EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

Issue 32, 2022

Articles
Information

Big Data and Knowledge Graph Based Fault Diagnosis for Electric Power Systems
Appears in:
inis 22(32): 1

Authors:
Yuzhong Zhou, Zhengping Lin, Liang Tu, Yufei Song, Zhengrong Wu

Abstract:
Fault detection plays an important role in the daily maintenance of power electric system. Big data and knowledge graph (KG) have been proposed by researchers to solve many problems in industrial Internet of Things, which also give lots of potentials in improving the performance of fault detection …Fault detection plays an important role in the daily maintenance of power electric system. Big data and knowledge graph (KG) have been proposed by researchers to solve many problems in industrial Internet of Things, which also give lots of potentials in improving the performance of fault detection for electric power systems. In particular, this paper analyzes a distributed knowledge graph framework for fault detection in the electric power systems, where multiple devices train their local detection models used for fault detection assisted with a central server. Each device owns its local data set composed of historical fault information and current device state, which can be used to train a local model for fault detection. To enhance the detection performance, the distributed devices interact with each other in the KG framework, where the devices ought to achieve the regional computation in addition to the model aggregation within a specified latency threshold. Through searching for the vibrant qualities together with determined ability at the devices, we enhance the knowledge graph framework by the optimum variety of energetic devices together with the restriction of latency as well as data transmission. Particularly, two data transmission bandwidth allocation (BA) schemes are developed for the distributed knowledge graph framework, through which scheme I is actually bared after the instantaneous device state information (DSI), and scheme II utilizes particle swarm optimization (PSO) technique along with the statistical DSI. The results of simulation on the examination as well as convergence are lastly demonstrated to show the advantages of the proposed distributed KG framework in the fault detection for the electric power systems.
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Retina-based quality assessment of tile-coded 360-degree videos
Appears in:
inis 22(32): 2

Authors:
Viet Hung Nguyen, Ngoc Nam Pham, Cong Thang Truong, Duy Tien Bui, Huu Thanh Nguyen, Thu Huong Truong

Abstract:
Nowadays, omnidirectional content, which delivers 360-degree views of scenes, is a significant aspect of Virtual Reality systems. While 360 video requires a lot of bandwidth, users only see visible tiles, therefore a large amount of bitrate can be saved without affecting the user’s experience on th…Nowadays, omnidirectional content, which delivers 360-degree views of scenes, is a significant aspect of Virtual Reality systems. While 360 video requires a lot of bandwidth, users only see visible tiles, therefore a large amount of bitrate can be saved without affecting the user’s experience on the service. The fact leads to current video adaptation solutions to filter out superfluous parts and extraneous bandwidth. To form a good basis for these adaptations, it is necessary to understand human’s video quality perception. In our research, we contribute to building an effective omnidirectional video database that can be applied to study the effects of the five zones of the human retina. We also design a new video quality assessment method to analyze the impacts of those zones of a 360 video according to the human retina. The proposed scheme is found to outperform 22 current objective quality measures by 11 to 31% in terms of the PCC parameter.
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A Framework of Deploying Blockchain in Wireless Sensor Networks
Appears in:
inis 22(32): 3

Authors:
Minh Nguyen, Cuong Nguyen, Hoang T. Tran

Abstract:
The most critical needs for wireless sensor networks (WSNs) are security, privacy, dependability, and autonomy. The networks might be vulnerable to hostile users and harmful usage if these problems are not ensured. Attacks and hazards are higher with centralized WSNs, particularly when data is shar…The most critical needs for wireless sensor networks (WSNs) are security, privacy, dependability, and autonomy. The networks might be vulnerable to hostile users and harmful usage if these problems are not ensured. Attacks and hazards are higher with centralized WSNs, particularly when data is shared with other businesses and sent between devices. In this paper, a WSN model with integrated blockchain security technology is proposed. Blockchains store the identity of each node. The validation is done by public blockchains and private blockchains. For sensor nodes, the authentication is implemented on the private blockchain. The public blockchain is used to authenticate cluster heads. Performing network attacks can easily be performed by unregistered nodes to access resources in the network. Broadcasting false information on the path of malicious nodes can increase packet latency and reduce packet delivery rate. In this paper, the model recommends the most secure nodes in the network to be used for secure routing. The main purpose is to reduce the attack of hackers from outside the network, improve the efficiency of detecting malicious nodes.
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Intelligent Bi-directional Relaying Communication for Edge Intelligence based Industrial IoT Networks
Appears in:
inis 22(32): 4

