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href="/journal/inis" title="EAI Endorsed Transactions on Industrial Networks and Intelligent Systems"><img src="/attachment/38465"></a></section><section class="issn"><strong>ISSN: </strong>2410-0218</section><section class="escripts link"><a href="https://escripts.eai.eu/paper/submit">Submit Article</a></section><section class="instructions link"><a href="/instructions">Submission Instructions</a></section><section class="ethics link"><a href="/ethics">Ethics and Malpractice Statement</a></section><section class="back-to-journal link"><a href="/journal/inis">Back to Journal Page</a></section><section class="browse-filters"><div class="browse-by"><a class="browse-link">2024<span class="pointer"></span></a><div class="filters"><a href="/issue/inis/12/1" class="filter ">Issue 1</a></div><a class="browse-link">2024<span class="pointer"></span></a><div class="filters"><a href="/issue/inis/11/4" class="filter ">Issue 4</a><a href="/issue/inis/11/3" class="filter ">Issue 3</a><a 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1</a></div></div></section></section><section class="info-and-search"><div class="manage-menu"></div><a href="/journal/inis"><h1>EAI Endorsed Transactions on Industrial Networks and Intelligent Systems</h1></a><section class="issue-number">Issue 23, 2020</section><section class="editors"><strong>Editor(s)-in-Chief: </strong><span class="editor">Trung Q. Duong</span> and <span class="editor">Le Nguyen Bao</span></section><section class="issue-tabs"><div class="tabs"><ul><li><a name="articles">Articles</a></li><li><a name="meta">Information</a></li></ul></div><div class="content"><div name="articles"><section id="publications-results" class="search-results"><ul class="results-list"><li class="result-item article-light first"><h3><a href="/doi/10.4108/eai.13-7-2018.163925">Kendall tau sequence distance: Extending Kendall tau from ranks to sequences</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>20</strong><span class="info-separator">(</span>23<span class="info-separator">)</span><span class="info-separator">: </span>e1</dd><br><dt class="title">Author: </dt><dd class="value">Vincent A. Cicirello</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">An edit distance is a measure of the minimum cost sequence of edit operations to transform one structure into another. Edit distance can be used as a measure of similarity as part of a pattern recognition system, with lower values of edit distance implying more similar structures. Edit distance i…</span><span class="full">An edit distance is a measure of the minimum cost sequence of edit operations to transform one structure <br>into another. Edit distance can be used as a measure of similarity as part of a pattern recognition system, with <br>lower values of edit distance implying more similar structures. Edit distance is most commonly encountered <br>within the context of strings, where Wagner and Fischer’s string edit distance is perhaps the most well-known. <br>However, edit distance is not limited to strings. For example, there are several edit distance measures for <br>permutations, including Wagner and Fischer’s string edit distance since a permutation is a special case of <br>a string. However, another edit distance for permutations is Kendall tau distance, which is the number of <br>pairwise element inversions. On permutations, Kendall tau distance is equivalent to an edit distance with <br>adjacent swap as the edit operation. A permutation is often used to represent a total ranking over a set <br>of elements. There exist multiple extensions of Kendall tau distance from total rankings (permutations) to <br>partial rankings (i.e., where multiple elements may have the same rank), but none of these are suitable for <br>computing distance between sequences. We set out to explore extending Kendall tau distance in a different <br>direction, namely from the special case of permutations to the more general case of strings or sequences of <br>elements from some finite alphabet. We name our distance metric Kendall tau sequence distance, and define <br>it as the minimum number of adjacent swaps necessary to transform one sequence into the other. We provide <br>two O(n lg n) algorithms for computing it, and experimentally compare their relative performance. We also <br>provide reference implementations of both algorithms in an open source Java library.