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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 10, 2017</section><section class="editors"><strong>Editor(s)-in-Chief: </strong><span class="editor">Lei Shu</span> and <span class="editor">Carlo Cecati</span></section><section class="editors"><strong>Guest Editor(s): </strong><span class="editor">Mithun Mukherjee</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.1-2-2017.152152">Constructing a Knowledge Base for Entertainment by Interlinking Multiple Data Sources</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>17</strong><span class="info-separator">(</span>10<span class="info-separator">)</span><span class="info-separator">: </span>e1</dd><br><dt class="title">Authors: </dt><dd class="value">Haklae Kim, Jungyeon Yang</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">This paper describes a knowledge base for entertainment domains, including movies, music, and celebrities. We present an ontology model for representing graph-based knowledge, and describe knowledge processing techniques for constructing an entertainment knowledge base, which is comprised of collec…</span><span class="full">This paper describes a knowledge base for entertainment domains, including movies, music, and celebrities. We present an ontology model for representing graph-based knowledge, and describe knowledge processing techniques for constructing an entertainment knowledge base, which is comprised of collection and extraction of large-scale data, and knowledge transformation from them. In particular, we address entity resolution and consolidation techniques for integrating particular entities among heterogeneous data sources. We also demonstrate a mobile application using this knowledge base, which allows users to discover relevant entertainment content based on question & answer system.<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.1-2-2017.152153">Gatewaying theWireless Sensor Networks</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>17</strong><span class="info-separator">(</span>10<span class="info-separator">)</span><span class="info-separator">: </span>e2</dd><br><dt class="title">Authors: </dt><dd class="value">Ming Zhu, Zhaoshu Tang, Wenlong Yue, Lei Wang, Zhenquan Qin, Lei Shu, Liang Sun</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">With the dev elopmen t of Internet of Things (IoT), bridging wireless sensor netw orks (WSNs) with other netw orks has become importan t. We divide bridging solutions into tw o categ ories: the hardw are solutions and the middlew are solutions. The hardw are solutions have both low pow er short dis…</span><span class="full">With the dev elopmen t of Internet of Things (IoT), bridging wireless sensor netw orks (WSNs) with other netw orks has become importan t. We divide bridging solutions into tw o categ ories: the hardw are solutions and the middlew are solutions. The hardw are solutions have both low pow er short distance wireless interf aces and other types of transmission interf aces, e.g. GPRS, 3G/4G, via hardw are implemen tations, which is more stable and applicable for the depl oyed sensor netw orks. In the middlew are solutions, the whole system processes appropria te protocol conversion, and independence of hardw are, making it easier to be reused in dieren t applica tions and netw orks. This paper brief y presen ts the implemen tation details and key poin ts of each solution. Particular ly, we deriv e the most appropria te situa tion for each solution from our comparisons and discussions in terms of evaluation applied to dieren t criteria.<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.1-2-2017.152154">An Application of SMC to continuous validation of heterogeneous systems</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>17</strong><span class="info-separator">(</span>10<span class="info-separator">)</span><span class="info-separator">: </span>e3</dd><br><dt class="title">Authors: </dt><dd class="value">Alexandre Arnold, Massimo Baleani, Alberto Ferrari, Marco Marazza, Valerio Senni, Axel Legay, Jean Quilbeuf, Christoph Etzien</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">This paper considers the rigorous design of Systems of Systems (SoS), i.e. systems composed of a set of heterogeneous components whose number evolves with time. Such components cooperate to accomplish functions that they could not achieve in isolation. Examples of SoS include smart cities or airpor…</span><span class="full">This paper considers the rigorous design of Systems of Systems (SoS), i.e. systems composed of a set of heterogeneous components whose number evolves with time. Such components cooperate to accomplish functions that they could not achieve in isolation. Examples of SoS include smart cities or airport management system. The dynamical evolution of SoS behavior and architecture makes it impossible to design an appropriate solution beforehand. Consequently, existing approaches build on an iterative process that takes SoS evolution into account. A key challenge in this process is the ability to reason about and analyze a given view of the SoS (on a