collects metering data of LV users within associate DTSZ without distortion, an edge computing–based electricity theft detection approach is proposed in this article. The correlation between line loss of a DTSZ and electricity usage of anomaly users of electricity theft is first analyzed. Thereafter, the Granger causality test is used to identify anomaly users with authentic usage data with edge computing in DTU. Finally, the abnormal data and the data repaired by different missing data filling algorithms are used on the main station to detect electricity theft. Numerical simulation suggests that although missing data completion could recover information in missing data partially, it could result in notable false positive alarms in electricity theft, while the proposed method based on edge computing can completely eliminate the data distortion caused by communication failure.

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Section: Edge Computing Based Electricity Theft Detection In Distribu...mentioning

confidence: 99%

Edge Computing Based Electricity-Theft Detection of Low-Voltage Users

Zheng

Chen²,

Yang³

et al. 2022

Front. Energy Res.

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“…Edge computing refers to the provision of computing processing services on the side close to the source of objects or data [ 1 , 2 ]. Thanks to the low power consumption, small size, and high performance of edge devices, edge computing is being widely used in various fields, such as signal denoising [ 3 ], frequency domain analysis [ 4 ], industrial field condition monitoring [ 5 , 6 ], fault diagnosis [ 7 , 8 ], health management [ 9 , 10 ], feature extraction [ 11 ], signal solving [ 12 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Resource-Saving Customizable Pipeline Network Architecture for Multi-Signal Processing in Edge Devices

Song

Qie

Hao

et al. 2022

Sensors

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With the development of the information age, the importance of edge computing has been highlighted in industrial site monitoring, health management, and fault diagnosis. Among them, the processing and computing of signals in edge scenarios is the cornerstone of realizing these scenarios. While the performance of edge devices has been dramatically improved, the demand for signal processing in the edge side has also ushered in explosive growth. However, the deployment of traditional serial or parallel signal processing architectures on edge devices has problems such as poor flexibility, low efficiency, and low resource utilization, making edge devices unable to exert their maximum performance. Therefore, this paper proposes a resource-saving customizable pipeline network architecture with a space-optimized resource allocation method and a coordinate addressing method for irregular topology. This architecture significantly improves the flexibility of multi-signal processing in edge devices, improves resource utilization, and further increases the performance potential of edge devices. Finally, we designed a comparative experiment to prove that the resource-saving and customizable pipeline network architecture can significantly reduce resource consumption under the premise of meeting real-time processing requirements.

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“…The data gathered on the device side must be sent to the cloud if the model training is only carried out in the cloud. Uploading the data to the cloud computing facility will enhance the danger of privacy leaks because the data generated on the device often contains the user’s private information [ 19 , 20 ]. Additionally, there is a possibility of high-latency transmission during data transmission from the device to the cloud computing facility [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration

Tang

Chen

2022

Entropy

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Recent deep-learning methods for fault diagnosis of rolling bearings need a significant amount of computing time and resources. Most of them cannot meet the requirements of real-time fault diagnosis of rolling bearings under the cloud computing framework. This paper proposes a quick cloud–edge collaborative bearing fault diagnostic method based on the tradeoff between the advantages and disadvantages of cloud and edge computing. First, a collaborative cloud-based framework and an improved DSCNN–GAP algorithm are suggested to build a general model using the public bearing fault dataset. Second, the general model is distributed to each edge node, and a limited number of unique fault samples acquired by each edge node are used to quickly adjust the parameters of the model before running diagnostic tests. Finally, a fusion result is made from the diagnostic results of each edge node by DS evidence theory. Experiment results show that the proposed method not only improves diagnostic accuracy by DSCNN–GAP and fusion of multi-sensors, but also decreases diagnosis time by migration learning with the cloud–edge collaborative framework. Additionally, the method can effectively enhance data security and privacy protection.

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Edge Computing Application, Architecture, and Challenges in Ubiquitous Power Internet of Things

Cited by 22 publications

References 62 publications

Edge Computing Based Electricity-Theft Detection of Low-Voltage Users

Edge Computing Based Electricity-Theft Detection of Low-Voltage Users

Resource-Saving Customizable Pipeline Network Architecture for Multi-Signal Processing in Edge Devices

Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration

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