<br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eai.13-7-2018.163985">SAM Centrality: a Hop-Based Centrality Measure for Ranking Users in Social Network</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>20</strong><span class="info-separator">(</span>23<span class="info-separator">)</span><span class="info-separator">: </span>e2</dd><br><dt class="title">Authors: </dt><dd class="value">Abdul Samad, Mamoona Qadir, Ishrat Nawaz, Muhammad Arshad Islam, Muhammad Aleem</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">Majority of researcher are attracted by the social network analysis due to the rush of people towards social network. Along with many problems, social network analysis is facing an interesting problem that is ranking of users in social network which is gaining more attention due to the increasing…</span><span class="full">Majority of researcher are attracted by the social network analysis due to the rush of people towards social <br>network. Along with many problems, social network analysis is facing an interesting problem that is ranking <br>of users in social network which is gaining more attention due to the increasing number of social users. <br>Measuring centrality of nodes in a social graph, have been important issue in social network analysis. Lot <br>of centrality methods have been proposed in this regard. In this paper, hop based centrality measures called <br>SAM is purposed. To investigate the measure, we applied on various dataset. In comparisons, on all these <br>social graphs, we obtain better results than other centrality measures (i.e., Degree, PageRank, Betweeness and <br>Closeness) using SIR model.<br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eai.13-7-2018.163988">A Comprehensive Survey of Link Prediction Techniques for Social Network</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>20</strong><span class="info-separator">(</span>23<span class="info-separator">)</span><span class="info-separator">: </span>e3</dd><br><dt class="title">Authors: </dt><dd class="value">Abdul Samad, Mamoona Qadir, Ishrat Nawaz, Muhammad Arshad Islam, Muhammad Aleem</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">A growing trend of using social networking sites is attracting researchers to study and analyze different aspects of social network. Besides many problems, link prediction is a fascinating problem in the field of social network analysis (SNA). Link prediction, in social network analysis, is a tas…</span><span class="full">A growing trend of using social networking sites is attracting researchers to study and analyze different <br>aspects of social network. Besides many problems, link prediction is a fascinating problem in the field of social <br>network analysis (SNA). Link prediction, in social network analysis, is a task of identifying the missing links <br>and predicting the new links. Several researchers have proposed solutions for the link prediction problem <br>during the past two decades. However, there is a need to provide comprehensive overview of the significant <br>contributions for a thorough analysis. The objective of this review is to summaries and discuss the existing <br>link prediction algorithms in a common context for an unbiased analysis. The extensive review is presented <br>by constructing the systematical category for proposed algorithms, selected problems, evaluation measures <br>along with selected network datasets. Finally, applications of link prediction are discussed.<br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eai.18-5-2020.164585">Spatiotemporal Variability of Air Quality Time Series for developing countries: Case of Ho Chi Minh city, Vietnam</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>20</strong><span class="info-separator">(</span>23<span class="info-separator">)</span><span class="info-separator">: </span>e4</dd><br><dt class="title">Authors: </dt><dd class="value">Hue Nam K. Nguyen, Nam Hung N. Tran, Bac T. Vu, Bang Q. Ho</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">In the recent years, air pollution has become a severe problem not only for Vietnam, but also for other countries. Ho Chi Minh City (HCMC) is the largest city in Vietnam where many air pollutants exceeded the Vietnam national technical regulation in ambient air quality including PM2.5, NOx, Ozone a…</span><span class="full">In the recent years, air pollution has become a severe problem not only for Vietnam, but also for other countries. Ho Chi Minh City (HCMC) is the largest city in Vietnam where many air pollutants