fixed number of SoS constituents) with respect to a set of goals, and use the results to eventually predict the evolution of the SoS. To address this challenge, we rely on a scalable formal verification technique known as Statistical Model Checking (SMC). SMC quantifies how close the current view is from achieving a given mission. We integrate SMC with existing industrial practice, by addressing both methodological and technological issues. Our contribution is: (1) a methodology for validation of SoS formal requirements; (2) a formal specification language able to express complex SoS requirements; (3) the adoption of current industry standards for simulation and heterogeneous systems integration ; (4) a robust SMC tool-chain integrated with system design tools used in practice. We illustrate the application of our SMC tool-chain and the obtained results on a case study.<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.1-2-2017.152155">A Novel Intrusion Detection Mechanism for SCADA systems which Automatically Adapts to Network Topology Changes</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>17</strong><span class="info-separator">(</span>10<span class="info-separator">)</span><span class="info-separator">: </span>e4</dd><br><dt class="title">Authors: </dt><dd class="value">Barnaby Stewart, Luis Rosa, Leandros A. Maglaras, Tiago J. Cruz, Mohamed Amine Ferrag, Paulo Simoes, Helge Janicke</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">Industrial Control Systems (ICS) are getting more vulnerable as they become increasingly interconnected with other systems. Industrial Internet of Things(IIoT) will bring new opportunities to business and society, along with new threats and security risks. One major change that ICS will face will b…</span><span class="full">Industrial Control Systems (ICS) are getting more vulnerable as they become increasingly interconnected with other systems. Industrial Internet of Things(IIoT) will bring new opportunities to business and society, along with new threats and security risks. One major change that ICS will face will be that of the dynamic network topology. Changes in the network architecture will aﬀect the performance of the ICS along with the eﬃciency of the security mechanisms that are deployed. The current article investigates how changes in the network architecture of a supervisory control and data acquisition (SCADA) system aﬀect the performance of an Intrusion Detection System IDS that is based on the One class Support Vector Machine (OCSVM). Also the article proposes an adaptive mechanism that can cope with such changes and can work in real time situations. The performance of the proposed adaptive IDS is tested using traces from a Hybrid ICS testbed with a dynamic topology.<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.1-2-2017.152156">Securing Smart Grid In-Network Aggregation through False Data Detection</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>17</strong><span class="info-separator">(</span>10<span class="info-separator">)</span><span class="info-separator">: </span>e5</dd><br><dt class="title">Authors: </dt><dd class="value">Lei Yang, Fengjun Li</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">Existing prevention-based secure in-network data aggregation schemes for the smart grids cannot eectively detect accidental errors and falsified data injected by malfunctioning or compromised meters. In this work, we develop a light-weight anomaly detector based on kernel density estimator to loca…</span><span class="full">Existing prevention-based secure in-network data aggregation schemes for the smart grids cannot eectively detect accidental errors and falsified data injected by malfunctioning or compromised meters. In this work, we develop a light-weight anomaly detector based on kernel density estimator to locate the smart meter from which the falsified data is injected. To reduce the overhead at the collector, we design a dynamic grouping scheme, which divides meters into multiple interconnected groups and distributes the verification and detection load among the root of the groups. To enable outlier detection at the root of the groups, we also design a novel data re-encryption scheme based on bilinear mapping so that data previously encrypted using the aggregation key is transformed in a form that can be recovered by the outlier detectors using a temporary re-encryption key. Therefore, our proposed detection scheme is compatible with existing in-network aggregation approaches based on additive homomorphic encryption. We analyze the security and eÿciency of our scheme in terms of storage, computation and communication overhead, and evaluate the performance of our outlier detector with experiments using real-world smart meter consumption data. The results show that the performance of the light-weight detector yield high precision and recall.<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">4</dd></dl><dl class="metadata"><dt class="title">Published</dt> <dd class="value">2017-02-01</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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