A configuration method of computing resources for microservice-based edge computing apparatus in smart distribution transformer area

Cen, Bowei; Hu, Chunchao; Cai, Zexiang; Wu, Zhigang; Zhang, Yanxu; Liu, Jianing; Su, Zhuo

doi:10.1016/j.ijepes.2021.107935

Cited by 21 publications

(15 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study entails the development of an integrated information system that leverages contemporary software architecture, namely microservices architecture, which has been implemented in various types of systems, including land administration systems, 40 greenhouse systems, 41 aquaculture value chain traceability systems, 42 energy management systems, [43][44][45][46][47] smart distribution transformer area, 48 control system, 49 groundwater management system, 50 smart water usage, 51 F I G U R E A5 IoT application UI screenshots.…”

Section: Discussionmentioning

confidence: 99%

“…This study entails the development of an integrated information system that leverages contemporary software architecture, namely microservices architecture, which has been implemented in various types of systems, including land administration systems, 40 greenhouse systems, 41 aquaculture value chain traceability systems, 42 energy management systems, 43–47 smart distribution transformer area, 48 control system, 49 groundwater management system, 50 smart water usage, 51 by utilizing the semantic web and internet of things (IoT) technologies. The designed Hazelnut Classification System (HDS) addresses the categorization of 18 Turkish hazelnut cultivars, offering comprehensive information tailored to diverse stakeholder needs.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Microservices‐based databank for Turkish hazelnut cultivars using IoT and semantic web technologies

Aydin,

Aldara

2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryInformation and communication technologies (ICTs) can play a crucial role in facilitating access to comprehensive information on the quality standards of Turkish hazelnut cultivars. In this regard, this study introduces a Hazelnut Databank System (HDS) that utilizes the microservices architecture, an integrated software system supported by the Internet of Things (IoT) and semantic web, to categorize Turkish hazelnut cultivars. The study focuses on developing microservices using various programming languages and frameworks. Specifically, C# on the .NET Core Framework was used for both microservices and the web‐based application implemented through the ASP.NET Core MVC Framework. Mobile‐based software applications were created using Xamarin. Forms, and the IoT application was developed using the Python programming language. The data storage is facilitated through the MS SQL Server database. Additionally, the study incorporates the implementation of a hazelnut species classification system using the DNN + ResNet50 machine learning model, achieving an impressive accuracy rate of 95.77%. The overall usability of the system was evaluated, resulting in a score of 42 out of 50. By providing detailed information on Turkish hazelnut cultivars, the HDS has the potential to greatly improve hazelnut production quality in Turkey and increase awareness of hazelnut agriculture among relevant stakeholders.

show abstract

Section: Discussionmentioning

confidence: 99%

“…This study entails the development of an integrated information system that leverages contemporary software architecture, namely microservices architecture, which has been implemented in various types of systems, including land administration systems, 40 greenhouse systems, 41 aquaculture value chain traceability systems, 42 energy management systems, 43–47 smart distribution transformer area, 48 control system, 49 groundwater management system, 50 smart water usage, 51 by utilizing the semantic web and internet of things (IoT) technologies. The designed Hazelnut Classification System (HDS) addresses the categorization of 18 Turkish hazelnut cultivars, offering comprehensive information tailored to diverse stakeholder needs.…”

Section: Discussionmentioning

confidence: 99%

Microservices‐based databank for Turkish hazelnut cultivars using IoT and semantic web technologies

Aydin,

Aldara

2024

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…In recent years, the application of EI in smart power systems has been emerging rapidly worldwide. Cen et al (2022) demonstrate the applicability of microservices and edge computing apparatuses in the distribution grid computational resource configuration. An integrated cloud-edge architecture, combined with reinforcement learning to facilitate costeffective smart buildings, is proposed by Zhang et al (2021).…”

Section: Introductionmentioning

confidence: 93%

Edge intelligence enabled optimal scheduling with distributed price-responsive load for regenerative electric boilers

Fan

Wang²,

Qi³

et al. 2022

Front. Energy Res.

View full text Add to dashboard Cite

Heat supply accounts for a substantial amount of terminal energy usage. However, along with price rises in primary energy, there is an urgent need to reduce the average cost of energy consumption during the purchasing of thermal services. Electric heating, an electricity-fed heating production and delivery technology, has been suggested as a promising method for improving heating efficiency, due to the ease of scheduling. However, the traditional centralized operating methods of electricity purchasing rely on explicit physical modeling of every detail, and accurate future predictions, the implementation of which are rarely practical in reality. To facilitate model-free decisions in the field of electricity purchasing, heat storage, and supply management, aimed at cost saving in a real-time price environment, this study proposes a scheduling framework based on deep reinforcement learning (DRL) and the existence of responsive users. First, the structure of a distributed heating system fed by regenerative electric boilers (REBs), which facilitate shiftable heat-load control, is introduced. A terminal heat demand response model based on thermal sensation vote (TSV), characterizing the consumption flexibility of responsive users, is also proposed. Second, due to thermal system inertia, the sequential decision problem of electric heating load scheduling is transformed into a specific Markov decision process (MDP). Finally, the edge intelligence (EI) deployed on the demand side uses a twin delayed deterministic policy gradient (TD-3) algorithm to address the action space continuity of electric heating devices. The combination of a DRL strategy and the computing power of EI enables real-time optimal scheduling. Unlike the traditional method, the trained intelligent agent makes adaptive control strategies according to the currently observed state space, thus avoiding prediction uncertainty. The simulation results validate that the intelligent agent responds positively to changes in electricity prices and weather conditions, reducing electricity consumption costs while maintaining user comfort. The adaptability and generalization of the proposed approach to different conditions is also demonstrated.

show abstract

“…Cloud computing technology has attracted much attention with high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied [ 71 ]. Additionally, multisource heterogeneous urban sensor access and data management technologies provide strong support for intelligent perception and scientific management at the city scale and can accelerate the construction of smart cities or digital twin cities with virtual reality features [ 72 , 73 , 74 ]. The difficulty of the processes has frequently increased with the single computing resources insufficient because of the complicated operation of the combined prediction model and meet in real-time for intelligent power systems [ 7 , 39 , 75 ].…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

AL-Jumaili

Muniyandi

Hasan

et al. 2023

Sensors

View full text Add to dashboard Cite

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

show abstract

A configuration method of computing resources for microservice-based edge computing apparatus in smart distribution transformer area

Cited by 21 publications

References 23 publications

Microservices‐based databank for Turkish hazelnut cultivars using IoT and semantic web technologies

Microservices‐based databank for Turkish hazelnut cultivars using IoT and semantic web technologies

Edge intelligence enabled optimal scheduling with distributed price-responsive load for regenerative electric boilers

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

Contact Info

Product

Resources

About