exceeded the Vietnam national technical regulation in ambient air quality including PM2.5, NOx, Ozone and CO. These high pollutant concentrations have destroyed human health of people in Ho Chi Minh City. This research is aimed to (i) analyse and assess the change in spatiotemporal of the air polluted substances in Ho Chi Minh City; (ii) study the impact of weather patern to air pollutants dispersion over Ho Chi Minh City. The description statistical method is applied to evaluate the air quality in every monitoring location during the period 2005 to 2016, and the Inverse Distance Weighting (IDW) spatial interpolation from Geographical Information Systems (GIS) was applied to create a map of polluted substances in air quality, especially Total Suspended Particles (TSP), Nitrogen oxides (NOx), Lead (Pb) and Carbon monoxide (CO), of the change in concentration of the polluted substances for every pixel in all researched locations. IDW method was validated by comparing between air quality monitoring and IDW spatial interpolation method. The results shown that IDW method is qualify for this study with R2 = 0.93 and d = 0.74 at Dinh Tien Hoang – Dien Bien Phu, An Suong and Go Vap monitoring locations. The IDW interpolation method was also good result for NOx with R2 = 0.96 and d = 0.75 at the Hang Xanh, An Suong, and Huynh Tan Phat monitoring locations. The results shown that the highest CO concentration is in Go Vap district, with the average is 14.849 mg/m3, TSP is highest in An Suong area with the average is 0.687mg/m3. NOx is highest in Dien Bien Phu wih 0.199 mg/m3. The spatial of air quality shown that the spread continues towards the north, northwest, and northeast direction of Ho Chi Minh City because the main air emission sources are in the center of HCMC and the main wind direction is south, southeast and northeast. The wind direction blows air pollutants from center of city toward to the north, northwest, and northeast of Ho Chi Minh City. The results of study also shown that the relations between policy and air pollution level. Policy interventions on air quality management will have a major impact on reducing air pollution for HCMC, such as policies that tighten vehicle exhaust emissions (from EURO II standard to EURO VI standard) and policy on cleaner fuel.<br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eai.18-5-2020.164586">Control Algorithms for UAVs: A Comprehensive Survey</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>20</strong><span class="info-separator">(</span>23<span class="info-separator">)</span><span class="info-separator">: </span>e5</dd><br><dt class="title">Authors: </dt><dd class="value">Hoa T. Nguyen, Toan V. Quyen, Cuong V. Nguyen, Anh M. Le, Hoa T. Tran, Minh T. Nguyen</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">The development of unmanned aerial vehicles (UAVs) has become a revolution in the ﬁelds of data collection, surveying, monitoring, and tracking objects in the ﬁeld. Many control and navigation algorithms are experimented and deployed for UAVs, especially quadrotors. Recent numerous approaches are g…</span><span class="full">The development of unmanned aerial vehicles (UAVs) has become a revolution in the ﬁelds of data collection, surveying, monitoring, and tracking objects in the ﬁeld. Many control and navigation algorithms are experimented and deployed for UAVs, especially quadrotors. Recent numerous approaches are geared towards reducing the inﬂuence of external disturbances to enhance the performance of UAVs. Nevertheless, designing cutting-edge controllers following the requirements of the applications is still a huge challenge. Based on the operating characteristics and movement principle of a quadrotor, this work reviews potential control algorithms of the current researches in the ﬁeld of the quadrotor ﬂight controller. Besides, a comparison has been made to provide an overview of the advantages and disadvantages of the mentioned methods. At last, the challenges and future directions of the quadrotor ﬂight controller are suggested.