Temperature Monitoring for Electrical Substations Using Infrared Thermography: Architecture for Industrial Internet of Things

Usamentiaga, Rubén; Fernández, Miguel Ángel García; Villán, Alberto Fernández; Carús, Juan Luís

doi:10.1109/tii.2018.2868452

Cited by 42 publications

(28 citation statements)

References 26 publications

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“…Docker is a well‐known container that is widely used in recent years. This technology is used in Usamentiaga et al 84 and Luo et al 109 However, it should be noted that containers have security vulnerabilities such as container manipulation, misuse of virtualized resources, information sniffing, and privacy leakage 129 . Some of security vulnerabilities can be solved through cluster management and container orchestration tools 124 such as Kubernetes.…”

Section: Classification Of Challengesmentioning

confidence: 99%

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Internet of Things architecture challenges: A systematic review

Nikoui

Rahmani

Balador

et al. 2020

Int J Communication

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The Internet of Things (IoT) is a rapidly growing trend within many domains, such as automotive, avionics, automation, energy, and health. IoT architecture is the system of numerous elements, including sensors, protocols, actuators, cloud services, and layers. IoT architecture plays an important role to provide desired services. Nowadays, tens of IoT architectures are provided by the research community. Many challenges have been identified by the research community, including interoperability, security and privacy, reliability, energy constraints, scalability, and lack of common standards. However, to provide suitable IoT architecture, the importance and priority of requirements in different scenarios may vary, and requirement analysis should be regarded. To this end, this paper presents a systematic mapping survey to give a review of IoT architecture and provide a structured overview of research trends. Moreover, a technical taxonomy is presented for these challenges according to reviewed studies. This classification model can be used as a guideline for future works.

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Section: Classification Of Challengesmentioning

confidence: 99%

“…SOA can be used to handle heterogeneity provide higher interoperability through encapsulation and abstraction of resources. In Usamentiaga et al, 84 REST and HTTP protocol are used at network layer to expose data and functionality for service layer in temperature monitoring system.…”

Section: Classification Of Challengesmentioning

confidence: 99%

Internet of Things architecture challenges: A systematic review

Nikoui

Rahmani

Balador

et al. 2020

Int J Communication

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“…Plus, the great paradigm shift from PLCs to ePLCs is resulting in some great advances in multiprocessing [32,33], customized compilation tools [29], support for market-friendly embedded hardware (e.g., Raspberry-Pi), and more. Some of this market-friendly embedded hardware can be used with IoT platforms [34,35].…”

Section: Edgementioning

confidence: 99%

Edge Computing in an IoT Base Station System: Reprogramming and Real‐Time Tasks

Sun

et al. 2019

Complexity

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There are millions of base stations distributed across China, each containing many support devices and monitoring sensors. Conventional base station management systems tend to be hosted in the cloud, but cloud-based systems are difficult to reprogram and performing tasks in real-time is sometimes problematic, for example, sounding a combination of alarms or executing linked tasks. To overcome these drawbacks, we propose a hybrid edge-cloud IoT base station system, called BSIS. This paper includes a theoretical mathematical model that demonstrates the dynamic characteristics of BSIS along with a formulation for implementing BSIS in practice. Embedded programmable logic controllers serve as the edge nodes; a dynamic programming method creates a seamless integration between the edge nodes and the cloud. The paper concludes with a series of comprehensive analyses on scalability, responsiveness, and reliability. These analyses indicate a possible 60% reduction in the number of alarms, an edge response time of less than 0.1s, and an average downtime ratio of 0.66%.

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“…Due to the marketization of the power system, its structure is complicated and the equipment is numerous [1], the intelligent identification of electrical equipment becomes extremely urgent. In the identification of electrical equipment, there are Optical Character Recognition (OCR) identification equipment nameplate [2], image recognition equipment [3] and other methods. The mainstream is to use target detection to identify.…”

Section: Introductionmentioning

confidence: 99%

Intelligent power equipment identification model based on grid topology analysis

Shi¹,

Yang

Zhang

et al. 2021

E3S Web Conf.

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With the continuous development of the power grid, power equipment becomes more complex and diverse, which has increased the workload of power maintenance personnel. This paper proposes a method of intelligent identification of distribution network equipment to reduce the power maintenance personnel's workload. The model needs device photos, GPS coordinates, and device topology information of the entire power grid to infer the possible situation of the current device. The model is mainly divided into two parts: target recognition and equipment prediction. In target recognition, we propose a Self-attention target detection network (SA-TDN) that combines Faster-RCNN and Attention mechanism. In equipment prediction part, we use KD-Tree to analyse the grid topology to predict the real identification of the device. We compared this model with other convolutional neural networks (CNN) classification models. The results show that our model is ahead of current models in prediction accuracy.

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Temperature Monitoring for Electrical Substations Using Infrared Thermography: Architecture for Industrial Internet of Things

Cited by 42 publications

References 26 publications

Internet of Things architecture challenges: A systematic review

Internet of Things architecture challenges: A systematic review

Edge Computing in an IoT Base Station System: Reprogramming and Real‐Time Tasks

Intelligent power equipment identification model based on grid topology analysis

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