Authors:
Jun Liu, Yuwei Zhang, Jing Wang, Tao Cui, Lin Zhang, Chao Li, Kai Chen, Huang Huang, Xuan Zhou, Wei Zhou, Zhao Wang, Sun Li, Suili Feng, Dongqing Xie, Dahua Fan, Jianghong Ou, Jiangtao Ou, Yun Li, Haige Xiang, Kaimeno Dube, Abbarbas Muazu, Nakilavai Rono, Yajuan Tang

Abstract:
Within this specific record, our group study the two-way interact body (TWRN) that has a number of amplify-and-forward (AF) relays. In that, the best one is actually really got to help the info communication among sources. A interact option is actually really according to the obsolete channel probl…Within this specific record, our group study the two-way interact body (TWRN) that has a number of amplify-and-forward (AF) relays. In that, the best one is actually really got to help the info communication among sources. A interact option is actually really according to the obsolete channel problem information (CSI) in addition to our group analyze its own very personal effect on the system effectiveness in the Rayleigh fading atmospheres. Especially, we extremely preliminary acquire a restricted decreased connected for the outage opportunity and afterward current an asymptotic assessment for greater signal-to-noise ratio (SNR). Our group extra acquire a restricted decreased connected along with an asymptotic result on the authorize error cost (SER). Originating got via these results, our group easily quickly obtain that body system range order remain at unity offered that the CSI is actually really obsolete. Relative results reveal the rigidness on the effectiveness bounds along with the effects of obsolete interact option on the body system effectiveness. Simulation outcomes are likewise offered to corroborate the scholastic evaluation.
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Intelligent Reflecting Surface assisted RF Energy Harvesting Mobile Edge Computing NOMA Networks: Performance Analysis and Optimization
Appears in:
inis 22(32): 5

Authors:
Dac-Binh Ha, Van-Truong Truong, Yoonill Lee

Abstract:
In this paper, we focus on the performance analysis and optimization of an RF energy harvesting (EH) mobile edge computing (MEC) network by the assistance of the intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) schemes. Specifically, a pair of users harvest RF energy f…In this paper, we focus on the performance analysis and optimization of an RF energy harvesting (EH) mobile edge computing (MEC) network by the assistance of the intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) schemes. Specifically, a pair of users harvest RF energy from a hybrid access point (HAP) and offloads their tasks to the MEC server at HAP through wireless links by employing an IRS-aided and uplink NOMA scheme. To evaluate the performance of this proposed system, the closed-form expressions of successful computation and energy transfer efficiency probabilities are derived. We further formulate a multi-objective optimization problem and propose an algorithm to find the optimal energy harvesting time switching ratio value to achieve the best performance, namely SENSGA-II. Moreover, the impacts of the network parameters are provided to draw helpful insight into the system performance. Finally, the Monte-Carlo simulation results are shown to confirm the correctness of our analysis. The results have shown that the deployment of IRS can improve the performance of this considered RF EH NOMA system by increasing the number of reflecting elements.
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Scope

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal, which is jointly sponsored and co-organized by Duy Tan University (Vietnam), publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing.

Applications of wireless sensor networks, body area networks in large-scale industrial applications, such as fault theories of wireless networks, including routing, network control and management, reliable transmission and architectures, etc.
Applications of social networking, big data, ubiqui…