<br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li></ul></section></div><div name="meta"><h2>Scope</h2><div class="abstract"><div class="shortened"><p>Along with the fast development of computer technologies, e.g., ubiquitous computing, cloud computing and cyber-physical system, all kinds of networks (e.g., control network, communication network, sensor network, body area network, social network, opportunistic network, cloud-based network, etc.) …</p></div><div class="full"><p>Along with the fast development of computer technologies, e.g., ubiquitous computing, cloud computing and cyber-physical system, all kinds of networks (e.g., control network, communication network, sensor network, body area network, social network, opportunistic network, cloud-based network, etc.) appeared and were applied in large-scale factories, including a lot of traditional and new industries, e.g., textile industry, coal industry, mining industry, steel industry, machinery industry, petrochemical industry, and biomedical industry, etc. Assisted by various industrial networks, automation in industry can reduce cost greatly because it takes advantage of control systems and information technologies to optimize productivity in the production of goods and delivery of services. However, the industrial environment is dynamic and harsh usually, including extreme temperature, humidity, electromagnetic interference and vibration, which proposed specific requirements to intelligent industrial systems under certain circumstances. All these highlight the criticality of the design, analysis and implementation of intelligent industrial systems.</p></div> <span class="expander more"><a class="trigger">more »</a></span></div><h2>Topics</h2><div class="abstract"><div class="shortened"><ul> <li>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.</li> <li>Applications of social networking, big data, ubiqui…</li> </ul></div><div class="full"><ul> <li>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.</li> <li>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.)</li> <li>Analysis of industrial control and communication networks, including network lifetime, security, network scalability, reliability, stability, etc.</li> <li>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</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>Novel control techniques, with respect to process control, equipment control, supervisory control, adaptive control, motion control, etc.</li> <li>Automated manufacturing systems, regarding formal modeling and analysis of manufacturing systems, scheduling of manufacturing systems, queuing systems and petri nets in manufacturing systems, etc.</li> <li>Computational intelligence in automation, including neural, fuzzy, evolutionary approaches in automation, ant colonies optimization and swarm intelligence in automation, machine learning, expert systems, etc.</li> <li>Hardware and software design and development for intelligent systems, such as intelligent and humanized production monitoring and control, etc.</li> <li>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.</li> <li>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.</li> <li>Simulation and testbed of current industrial networks and intelligent systems, including network performance analysis, automated manufacturing, intelligent monitoring, disaster prevention, etc.</li> <li>Vision of future smart factories, service, marketing, and their integration, incorporating current existing technologies.</li> <li>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.</li> <li>Pattern recognition methods for various industries and services: including statistical theory, clustering, similarity measures, unsupervised learning, supervised learning, etc.</li> <li>Survey, review and essay of current industrial networks researches and intelligent systems development.</li> </ul></div> <span class="expander more"><a class="trigger">more »</a></span></div><h2>Indexing</h2><div class="abstract"><div class="shortened"><ul> <li><a href="https://www.scopus.com/sourceid/21101049547">Scopus</a></li> <li><a href="https://doaj.org/toc/2410-0218">DOAJ</a></li> <li><a href="https://dblp.uni-trier.de/db/journals/inis/">DBLP</a></li> <li><a href="https://search.crossref.org/?q=2410-0218">CrossRef</a></li> <li>[OCLC Discovery Services](https://www.worldcat.org/search?q=eai+endorsed+tran…</li> </ul></div><div class="full"><ul> <li><a href="https://www.scopus.com/sourceid/21101049547">Scopus</a></li> <li><a href="https://doaj.org/toc/2410-0218">DOAJ</a></li> <li><a href="https://dblp.uni-trier.de/db/journals/inis/">DBLP</a></li> <li><a href="https://search.crossref.org/?q=2410-0218">CrossRef</a></li> <li><a href="https://www.worldcat.org/search?q=eai+endorsed+transactions+on+industrial+networks&qt=owc_search">OCLC Discovery Services</a></li> <li><a href="https://europub.co.uk/journals/8120">EuroPub</a></li> <li><a