Register | Login

EUDL

European Union Digital Library
Proceedings Journals Search EAI
2017
Issue 13Issue 12Issue 11Issue 10
2016
Issue 9Issue 8Issue 7Issue 6
2015
Issue 5Issue 4Issue 3Issue 2
Older volumes
Issue 1

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

Editor(s)-in-Chief: Lei Shu

Scope

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.) …

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

more »

Topics

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

Special Issues

Special issue editor: Mithun Mukherjee (GUDP)

Call for Papers: Special issue on Intelligent Networks with NFV/SDN and Big Data<…

Special issue editor: Mithun Mukherjee (GUDP)

Call for Papers: Special issue on Intelligent Networks with NFV/SDN and Big Data (Submission Deadline: June 15, 2018)

Call for Papers: Special issue on: Fog Computing for Intelligent Systems (Submission Deadline: August 15, 2018)

Call for Papers: Special issue on: Enabling Technologies for Networks-on-Chip (Submission Deadline: August 15, 2018)

more »

Editorial Board

  • Ala Al-Fuqaha (Western Michigan University, USA)
  • Al-Sakib Khan Pathan (Southeast University, Bangladesh)
  • Ammar Rayes (Cisco Systems, USA)
  • Athanasios Maglaras (Dr, Prof . ofT.E.I. of Larissa)
  • Chau Yuen (Singapore University of Technology and Design, …

