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Int. J. Distributed Syst. Technol.最新文献

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Physical Education Teaching Quality Evaluation Method Using Collaborative Edge Computing and Social Internet of Things 基于协同边缘计算和社会物联网的体育教学质量评价方法
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-12 DOI: 10.4018/ijdst.307951
Shao Hu
{"title":"Physical Education Teaching Quality Evaluation Method Using Collaborative Edge Computing and Social Internet of Things","authors":"Shao Hu","doi":"10.4018/ijdst.307951","DOIUrl":"https://doi.org/10.4018/ijdst.307951","url":null,"abstract":"A quality assessment technique for physical education and digital hybrid learning application of mobile edge devices is presented to improve physical education and evaluation hybrid instruction. As a result, the primary goal of this study is to showcase the rapid gains of social internet of things (SIoT) materials for applications and to provide various challenges and opportunities for future cases with collaborative edge computing (CEC). This work creates the quality assessment indicator system of mixed physical and digital evaluation based on aim, index, the value of the index, and assessment standard. Collaborative edge computing and social internet of things in the classroom for research into the use of SIoT and its implementations in underdeveloped nations is strongly encouraged, according to the findings of this study by increasing 28.1% enhancement. The fuzzy extensive analysis methodology is employed to successfully achieve the hybrid physical training quality assessment and digital evaluation method.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116627441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Designing Collaborative Edge Computing for Electricity Heterogeneous Data Based on Social IoT Systems 基于社会物联网系统的电力异构数据协同边缘计算设计
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-12 DOI: 10.4018/ijdst.307955
Yong Cheng, J. Du, Yonggang Yang, Zhibao Ma, Ning Li, Jianhui Zhao, Di Wu
{"title":"Designing Collaborative Edge Computing for Electricity Heterogeneous Data Based on Social IoT Systems","authors":"Yong Cheng, J. Du, Yonggang Yang, Zhibao Ma, Ning Li, Jianhui Zhao, Di Wu","doi":"10.4018/ijdst.307955","DOIUrl":"https://doi.org/10.4018/ijdst.307955","url":null,"abstract":"Power generation, transmission, maintenance costs, and electricity prices are heavily influenced by accurate load forecasts at energy suppliers' operation centers. Every aspect of our life has been transformed by the social internet of things (SIoT). Collaborative edge computing (CEC) has emerged as a new paradigm for meeting the demands of the internet of things by alleviating resource congestion (IoT). Remote devices can connect to CEC's processing, storage, and network resources. About short-term electrical load forecasting, this study explores the application of feed-forward deep neurological networking (FF-DNN) and recurrent deep neuronal networking (R-DNN) methods and analyzes their accuracy and computing performance. A dynamic prediction system using a deep neural network (DPS-DNN) is proposed in this research. The recently unveiled smartgrid with the results shows the higher performance of the proposed DPS-DNN model than the existing models with an enhancement of 93.15% based on collaborative edge networks based on SIoT.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122616692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Blockchain-Enabled Collaborative Edge Computing for Intelligent Education Systems Using Social IoT 使用社会物联网的智能教育系统的区块链支持的协作边缘计算
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-12 DOI: 10.4018/ijdst.307958
Xun Liu
{"title":"Blockchain-Enabled Collaborative Edge Computing for Intelligent Education Systems Using Social IoT","authors":"Xun Liu","doi":"10.4018/ijdst.307958","DOIUrl":"https://doi.org/10.4018/ijdst.307958","url":null,"abstract":"As new technologies such as the internet of things, big data analysis, artificial intelligence, and cloud computing are widely used, intelligent learning platforms and web-based educational platforms are gaining popularity. Social internet of things (SIoT) uses mobile edge computing and interpersonal interactions among SIoT users to take advantage of the benefits that collaborative edge computing (CEC) offers, even while posing new challenges. The communication efficiency and the security of intelligent education systems must be considerably developed to ensure real-time services. Therefore, this work deliberates an advanced structural framework for a blockchain-enabled 6G communication network (BC-6GCN) for the intelligent education system. Schools must analyze massive data volumes to provide intelligent education services, leaving the data open to manipulation by malicious hackers. The challenges discussed can lead to the potential advancement of protected, reliable, and smart SIoT frameworks.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125646206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Identifying Buying Patterns From Consumer Purchase History Using Big Data and Cloud Computing 利用大数据和云计算从消费者购买历史中识别购买模式
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-12 DOI: 10.4018/ijdst.307957
Dandan Ye, BalaAnand Muthu, P. Kumar
