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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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id="journal"><form class="search-form" id="article_search" method="get"><section class="cover-and-filters"><section class="cover"><a href="/journal/inis" title="EAI Endorsed Transactions on Industrial Networks and Intelligent Systems"><img src="/attachment/23505"></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/12/2" class="filter ">Issue 2</a><a href="/issue/inis/12/1" class="filter ">Issue 1</a></div><a 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2</a></div><a class="browse-link">2014<span class="pointer"></span></a><div class="filters"><a href="/issue/inis/1/1" class="filter ">Issue 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 9, 2016</section><section class="editors"><strong>Editor(s)-in-Chief: </strong><span class="editor">Lei Shu</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.24-8-2015.2260594">Split and Merge Strategies for Solving Uncertain Equations Using Affine Arithmetic</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>16</strong><span class="info-separator">(</span>9<span class="info-separator">)</span><span class="info-separator">: </span>e1</dd><br><dt class="title">Authors: </dt><dd class="value">Oliver Scharf, Markus Olbrich, Erich Barke</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">The behaviour of systems is determined by various parameters. Due to several reasons like e. g. manufacturing tolerances these parameters can have some uncertainties. Corner Case and Monte Carlo simulations are well known approaches to handle uncertain systems. They sample the corners and random po…</span><span class="full">The behaviour of systems is determined by various parameters. Due to several reasons like e. g. manufacturing tolerances these parameters can have some uncertainties. Corner Case and Monte Carlo simulations are well known approaches to handle uncertain systems. They sample the corners and random points of the parameter space, respectively. Both require many runs and do not guarantee the inclusion of the worst case. As alternatives, range based approaches can be used. They model parameter uncertainties as ranges. The simulation outputs are ranges which include all possible results created by the parameter uncertainties. One type <br>of range arithmetic is the affine arithmetic, which allows <br>to maintain linear correlations to avoid over-approximation. An equation solver based on affine arithmetic has been proposed earlier. Unlike many other range based approaches it can solve implicit non-linear equations. This is necessary for analog circuit simulation. For large uncertainties the solver suffers from convergence problems. To overcome these problems it is possible to split the parameter ranges, calculate the solutions separately and merge them again. For higher dimensional systems this leads to excessive runtimes as each parameter is split. To minimize the additional runtime several split and merge strategies are proposed and compared using two analog circuit examples. <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.24-8-2015.2260869">Hybrid Simulation Using SAHISim Framework</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>16</strong><span class="info-separator">(</span>9<span class="info-separator">)</span><span class="info-separator">: </span>e2</dd><br><dt class="title">Authors: </dt><dd class="value">Muhammad Usman Awais, Wolfgang Gawlik, Gregor De-Cillia, Peter Palensky</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">Hybrid systems such as Cyber Physical Systems (CPS) are becoming more important with time. Apart from CPS there are many hybrid systems in nature. To perform a simulation based analysis of a hybrid system, a simulation framework is presented, named SAHISim. It is based on the most popular simulatio…</span><span class="full">Hybrid systems such as Cyber Physical Systems (CPS) are becoming more important with time. Apart from CPS there are many hybrid systems in nature. To perform a simulation based analysis of a hybrid system, a simulation framework is presented, named SAHISim. It is based on the most popular simulation interoperability standards, i.e. High Level Architecture (HLA) and Functional Mock-up Interface (FMI). Being a distributed architecture it is able to execute on cluster, cloud and other distributed topologies. Moreover, as it is based on standards so it allows many different simulation packages to interoperate, making it a flexible and robust solution for simulation based analysis. The underlying algorithm which enables the synchronization of different simulation components is discussed in detail. A test example is presented, whose results are compared to a monolithic simulation of the same model for verification of 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.24-8-2015.2261152">Using Finite Forkable DEVS for Decision-Making Based on Time Measured with Uncertainty</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>16</strong><span class="info-separator">(</span>9<span class="info-separator">)</span><span class="info-separator">: </span>e3</dd><br><dt class="title">Authors: </dt><dd class="value">Damian Vicino, Olivier Dalle, Gabriel Wainer</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">The time-line in Discrete Event Simulation (DES) is a sequence of events defined in a numerable subset of R+. When it comes from an experimental measurement, the timing of these events has a limited precision. This precision is usually well-known and documented for each instruments and procedures u…</span><span class="full">The time-line in Discrete Event Simulation (DES) is a sequence of events defined in a numerable subset of R+. When it comes from an experimental measurement, the timing of these events has a limited precision. This precision is usually well-known and documented for each instruments and procedures used for collecting experimental datas. <br>Therefore, these instruments and procedures produce measurement results expressed using values each associated with an uncertainty quantification, given by uncertainty intervals. <br>Tools have been developed in Continuous Systems modeling for deriving the uncertainty intervals of the final results corresponding to the propagation of the uncertainty intervals being evaluated. <br>These tools cannot be used in DES as they are defined, and no alternative tools that would apply to DES have been developed yet. <br>In this paper, we propose simulation algorithms, based on the Discrete Event System Specification (DEVS) formalism, that can be used to simulate and obtain every possible output and state trajectories of simulations that receive input values with uncertainty quantification. Then, we present a subclass of DEVS models, called Finite Forkable DEVS (FF-DEVS), that can be simulated by the proposed algorithms. This subclass ensures that the simulation is forking only a finite number of processes for each simulation step. Finally, we discuss the simulation of a traffic light model and show the trajectories obtained when it is subject to input uncertainty. <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.24-8-2015.2260899">A Fast Discrete Event Simulation Model for Queueing Network Systems</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>16</strong><span class="info-separator">(</span>9<span class="info-separator">)</span><span class="info-separator">: </span>e4</dd><br><dt class="title">Authors: </dt><dd class="value">Jose Vazquez-Avila, Remberto Sandoval-Arechiga, Ramon Parra-Michel</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">Based on Lindley's recursive equations for G/G/1 systems, this paper proposes a Fast Discrete Event Simulation (FDES) model for queueing networks. Equations for multiplexer and de-multiplexer elements are presented, which allows to simulate not only tandem but queueing networks with an arbitrary to…</span><span class="full">Based on Lindley's recursive equations for G/G/1 systems, this paper proposes a Fast Discrete Event Simulation (FDES) model for queueing networks. Equations for multiplexer and de-multiplexer elements are presented, which allows to simulate not only tandem but queueing networks with an arbitrary topology. Time savings obtained with FDES could speed up the analyses of large-scale queueing network systems. Experimental results show that FDES modeling can be two orders of magnitude faster than their counterparts based on the event-scheduling for practical cases.<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.24-8-2015.2261074">Improving ns-3 Emulation Support in Real-World Networking Scenarios</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis<span class="info-separator"> </span><strong>16</strong><span class="info-separator">(</span>9<span class="info-separator">)</span><span class="info-separator">: </span>e5</dd><br><dt class="title">Authors: </dt><dd class="value">Helder Fontes, Rui Campos, Manuel Ricardo</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">A common problem in networking research and development is the duplicate effort of writing simulation and implementation code. This duplication can be avoided through the use of fast-prototyping methodologies, which enable reusing simulation code in real prototyping and in production environments. …</span><span class="full">A common problem in networking research and development is the duplicate effort of writing simulation and implementation code. This duplication can be avoided through the use of fast-prototyping methodologies, which enable reusing simulation code in real prototyping and in production environments. Although this functionality is already available by using ns-3 emulation, there are still limitations regarding the support of real network interfaces and easy configuration of the network settings, such as IP and MAC addresses. <br>In this paper we propose an improved version of the ns-3 emulation component by introducing new functionalities that address these limitations. The new functionalities include the support of new types of real network interfaces and the easier integration of emulation nodes with existing networks by means of a new auto-configuration mechanism for ns-3 nodes. Experimental results obtained in a laboratorial testbed and in a real vehicular network testbed demonstrate the new functionalities proper operation, and their backwards compatibility with previously coded ns-3 scenarios.<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">3</dd></dl><dl class="metadata"><dt class="title">Published</dt> <dd class="value">2016-12-20</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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