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<html><head><title>EAI Endorsed Transactions on Industrial Networks and Intelligent Systems - EUDL</title><link rel="icon" href="/images/favicon.ico"><link rel="stylesheet" type="text/css" href="/css/screen.css"><link rel="stylesheet" href="/css/zenburn.css"><meta http-equiv="Content-Type" content="charset=utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="Description" content="Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/inis/index"><script type="text/javascript" src="https://services.eai.eu//load-signup-form/EAI"></script><script type="text/javascript" src="https://services.eai.eu//ujs/forms/signup/sso-client.js"></script><script type="text/javascript">if (!window.EUDL){ window.EUDL={} };EUDL.cas_url="https://account.eai.eu/cas";EUDL.profile_url="https://account.eai.eu";if(window.SSO){SSO.set_mode('eai')};</script><script type="text/javascript" src="/js/jquery.js"></script><script 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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 16, 2018</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.29-11-2018.155885">Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>18</strong><span class="info-separator">(</span>16<span class="info-separator">)</span><span class="info-separator">: </span>e1</dd><br><dt class="title">Authors: </dt><dd class="value">Dongxu Han, Hongbo Du, Sabah Jassim </dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">Gaussian Bayes classifiers are widely used in machine learning for various purposes. Its special characteristic has provided a great capacity for estimating the likelihood and reliability of individual classification decision made, which has been used in many areas such as decision support assessme…</span><span class="full">Gaussian Bayes classifiers are widely used in machine learning for various purposes. Its special characteristic has provided a great capacity for estimating the likelihood and reliability of individual classification decision made, which has been used in many areas such as decision support assessments and risk analysis. However, Gaussian Bayes models tend to perform poorly when processing feature vectors of high dimensionality. This limitation is often resolved using dimension reduction techniques such as Principal Component Analysis. Conventional approaches on reducing dimensionalities usually rely on using a simple threshold based on accuracy measurements or sampling characteristics but rarely consider the sensitivity aspect of the prediction model created. In this paper, we have investigated the influence of eigenvalue selections on Gaussian Bayes classifiers in the context of sensitivity adjustment. Experiments based on real-life data have shown indicative and intriguing results. <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.29-11-2018.155998">Uplink Performance of Cell-Free Massive MIMO with Access Point Selections</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>18</strong><span class="info-separator">(</span>16<span class="info-separator">)</span><span class="info-separator">: </span>e2</dd><br><dt class="title">Authors: </dt><dd class="value">Toan X. Doan, Long D. Nguyen</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">Cell-free massive multiple-input multiple-output (MIMO), in which a massive number of access points (APs) distributed over a large area serve a smaller number of users in the same time and frequency resources, inherits advantages from conventional massive MIMO (i.e. favourable propagation and chann…</span><span class="full">Cell-free massive multiple-input multiple-output (MIMO), in which a massive number of access points (APs) distributed over a large area serve a smaller number of users in the same time and frequency resources, inherits advantages from conventional massive MIMO (i.e. favourable propagation and channel hardening), and distributed system (i.e. macro diversity gain). As a result, cell-free massive MIMO can provide a great spectral eﬃciency, high capacity and oﬀer uniformly great service for all users. To contribute to this great concept,an uplink and downlink performance of cell-free massive MIMO are investigated in this work. Novel access point selection and signal detection schemes are proposed to reduce the requirements of backhaul links connecting the APs and the central processing unit, and to improve the system performance in terms of the achievable rate. Note that most of signal detection schemes for cell-free massive MIMO in the literature rely on the channel hardening property, with results in less accuracy for small and moderate number of APs. Firstly, closed-form expressions for the achievable rate of the downlink and uplink are derived. Then, performance comparisons between the proposed signal detection scheme and the conventional scheme are exploited. The result shows that the proposed scheme (with the novel AP selection and signal detection) outperforms the conventional scheme in terms of the achievable rate and the amount of data load exchanging over the backhaul links.