{"title":"Identifying Buying Patterns From Consumer Purchase History Using Big Data and Cloud Computing","authors":"Dandan Ye, BalaAnand Muthu, P. Kumar","doi":"10.4018/ijdst.307957","DOIUrl":"https://doi.org/10.4018/ijdst.307957","url":null,"abstract":"The consumer buying process refers to the procedures taken by a buyer when making a purchase. There are patterns that customers follow before they make purchases that can be described as consumer behavior. When making decisions, businesses and engineers turn to big data for the valuable insights it contains. Edge computing, although presenting processing issues, has aided in the evolution of big data by offering computational, networking, and storage capability. The process consists of identifying needs and wants, conducting research, evaluating options, and making a purchase, followed by evaluating the purchase. This is a considered major problem in the prediction history. To overcome these issues, here comes a framework of predicting customer purchasing using big data analytics (PCP-BDA) to determine the purpose of every customer becoming aware of the need or desire for a product and ends with the purchase transaction.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125004336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Design and Implementation of a Web Editing and Publishing System Based on a Semantic Network Generation Algorithm 基于语义网络生成算法的网络编辑发布系统的设计与实现
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-12 DOI: 10.4018/ijdst.308001
Jing Wang
{"title":"Design and Implementation of a Web Editing and Publishing System Based on a Semantic Network Generation Algorithm","authors":"Jing Wang","doi":"10.4018/ijdst.308001","DOIUrl":"https://doi.org/10.4018/ijdst.308001","url":null,"abstract":"In order to solve the problem of web editing data mining effectively, a semantic network generation algorithm is proposed. First of all, on the basis of preprocessing the variant short text, the maximum matching distance between short text is calculated by using the dictionary to expand the semantics of the Chinese words, which is used as an index to measure the formal distance between short text. Finally, a weighted method is used to synthesize formal distance and unit semantic distance into text distance, which is applied to the clustering analysis of online comments. The length of the word list is used to punish the distance. Results show that the most popular query topics on the Internet are shopping 10%, entertainment 10%, pornography 12%, computer 9%, research 9%, healthy life 5%, travel 5%, games 5%, family medical 5%, sports 3%, personal economic plan 3%, holiday 1% and others. It is proved that the improved algorithm proposed in this paper is superior to other methods and the clustering performance is significantly improved.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127820918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Innovative Knowledge Automation Framework in DM and Collaborative Edge Computing Social IoT Systems DM和协作边缘计算社会物联网系统中的创新知识自动化框架
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-12 DOI: 10.4018/ijdst.307953
Qiansha Zhang, Gang Li
{"title":"Innovative Knowledge Automation Framework in DM and Collaborative Edge Computing Social IoT Systems","authors":"Qiansha Zhang, Gang Li","doi":"10.4018/ijdst.307953","DOIUrl":"https://doi.org/10.4018/ijdst.307953","url":null,"abstract":"Digital marketing-based innovative knowledge management helps people inspire creativity and cultural changes required to advance the organization and satisfy changing business requirements. Knowledge workers can respond more rapidly when they have quicker access to resources and information across the company. A knowledge-based approach views innovation as a process characterized by the knowledge needed to understand how the innovation was created. The term “digital marketing automation” (DMA) refers to software platforms and technologies built for marketing departments and enterprises to sell online and automate tedious tasks more effectively. Digital marketing encompasses all forms of advertising that take place online, including but not limited to websites, search engines, social media, email, and mobile apps. An entirely new approach to big data processing has emerged because of the rise of edge computing in the internet of things environment. As a result of these findings, a distributed neural network cloud-edge computing paradigm is presented.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"79 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114015527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Intelligent Detection Method of Electric Energy Meter Overload Operating and Collaborative Edge Computing for Social Internet of Things Systems 面向社会物联网系统的电能表过载智能检测方法及协同边缘计算
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-12 DOI: 10.4018/ijdst.307954
Ruiming Yuan, Sida Zheng, Yan Liu, Fukuan Pang, Xiaokun Yang