  • Ala Al-Fuqaha (Western Michigan University, USA)
  • Al-Sakib Khan Pathan (Southeast University, Bangladesh)
  • Ammar Rayes (Cisco Systems, USA)
  • Athanasios Maglaras (Dr, Prof . ofT.E.I. of Larissa)
  • Chau Yuen (Singapore University of Technology and Design, Singapore)
  • Chengfei Liu (Swinburne University of Technology, Australia)
  • Christer Carlsson (Åbo Akademi University, Finland)
  • Chunsheng Zhu (University of British Columbia)
  • Constandinos Mavromoustakis (University of Nicosia, Cyprus)
  • Der-Jiunn Deng (National Changhua University of Education, Taiwan)
  • Dickson Chiu (The University of Hong Kong)
  • Eleanna Kafeza (Athens University of Economics and Business, Greece)
  • Fu-ren Lin (National Tsing Hua University, Taiwan)
  • Gerhard Hancke (University of London, UK)
  • Guangjie Han (Hohai University, China)
  • Guojun Wang (Central South University, China)
  • Hacene Fouchal (University of Reims Champagne-Ardenne, France)
  • Haklae Kim (Samsung Electronics, Co. Ltd, South Korea)
  • Hideyasu Sasaki (Ritsumeikan University, Kyoto, Japan)
  • Ho-fung Leung (Chinese University of Hong Kong, Hong Kong)
  • Honggang Wang (University of Massachusetts Dartmouth, USA)
  • Hua Hu (Hangzhou Dianzi University, China)
  • Ibrahim Kushchu (Mobile Government Consortium International, UK)
  • Irene Kafeza (Irene Law Office, Greece)
  • Isabelle Comyn-Wattiau (ESSEC Business School Paris, France)
  • Jaime Lloret- Mauri (Universitat Politècnica de València, Spain)
  • Javier M. Aguiar (Universidad de Valladolid, Valladolid, Spain)
  • Jesus Alonso-Zarate (Telecommunications Technology Center of Catalonia, Spain)
  • Jian Yang (Macquarie University, Australia)
  • Jiankun Hu (University of New South Wales, Australia)
  • Jianmin Jiang (Shenzhen University)
  • Jianwei Niu (Beihang University, China)
  • Jinlei Jiang (Tsinghua University, China)
  • Jinsong Wu (Bell Laboratory, China)
  • Joel Rodrigues (Inst. Telecomunicações, Univ. of Beira Interior, Portugal)
  • Juan Trujillo (University of Alicante, Spain)
  • Jucheng Yang (Tianjing University of Technology, China)
  • Junqing Zhang (Queen's University Belfast)
  • KUN WANG (Nanjing University of Posts and Telecommunications)
  • Kuo-Ming Chao (Leader – Distributed Systems and Modelling Research Group, UK)
  • Leandros A. Maglaras (De Montfort University, UK)
  • Leandros Maglaras (De Montfort University)
  • Lei Wang (Dalian University of Technology, China)
  • Liang Zhou (Nanjing University of Posts and Telecommunications, China)
  • Liangtian Wan (Electrical and Electrical Engineering, Nanyang Technological University,
  • Singapore)
  • Lu Liu (University of Derby, UK)
  • Maggie M. Wang (The University of Hong Kong, Hong Kong)
  • Malcolm Egan (INSA Lyon)
  • Marijn Janssen (Delft University of Technology, The Netherlands)
  • Nicholas C Romano (Oklahoma State University, USA)
  • Noel Crespi (Institut Mines-Telecom, Telecom SudParis, France)
  • P. Radha Krishna (SET Labs, Infosys Technologies Limited, India)
  • Panlong Yang (PLA University of Science and Technology, China)
  • Pasi Tyrväinen (University of Jyväskylä, Finland)
  • Patrick C.K. Hung (University of Ontario Institute of Technology, Canada)
  • Periklis Chatzimisios (Alexander TEI of Thessaloniki, Greece)
  • Pierluigi Siano (Università degli Studi di Salerno, Italy)
  • Pirkko Walden (Abo Akademi University, Finland)
  • Raymond Y.K Lau (City University of Hong Kong, Hong Kong)
  • Richard Yu (Carleton University, Canada)
  • Rong Yu (Guangdong University of Technology, China)
  • Rose Hu (Utah State University, USA)
  • Sammy Chan (City University of HongKong, HK)
  • Sghaier Guizani (Alfaisal University, Saudi Arabia)
  • Shing-Chi Cheung (Hong Kong University of Science and Technology, Hong Kong)
  • Shui Yu (Deakin University, Australia)
  • Song Guo (University of Aizu, Japan)
  • Stephen J. H. Yang (National Central University, Taiwan)
  • Syed Hassan Ahmed (University of Central Florida, USA)
  • Tiago Cruz (University of Coimbra)
  • Tran Trung (PTIT, VietNam)
  • Trung Q. Duong (Blekinge Institute of Technology, Sweden)
  • Victor Leung (The University of British Columbia)
  • Vo Nguyen Son Dr. (Duy Tan University, Vietnam)
  • Wai-Wa Fung (Information Security and Forensics Society, Hong Kong)
  • Walid Gaaloul (Institut National des Télécommunications, France)
  • Wendy W. Y. Hui (University of Nottingham at Ningbo, China)
  • William Cheung (Hong Kong Baptist University, Hong Kong)
  • Xianfu Chen (VTT Technical Research Centre of Finland, Finland)
  • Xiang Gui (Massey University, New Zealand)
  • Xiaoling Wu (Chinese Academy of Sciences, China)
  • Xu Wang (Heriot Watt University, UK)
  • Yan Bai (University of Washington Tacoma, USA)
  • Yan Zhang (Simula Research Laboratory and University of Oslo, Norway)
  • Yi Zhuang (Zhejian Gongshang University, China)
  • Yong Li (Tsinghua University, China)
  • Yong Tang (South China Normal University, China)
  • Yuanfang Chen (Institute Mines-Telecom, University Pierre and Marie Curie )
  • Yuexing Peng (Beijing University of Posts and Telecommunications, China)
  • Yuqing Sun (Shangdong University, China)
  • Zakaria Maamar (Zayed University, UAE)
  • Zhangbing Zhou (China University of Geosciences, China)
  • ZhiMing Cai (Macau University of Science and Technology, Macau)
  • Zhiqiang Huo (GDUPT,UoL)
more »

Submission Instructions
Publication Ethics and Malpractice Statement
Editors and Editorial Board
Publisher
EAI
ISSN
2410-0218
Volume
3
Published
30th Nov 2016
  • An Analysis of Increased Vertical Scaling in Three-Dimensional Virtual World Simulation

    Research Article in EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 16(8): e1

    Sean Mondesire, Jonathan Stevens, Douglas Maxwell

    Abstract
    In this paper, we describe the analysis of the effect of vertical computational scaling on the performance of a simulation based training prototype currently under development by the U.S. Army Resear…In this paper, we describe the analysis of the effect of vertical computational scaling on the performance of a simulation based training prototype currently under development by the U.S. Army Research Laboratory. The United States military is interested in facilitating Warfighter training by investigating large-scale realistic virtual operational environments. In order to support expanded training at higher echelons, virtual world simulators need to scale to support more simultaneous client connections, more intelligent agents, and more physics interactions. This work provides an in-depth analysis of a virtual world simulator under different hardware profiles to determine the effect of increased vertical computational scaling.
    more »
  • Merging OMG Standards in a General Modeling, Transformation, and Simulation Framework