<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.29-11-2018.155999">Map Matching Algorithm: Trajectory and Sequential Map Analysis on Road Network</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>18</strong><span class="info-separator">(</span>16<span class="info-separator">)</span><span class="info-separator">: </span>e3</dd><br><dt class="title">Authors: </dt><dd class="value">Kanta Prasad Sharma, Ramesh C. Poonia, Raghvendra Kumar, Surendra Sunda, Dac-Nhuong Le</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">The Global Positioning System (GPS) tracking data is essential for sensor data sources. It plays an important role for various systems like Traﬃc assessment and Prediction, routing and navigation, Fleet management etc. Trajectory data accuracy is key factor for sampling based vehicle movement using…</span><span class="full">The Global Positioning System (GPS) tracking data is essential for sensor data sources. It plays an important role for various systems like Traﬃc assessment and Prediction, routing and navigation, Fleet management etc. Trajectory data accuracy is key factor for sampling based vehicle movement using existing GPS alerting systems. GPS navigation process is not reliable because of weak signaling transmission, weather scenario, specially, tall buildings area and drass sectors in Indian scenario. Map matching ﬁnding a path between available points on the active road segment, enhance road data accuracy through minimize frechet distance for future purpose. Therefore, accurate road data, become necessary for fast map matching outcomes. This work provides to locate the frechet distance on available free space for accurate path ﬁnding. This work also contributes to measuring frechet distance, trajectory data error estimation and ﬁnding free space surface on road network with sequential map computational method.<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.29-11-2018.156000">Inpainting large missing regions based on Seam Carving</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>18</strong><span class="info-separator">(</span>16<span class="info-separator">)</span><span class="info-separator">: </span>e4</dd><br><dt class="title">Authors: </dt><dd class="value">Ahmed K. Al-Jaberi, Sabah A. Jassim, Naseer Al-Jawad</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">Inpainting techniques are developed to recover missing image information. Existing inpainting approaches are: Partial Differential Equations Based Inpainting (PDE-BI) and Exemplar-Based Inpainting (EBI). PDE-BI methods used to fill in the missing information via information propagation from neighbo…</span><span class="full">Inpainting techniques are developed to recover missing image information. Existing inpainting approaches are: Partial Differential Equations Based Inpainting (PDE-BI) and Exemplar-Based Inpainting (EBI). PDE-BI methods used to fill in the missing information via information propagation from neighbouring areas. However, it can only reconstruct successfully small missing regions that are surrounded by limited texture. However, EBI methods are used to recover large regions with richly-textured/structured areas around them, moreover, artefacts are likely to occur. This paper proposes a technique to reduce the missing region size based on seam carving approach, which enables EBI and PDE-BI to recover the missing part. In our proposal, seam carving is used to reduce only the size of the missing region, to be subsequently recovered using EBI method. The added extra paths resulting from the added seams is repaired using PDE-BI. This method outperformed the state-of-art EBI methods.<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.29-11-2018.156001">Natural Disaster and Environmental Monitoring System for Smart Cities: Design and Installation Insights</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>18</strong><span class="info-separator">(</span>16<span class="info-separator">)</span><span class="info-separator">: </span>e5</dd><br><dt class="title">Authors: </dt><dd class="value">Nguyen-Son Vo, Antonino Masaracchia, Long D. Nguyen, Ba-Cuong Huynh</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">Climate change is mainly caused by human activities. Consequently, natural disasters such as ﬂooding, storm, and drought are attacking people at high frequency and extreme damage. Besides, many megacities have been facing the rapid urbanization problem of carbon emission, noise, dust, and temperatu…</span><span class="full">Climate change is mainly caused by human activities. Consequently, natural disasters such as ﬂooding, storm, and drought are attacking people at high frequency and extreme damage. Besides, many megacities have been facing the rapid urbanization problem of carbon emission, noise, dust, and temperature that seriously impacts on the living conditions of people. In this paper, we design and implement a monitoring system for early detecting and warning the natural disasters and the environmental threats of the rapid urbanization in Quang Nam and Da Nang provinces in the Central Vietnam. The system will sense, communicate, store, process, and display the important information including precipitation, wind speed and direction, water level, and landslide/earthquake in Quang Nam and CO2 emission, temperature, dust, and noise in Da Nang. The experimental results can help the local government and citizens with better management of natural disasters and environmental threats for smart cities.<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">5</dd></dl><dl class="metadata"><dt class="title">Published</dt> <dd class="value">2018-11-29</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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