Applications of wireless sensor networks, body area networks in large-scale industrial applications, such as fault theories of wireless networks, including routing, network control and management, reliable transmission and architectures, etc.
Applications of social networking, big data, ubiquitous computing, mobile computing, and cloud computing in various industries and services (e.g., intelligent systems enhanced by social networking, cloud-based industrial networks, cloud-assisted intelligent systems, etc.)
Analysis of industrial control and communication networks, including network lifetime, security, network scalability, reliability, stability, etc.
Design and choice of industrial, intelligent, application-specific network protocols and algorithms (e.g., EtherNet/IP, Ethernet Powerlink, EtherCAT, Modbus-TCP, Profinet, SERCOS III, etc.) at any communication layer
Opportunistic networks in the industry, such as underwater sensor networks in sewage treatment systems, including establishing a temporary data transmission structure using available devices (e.g., underwater robot, surface data station, surface sink and under water sink), optimizing horizontal multi-hop data links (e.g., 3D data transmission), etc.
Applications of intelligent systems in various industries, including collaborative systems, quality control, optimization, decision support, planning, high-level control concepts (e.g., multi-agent and holonic systems, service-oriented architectures), low-level control concepts (e.g., IEC 61131-3 and IEC 61499-based control), advanced system engineering concepts (e.g., model-driven development, component-based design), supply chains, value chains, virtual organizations, and virtual societies, emergency preparedness, crisis management, business channels, electronic marketplaces, enterprise resources planning, etc.
Design and analysis of real-time embedded industrial systems, including real-time computing, real-time operating systems, real-time communications, networked embedded systems technology, etc.
Novel control techniques, with respect to process control, equipment control, supervisory control, adaptive control, motion control, etc.
Automated manufacturing systems, regarding formal modeling and analysis of manufacturing systems, scheduling of manufacturing systems, queuing systems and petri nets in manufacturing systems, etc.
Computational intelligence in automation, including neural, fuzzy, evolutionary approaches in automation, ant colonies optimization and swarm intelligence in automation, machine learning, expert systems, etc.
Hardware and software design and development for intelligent systems, such as intelligent and humanized production monitoring and control, etc.
Big data analysis and processing in various industries and services, including constructing data analysis models, providing data analysis and processing tools and designing various optimization algorithms based on data analysis.
Crowd-sourced behavior analysis in various industry and services, such as measuring and calculating the diffusion direction and speed of gas in the petrochemical industry based on crowd-sourced data from a large number of and various types of sensors, as well as product and service evaluation.
Simulation and testbed of current industrial networks and intelligent systems, including network performance analysis, automated manufacturing, intelligent monitoring, disaster prevention, etc.
Vision of future smart factories, service, marketing, and their integration, incorporating current existing technologies.
Multimedia applications, content management, process management and knowledge management for various industries, services, and engineering education: including multimedia processing, multimedia retrieval, multimedia indexing, image sensing, image processing, image coding, image recognition, etc.
Pattern recognition methods for various industries and services: including statistical theory, clustering, similarity measures, unsupervised learning, supervised learning, etc.
Survey, review and essay of current industrial networks researches and intelligent systems development.

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[OCLC Discovery Services](https://www.worldcat.org/search?q=eai+endorsed+tran…

Ala Al-Fuqaha (Western Michigan University, USA)
Al-Sakib Khan Pathan (Southeast University, Bangladesh)
Ammar Rayes (Cisco Systems, USA)
Antonino Masaracchia (IIT-CNR, Italy)
Athanasios Maglaras (Dr, Prof . ofT.E.I. of Larissa)
Berk Canberk (Northeastern University, USA)
Ca V. Phan (…