href="https://publons.com/journal/29023/eai-endorsed-transactions-on-industrial-networks-a">Publons</a></li> <li><a href="https://app.dimensions.ai/discover/publication?or_facet_source_title=jour.1152852">Dimensions</a></li> <li><a href="https://www.proquest.com/products-services/Publicly-Available-Content-Database.html#overviewlinkSection">Publicly Available Content Database (ProQuest)</a></li> <li><a href="https://www.proquest.com/products-services/adv_tech_aero.html">Advanced Technologies & Aerospace Database (ProQuest)</a></li> <li><a href="https://www.proquest.com/products-services/adv_tech_aero.html">SciTech Premium Collection (ProQuest)</a></li> <li><a href="https://scholar.google.sk/scholar?as_ylo=2018&q=source:EAI+source:Endorsed+source:Transactions+source:on+source:Industrial+source:Networks+source:and+source:Intelligent+source:Systems&hl=es&as_sdt=0,5">Google Scholar</a></li> </ul></div> <span class="expander more"><a class="trigger">more »</a></span></div><h2>Editorial Board</h2><div class="abstract"><div class="shortened"><ul> <li>Ala Al-Fuqaha (Western Michigan University, USA)</li> <li>Al-Sakib Khan Pathan (Southeast University, Bangladesh)</li> <li>Ammar Rayes (Cisco Systems, USA)</li> <li>Antonino Masaracchia (IIT-CNR, Italy)</li> <li>Athanasios Maglaras (Dr, Prof . ofT.E.I. of Larissa)</li> <li>Berk Canberk (Northeastern University, USA)</li> <li>Ca V. Phan (…</li> </ul></div><div class="full"><ul> <li>Ala Al-Fuqaha (Western Michigan University, USA)</li> <li>Al-Sakib Khan Pathan (Southeast University, Bangladesh)</li> <li>Ammar Rayes (Cisco Systems, USA)</li> <li>Antonino Masaracchia (IIT-CNR, Italy)</li> <li>Athanasios Maglaras (Dr, Prof . ofT.E.I. of Larissa)</li> <li>Berk Canberk (Northeastern University, USA)</li> <li>Ca V. Phan (Ho Chi Minh City University of Technology and Education, Vietnam)</li> <li>Chau Yuen (Singapore University of Technology and Design, Singapore)</li> <li>Chengfei Liu (Swinburne University of Technology, Australia)</li> <li>Chinmoy Kundu (University of Texas at Dallas, USA)</li> <li>Christer Carlsson (Åbo Akademi University, Finland)</li> <li>Chunsheng Zhu (University of British Columbia)</li> <li>Constandinos Mavromoustakis (University of Nicosia, Cyprus)</li> <li>Der-Jiunn Deng (National Changhua University of Education, Taiwan)</li> <li>Dickson Chiu (The University of Hong Kong)</li> <li>Eleanna Kafeza (Athens University of Economics and Business, Greece)</li> <li>Fu-ren Lin (National Tsing Hua University, Taiwan)</li> <li>Gerhard Hancke (University of London, UK)</li> <li>Guangjie Han (Hohai University, China)</li> <li>Guojun Wang (Central South University, China)</li> <li>Hacene Fouchal (University of Reims Champagne-Ardenne, France)</li> <li>Haklae Kim (Chung-Ang University, South Korea)</li> <li>Halil Yetgin (Bitlis Eren University, Turkey)</li> <li>Hideyasu Sasaki (Ritsumeikan University, Kyoto, Japan)</li> <li>Ho-fung Leung (Chinese University of Hong Kong, Hong Kong)</li> <li>Honggang Wang (University of Massachusetts Dartmouth, USA)</li> <li>Hua Hu (Hangzhou Dianzi University, China)</li> <li>Ibrahim Kushchu (Mobile Government Consortium International, UK)</li> <li>Irene Kafeza (Irene Law Office, Greece)</li> <li>Isabelle Comyn-Wattiau (ESSEC Business School Paris, France)</li> <li>Jaime Lloret- Mauri (Universitat Politècnica de València, Spain)</li> <li>Javier M. Aguiar (Universidad de Valladolid, Valladolid, Spain)</li> <li>Jesus Alonso-Zarate (Telecommunications Technology Center of Catalonia, Spain)</li> <li>Jian Yang (Macquarie University, Australia)</li> <li>Jiankun Hu (University of New South Wales, Australia)</li> <li>Jianmin Jiang (Shenzhen University)</li> <li>Jianwei Niu (Beihang University, China)</li> <li>Jinlei Jiang (Tsinghua University, China)</li> <li>Jinsong Wu (Bell Laboratory, China)</li> <li>Joel Rodrigues (Inst. Telecomunicações, Univ. of Beira Interior, Portugal)</li> <li>Juan Trujillo (University of Alicante, Spain)</li> <li>Jucheng Yang (Tianjing University of Technology, China)</li> <li>Junqing Zhang (Queen's University Belfast)</li> <li>KUN WANG (Nanjing University of Posts and Telecommunications)</li> <li>Kuo-Ming Chao (Leader – Distributed Systems and Modelling Research Group, UK)</li> <li>Leandros A. Maglaras (De Montfort University, UK)</li> <li>Lei Wang (Dalian University of Technology, China)</li> <li>Liang Zhou (Nanjing University of Posts and Telecommunications, China)</li> <li>Long D. Nguyen (Dong Nai University, Vietnam)</li> <li>Maggie M. Wang (The University of Hong Kong, Hong