Anomaly Detection Based on Edge Computing Framework for AMI

Liang, Haolan; Ye, Chunxiao; Zhou, Yuangao; Yang, Hongzhao

doi:10.1109/iceemt52412.2021.9601888

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…The paper evaluates the proposed model on three public datasets: UMN, Violent Flows, and Hockey Fights, and reports the accuracy, precision, recall, and F1-score metrics. Anomaly detection in mining scenarios is addressed through the endorsement of edge computing over traditional cloud methods, and an anomaly detection algorithm based on fuzzy theory is proposed for enhanced professionalism [38]. In [39], a framework is presented for implementing the ELM (ex-treme learning machines) model, a variation of autoencoders, validated using the NASA bearing dataset, with a specific emphasis on online anomaly detection in machinery.…”

Section: IVmentioning

confidence: 99%

Anomaly Detection in Crowd Surveillance using Edge Computing: A Comprehensive Survey

Juvekar,

Bhadgaonkar,

Ghadge

et al. 2024

IJRASET

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As urban environments continue to witness the proliferation of surveillance systems, ensuring public safety and security has become an increasingly challenging endeavor. Anomaly detection in crowded areas has emerged as a crucial task in these settings, aimed at identifying suspicious activities or potential threats. To meet the demands of real-time monitoring and analysis, edge computing has gained prominence as a critical technology. This survey paper provides a comprehensive overview of the state-of-the-art in anomaly detection, its use in crowd surviellance and the role played by edge computing in anomaly detection .The paper reviews an extensive body of literature encompassing various techniques and methodologies employed for anomaly detection in crowded scenes. It explores the evolution of traditional video-based approaches, such as motion analysis and object tracking, and the recent advancements leveraging deep learning, including various models in machine learning. These techniques are examined for their applicability in crowd surveillance scenarios.Various commonly used datasets to measure the quality of anomaly detection are also explored, along with their attributes and descriptions.The survey analyzes how edge computing solutions, such as edge AI accelerators and edge devices, enable faster and more context-aware processing of video data, while also addressing issues related to bandwidth constraints, privacy concerns, and scalability; paving way for a further research in harnessing the power of edge computing in crowd surveillance anomaly detection.

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Section: IVmentioning

confidence: 99%

Anomaly Detection in Crowd Surveillance using Edge Computing: A Comprehensive Survey

Juvekar,

Bhadgaonkar,

Ghadge

et al. 2024

IJRASET

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show abstract

“…To capture the feature mapping, the input data are multiplied by the convolutional kernel in the convolutional network, which is then activated by a nonlinear function. The convolution kernel randomly initializes weights and biases (Liang, Ye, Zhou, & Yang, 2021). After each CNN layer, a normalization layer and a max-pooling layer are added.…”

Section: System Modelmentioning

confidence: 99%

“…A typical neural network comprises numerous small, interconnected processes called neurons, each generating a string of activations with real values. Environmental sensors activate input neurons, and weighted connections from previously active neurons excite more neurons (Komyakov, Erbes, & Ivanchenko, 2015;Liang et al, 2021;Schmidhuber, 2015) (see Fig. 3).…”

Section: Deep Neural Network Structurementioning

confidence: 99%

Proposed algorithm for smart grid DDoS detection based on deep learning

Diaba

Elmusrati

2023

Neural Networks

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“…In the AMI, the smart meters are uploading their power usage information to the data concentrators through wired and wireless communication in the Neighborhood Area Network (NAN). And then the data center actively requests power data from data concentrators through the wide area network (WAN), or data concentrators pass through the WAN at a preset time interval, and they centrally upload power consumption data to the data center, then the data center distributes electricity price information to users and implements related measures such as load management, demand response, and meter control commands to improve customer service (Liang et al, 2021).…”

Section: Advanced Metering Infrastructurementioning

confidence: 99%

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

et al. 2022

Self Cite

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The Ubiquitous Power Internet of Things (UPIoT) is a concrete manifestation of the Internet of things (IoT) in the power industry, which is a deep integration of the interconnected power network and communication network, realizing full perception of the system status and full business penetration in all links of power production, transmission, and consumption. The introduction of edge computing in UPIoT fully meets the requirements of rapid response, real-time perception, and to some extent, privacy protection. However, there is currently no comprehensive investigation on the application of edge computing technology in UPIoT. First, this paper introduces the development background and construction of UPIoT and its technical architecture. Then the challenges faced by UPIoT in the process of construction are analyzed. Furthermore, the paper elaborates on the functions and features of edge computing, proposes that the support of edge computing technology can solve the challenges of efficient, fast, and secure processing of massive edge data faced by the traditional cloud-based centralized big data processing technology of UPIoT, and analyzes the architecture of the edge computing-assisted UPIoT. For the three typical scenarios of UPIoT, namely power monitoring system, smart energy system and power metering system, the edge computing architecture of the three scenarios are analyzed, and the specific application methods and roles played by edge computing in the three scenarios are also elaborated. Finally, we discuss the challenges of edge computing in UPIoT, in terms of policy challenges, market challenges, and technical challenges, as well as outline the outlooks of the technical challenges.

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Anomaly Detection Based on Edge Computing Framework for AMI

Cited by 3 publications

References 19 publications

Anomaly Detection in Crowd Surveillance using Edge Computing: A Comprehensive Survey

Anomaly Detection in Crowd Surveillance using Edge Computing: A Comprehensive Survey

Proposed algorithm for smart grid DDoS detection based on deep learning

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

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