Ala Al-Fuqaha (Western Michigan University, USA)
Al-Sakib Khan Pathan (Southeast University, Bangladesh)
Ammar Rayes (Cisco Systems, USA)
Antonino Masaracchia (IIT-CNR, Italy)
Athanasios Maglaras (Dr, Prof . ofT.E.I. of Larissa)
Berk Canberk (Northeastern University, USA)
Ca V. Phan (Ho Chi Minh City University of Technology and Education, Vietnam)
Chau Yuen (Singapore University of Technology and Design, Singapore)
Chengfei Liu (Swinburne University of Technology, Australia)
Chinmoy Kundu (University of Texas at Dallas, USA)
Christer Carlsson (Åbo Akademi University, Finland)
Chunsheng Zhu (University of British Columbia)
Constandinos Mavromoustakis (University of Nicosia, Cyprus)
Der-Jiunn Deng (National Changhua University of Education, Taiwan)
Dickson Chiu (The University of Hong Kong)
Eleanna Kafeza (Athens University of Economics and Business, Greece)
Fu-ren Lin (National Tsing Hua University, Taiwan)
Gerhard Hancke (University of London, UK)
Guangjie Han (Hohai University, China)
Guojun Wang (Central South University, China)
Hacene Fouchal (University of Reims Champagne-Ardenne, France)
Haklae Kim (Chung-Ang University, South Korea)
Halil Yetgin (Bitlis Eren University, Turkey)
Hideyasu Sasaki (Ritsumeikan University, Kyoto, Japan)
Ho-fung Leung (Chinese University of Hong Kong, Hong Kong)
Honggang Wang (University of Massachusetts Dartmouth, USA)
Hua Hu (Hangzhou Dianzi University, China)
Ibrahim Kushchu (Mobile Government Consortium International, UK)
Irene Kafeza (Irene Law Office, Greece)
Isabelle Comyn-Wattiau (ESSEC Business School Paris, France)
Jaime Lloret- Mauri (Universitat Politècnica de València, Spain)
Javier M. Aguiar (Universidad de Valladolid, Valladolid, Spain)
Jesus Alonso-Zarate (Telecommunications Technology Center of Catalonia, Spain)
Jian Yang (Macquarie University, Australia)
Jiankun Hu (University of New South Wales, Australia)
Jianmin Jiang (Shenzhen University)
Jianwei Niu (Beihang University, China)
Jinlei Jiang (Tsinghua University, China)
Jinsong Wu (Bell Laboratory, China)
Joel Rodrigues (Inst. Telecomunicações, Univ. of Beira Interior, Portugal)
Juan Trujillo (University of Alicante, Spain)
Jucheng Yang (Tianjing University of Technology, China)
Junqing Zhang (Queen's University Belfast)
KUN WANG (Nanjing University of Posts and Telecommunications)
Kuo-Ming Chao (Leader – Distributed Systems and Modelling Research Group, UK)
Leandros A. Maglaras (De Montfort University, UK)
Lei Wang (Dalian University of Technology, China)
Liang Zhou (Nanjing University of Posts and Telecommunications, China)
Long D. Nguyen (Dong Nai University, Vietnam)
Maggie M. Wang (The University of Hong Kong, Hong Kong)
Nghia Duong-Trung (German Research Center for Artificial Intelligence, Germany)
Ngo Hoang Tu (Seoul National University of Science and Technology, South Korea)
Nguyen Van Nam (Viettel, Vietnam)
Nicholas C Romano (Oklahoma State University, USA)
Noel Crespi (Institut Mines-Telecom, Telecom SudParis, France)
Panlong Yang (PLA University of Science and Technology, China)
Pasi Tyrväinen (University of Jyväskylä, Finland)
Patrick C.K. Hung (University of Ontario Institute of Technology, Canada)
Periklis Chatzimisios (Alexander TEI of Thessaloniki, Greece)
Pierluigi Siano (Università degli Studi di Salerno, Italy)
Pirkko Walden (Abo Akademi University, Finland)
Phuong Bui (Duy Tan University, Vietnam)
Raymond Y.K Lau (City University of Hong Kong, Hong Kong)
Richard Yu (Carleton University, Canada)
Rong Yu (Guangdong University of Technology, China)
Rose Hu (Utah State University, USA)
Sammy Chan (City University of HongKong, HK)
Shing-Chi Cheung (Hong Kong University of Science and Technology, Hong Kong)
Stephen J. H. Yang (National Central University, Taiwan)
Syed Hassan Ahmed (University of Central Florida, USA)
Thanh-Phuong Nguyen (University of Toulon, France)
Tran Trung Duy (PTIT, VietNam)
Trang Hoang (Ho Chi Minh City University of Technology - Vietnam National University Ho Chi Minh City, Vietnam)
Tuan-Minh Pham (Phenikaa University, Vietnam)
Umar Zakir Abdul Hamid (Sensible 4 Oy, Helsinki)
Victor Leung (The University of British Columbia)
Vo Nguyen Son Dr. (Duy Tan University, Vietnam)
Wai-Wa Fung (Information Security and Forensics Society, Hong Kong)
Walid Gaaloul (Institut National des Télécommunications, France)
Weiwei Jiang, (Beijing University of Posts and Telecommunications (BUPT), China)
Wendy W. Y. Hui (University of Nottingham at Ningbo, China)
William Cheung (Hong Kong Baptist University, Hong Kong)
Xianfu Chen (VTT Technical Research Centre of Finland, Finland)
Xiang Gui (Massey University, New Zealand)
Xiaoling Wu (Chinese Academy of Sciences, China)
Xu Wang (Heriot Watt University, UK)
Yan Bai (University of Washington Tacoma, USA)
Yan Zhang (Simula Research Laboratory and University of Oslo, Norway)
Yi Zhuang (Zhejian Gongshang University, China)
Yong Li (Tsinghua University, China)
Yong Tang (South China Normal University, China)
Yuanfang Chen (Institute Mines-Telecom, University Pierre and Marie Curie )
Yuexing Peng (Beijing University of Posts and Telecommunications, China)
Yuqing Sun (Shangdong University, China)
Zakaria Maamar (Zayed University, UAE)
Zhangbing Zhou (China University of Geosciences, China)
Zhichao Sheng (Shanghai University, China)
ZhiMing Cai (Macau University of Science and Technology, Macau)
Mithun Mukherjee (Nanjing University of Information Science and Technology, China)

Journal Blurb

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/inis/index

Publisher: EAI
ISSN: 2410-0218
Volume: 9

Published: 2022-08-11

Big Data and Knowledge Graph Based Fault Diagnosis for Electric Power Systems

Retina-based quality assessment of tile-coded 360-degree videos

A Framework of Deploying Blockchain in Wireless Sensor Networks

Intelligent Bi-directional Relaying Communication for Edge Intelligence based Industrial IoT Networks

Intelligent Reflecting Surface assisted RF Energy Harvesting Mobile Edge Computing NOMA Networks: Performance Analysis and Optimization

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