Kong)</li> <li>Nghia Duong-Trung (German Research Center for Artificial Intelligence, Germany)</li> <li>Ngo Hoang Tu (Seoul National University of Science and Technology, South Korea)</li> <li>Nguyen Van Nam (Viettel, Vietnam)</li> <li>Nicholas C Romano (Oklahoma State University, USA)</li> <li>Noel Crespi (Institut Mines-Telecom, Telecom SudParis, France)</li> <li>Panlong Yang (PLA University of Science and Technology, China)</li> <li>Pasi Tyrväinen (University of Jyväskylä, Finland)</li> <li>Patrick C.K. Hung (University of Ontario Institute of Technology, Canada)</li> <li>Periklis Chatzimisios (Alexander TEI of Thessaloniki, Greece)</li> <li>Pierluigi Siano (Università degli Studi di Salerno, Italy)</li> <li>Pirkko Walden (Abo Akademi University, Finland)</li> <li>Phuong Bui (Duy Tan University, Vietnam)</li> <li>Raymond Y.K Lau (City University of Hong Kong, Hong Kong)</li> <li>Richard Yu (Carleton University, Canada)</li> <li>Rong Yu (Guangdong University of Technology, China)</li> <li>Rose Hu (Utah State University, USA)</li> <li>Sammy Chan (City University of HongKong, HK)</li> <li>Shing-Chi Cheung (Hong Kong University of Science and Technology, Hong Kong)</li> <li>Stephen J. H. Yang (National Central University, Taiwan)</li> <li>Syed Hassan Ahmed (University of Central Florida, USA)</li> <li>Thanh-Phuong Nguyen (University of Toulon, France)</li> <li>Tran Trung Duy (PTIT, VietNam)</li> <li>Trang Hoang (Ho Chi Minh City University of Technology - Vietnam National University Ho Chi Minh City, Vietnam)</li> <li>Tuan-Minh Pham (Phenikaa University, Vietnam)</li> <li>Umar Zakir Abdul Hamid (Sensible 4 Oy, Helsinki)</li> <li>Victor Leung (The University of British Columbia)</li> <li>Vo Nguyen Son Dr. (Duy Tan University, Vietnam)</li> <li>Wai-Wa Fung (Information Security and Forensics Society, Hong Kong)</li> <li>Walid Gaaloul (Institut National des Télécommunications, France)</li> <li>Weiwei Jiang, (Beijing University of Posts and Telecommunications (BUPT), China)</li> <li>Wendy W. Y. Hui (University of Nottingham at Ningbo, China)</li> <li>William Cheung (Hong Kong Baptist University, Hong Kong)</li> <li>Xianfu Chen (VTT Technical Research Centre of Finland, Finland)</li> <li>Xiang Gui (Massey University, New Zealand)</li> <li>Xiaoling Wu (Chinese Academy of Sciences, China)</li> <li>Xu Wang (Heriot Watt University, UK)</li> <li>Yan Bai (University of Washington Tacoma, USA)</li> <li>Yan Zhang (Simula Research Laboratory and University of Oslo, Norway)</li> <li>Yi Zhuang (Zhejian Gongshang University, China)</li> <li>Yong Li (Tsinghua University, China)</li> <li>Yong Tang (South China Normal University, China)</li> <li>Yuanfang Chen (Institute Mines-Telecom, University Pierre and Marie Curie )</li> <li>Yuexing Peng (Beijing University of Posts and Telecommunications, China)</li> <li>Yuqing Sun (Shangdong University, China)</li> <li>Zakaria Maamar (Zayed University, UAE)</li> <li>Zhangbing Zhou (China University of Geosciences, China)</li> <li>Zhichao Sheng (Shanghai University, China)</li> <li>ZhiMing Cai (Macau University of Science and Technology, Macau)</li> <li>Mithun Mukherjee (Nanjing University of Information Science and Technology, China)</li> <li> </li> </ul></div> <span class="expander more"><a class="trigger">more »</a></span></div><h2>Journal Blurb</h2><div class="abstract"><div class="shortened"><p>Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/inis/index</p></div><div class="full"><p>Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/inis/index</p></div> <span class="expander more"><a class="trigger">more »</a></span></div></div></div></section><section class="publication-info"><dl class="metadata"><dt class="title">Publisher</dt> <dd class="value">EAI</dd> <dt class="title">ISSN</dt> <dd class="value">2410-0218</dd> <dt class="title">Volume</dt> <dd class="value">7</dd></dl><dl class="metadata"><dt class="title">Published</dt> <dd class="value">2020-05-18</dd></dl></section></section></form></section></section><div class="clear"></div><footer><div class="links"><a href="https://www.ebsco.com/" target="_blank"><img class="logo ebsco-logo" src="/images/ebsco.png" alt="EBSCO"></a><a href="https://www.proquest.com/" target="_blank"><img class="logo proquest-logo" src="/images/proquest.png" alt="ProQuest"></a><a href="https://dblp.uni-trier.de/db/journals/publ/icst.html" target="_blank"><img class="logo dblp-logo" src="/images/dblp.png" alt="DBLP"></a><a 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