{"title":"The Intelligent Detection Method of Electric Energy Meter Overload Operating and Collaborative Edge Computing for Social Internet of Things Systems","authors":"Ruiming Yuan, Sida Zheng, Yan Liu, Fukuan Pang, Xiaokun Yang","doi":"10.4018/ijdst.307954","DOIUrl":"https://doi.org/10.4018/ijdst.307954","url":null,"abstract":"This study describes a line cloud architecture and IDM-based energy metering system to replace existing meter reading methods. They can regularly monitor meter readings without sending someone to each residence, and the bill is automatically sent to each user via IDM. If the consumer fails to pay the bill, the service provider can cut off the supply; this technology will prevent the illicit use of electricity, often known as power detection, and locate line faults rapidly and precisely without the need for human intervention. Because of the increased deployment of energy meters, much data on electric energy is used. Developing cloud architecture technologies could utilize this data better to prevent power detection. In this paper, an intelligent detection method of electric energy meter overload functioning state based on cloud architecture (IDM EEMOF-CA) is provided in detail and utilized to identify electric energy detection. There are currently no studies involving the use of IDM EEM-CA to detect power exposure to the authors' knowledge.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122527605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Distributed System-Based Multiplex Networks to Extract Texture Feature 基于分布式系统的多路网络纹理特征提取
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-01 DOI: 10.4018/ijdst.307991
Yang Liu, Wei-qi Yuan
{"title":"A Distributed System-Based Multiplex Networks to Extract Texture Feature","authors":"Yang Liu, Wei-qi Yuan","doi":"10.4018/ijdst.307991","DOIUrl":"https://doi.org/10.4018/ijdst.307991","url":null,"abstract":"Defect detection is an indispensable part of quality detection in manufacturing. It is a challenging task to recognize defects on the surface of castings with random textures. This paper proposes a texture extraction method based on multiplex networks for defect segmentation in a random background. The proposed method redefines the image information in the form of multiplex network topologies according to the different properties of casting surface texture. Finally, the proposed method segments different texture regions by extracting the similarity of texture primitives in the multiplex networks. The study conducted experiments in a distributed system environment, and the results show that the proposed method is effective in actual industrial data sets. As an interdisciplinary application of network science and machine vision, the proposed method provides a valuable application mode for the development of complex networks in new fields and provides a new research idea for the texture analysis of castings.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128602715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Face Detection-Induced Access Control System via Large Margin Metric Learning 基于大余量度量学习的人脸检测诱导门禁系统
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-01 DOI: 10.4018/ijdst.307987
Li'e Pu, Jialin Sun
{"title":"Face Detection-Induced Access Control System via Large Margin Metric Learning","authors":"Li'e Pu, Jialin Sun","doi":"10.4018/ijdst.307987","DOIUrl":"https://doi.org/10.4018/ijdst.307987","url":null,"abstract":"With the development of science and technology and the acceleration of economic integration, identity authentication has become the most basic element in cyberspace and the basis of the whole information security system. Biometric recognition technology is an important technology in the process of identity authentication. Among them, face recognition technology has been favored by researchers, social applications, and users in the field of identity authentication by virtue of its inherent advantages such as ease of use and insensitivity. In this paper, a face recognition-based access control system is established with the help of large margin metric learning. First, a face library is input into a deep neural network to extract representation features. Second, the deep representation features are used to learn a large margin metric learning model. Third, the face image is captured by a digital camera to input into large margin metric learning model for identifying the person. The experimental results show that the proposed system can accurately identify most of the persons.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114920710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing 利用人工智能和边缘计算优化企业管理
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-01 DOI: 10.4018/ijdst.307994
Shanshan Wang
{"title":"Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing","authors":"Shanshan Wang","doi":"10.4018/ijdst.307994","DOIUrl":"https://doi.org/10.4018/ijdst.307994","url":null,"abstract":"In the internet era, huge data is generated every day. With the help of cloud computing, enterprises can store and analyze these data more conveniently. With the emergence of the internet of things, more hardware devices have accessed the network and produced massive data. The data heavily relies on cloud computing for centralized data processing and analysis. However, the rapid growth of data volume has exceeded the network throughput capacity of cloud computing. By deploying computing nodes at the edge of the local network, edge computing allows devices to complete data collection and preprocessing in the local network. Thus, it can overcome the problems of low efficiency and large transmission delay of cloud computing for massive native data. This paper designs a human trajectory training system for enterprise management. The simulation demonstrates that the system can support human trajectory tracing and prediction for enterprise management.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128247349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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