    Research Article in EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 16(8): e2

    Vitali Schneider, Anna Yupatova, Winfried Dulz, Reinhard German

    Abstract
    Test-driven Agile Simulation (TAS) is a general-purpose approach that combines model-driven engineering, simulation, and testing techniques to improve overall quality for the development process. TAS…Test-driven Agile Simulation (TAS) is a general-purpose approach that combines model-driven engineering, simulation, and testing techniques to improve overall quality for the development process. TAS focuses on the construction of system and test specification models that are conform to the standards provided by the Object Management Group (OMG). Specifically, this approach aims at the detection of design errors by simulating the specified system and executing test cases as soon as possible at an early modeling level. In order to facilitate the development process we propose SimTAny: a versatile framework that enables seamless modeling, simulation, and testing of model specifications. The framework combines appropriate tools and software components within an integrated environment based on service-oriented architecture (SOA) and Eclipse RCP. The TAS approach as well as the SimTAny framework rely on various OMG standards and widely accepted tools. In particular, a combination of the UML and several standardized extension profiles namely SysML, MARTE, and UTP enables the development of high-quality software products based on a standard conform tool chain. The framework provides, among others, a MOFM2T standard conform model-to-text transformation component in order to generate executable simulation code for the simulation engine OMNeT++. In this paper we introduce the main features of the SimTAny framework with a special focus on the utilized OMG standards.
    more »
  • TiPeNeSS: A Timed Petri Net Simulator Software with Generally Distributed Firing Delays

    Research Article in EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 16(8): e3

    Ádám Horváth, András Molnár

    Abstract
    Performance analysis can be carried out in several ways, especially in case of Markovian models. In order to interpret high level of abstraction, we often use modeling tools like timed Petri nets (TP…Performance analysis can be carried out in several ways, especially in case of Markovian models. In order to interpret high level of abstraction, we often use modeling tools like timed Petri nets (TPNs). Although some subclasses of TPNs (e.g. stochastic Petri nets (SPNs) [17, 19]) can
    be handled analytically, a general timed Petri net is hard to evaluate via numerical analysis. However, the simulation of SPNs or deterministic and stochastic Petri nets (DSPNs) [16] are supported by many known tools (see, e.g. [3, 20]), it is hard to find a simulation tool for timed Petri nets with generally distributed (i.e., particular but arbitrarily chosen) firing times.
    In this paper, we present TiPeNeSS (Timed Petri Net Simulator Software) which supports the simulation of timed Petri nets containing transitions with generally distributed firing delays. The input of the software (the Petri net and the parameters) is defined in an XML file, what allows us to generate results in batch mode. Besides, we describe a case study in which we optimize the frequency of the regular maintenance in a manufacturing process.
    more »
  • Parallel Simulation of Queueing Petri Nets

    Research Article in EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 16(8): e4

    Jürgen Walter, Simon Spinner, Samuel Kounev

    Abstract
    Queueing Petri Nets (QPNs) are a powerful formalism to model the performance of software systems. Such models can be solved using analytical or simulation techniques. Analytical techniques suffer f…Queueing Petri Nets (QPNs) are a powerful formalism to model the performance of software systems. Such models
    can be solved using analytical or simulation techniques.
    Analytical techniques suffer from scalability issues, whereas
    simulation techniques often require very long simulation runs.
    Existing simulation techniques for QPNs are strictly sequential
    and cannot exploit the parallelism provided by modern
    multi-core processors. In this paper, we present an approach
    to parallel discrete-event simulation of QPNs using a conservative
    synchronization algorithm. We consider the spatial
    decomposition of QPNs as well as the lookahead calculation
    for different scheduling strategies. Additionally,
    we propose techniques to reduce the synchronization overhead
    when simulating performance models describing systems
    with open workloads. The approach is evaluated in
    three case studies using performance models of real-world
    software systems. We observe speedups between 1.9 and
    2.5 for these case studies. We also assessed the maximum
    speedup that can be achieved with our approach using synthetic
    models.
    more »
  • Hardware-software co-simulation for medical X-ray control units

    Research Article in EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 16(8): e5

    Bruno Kleinert, Gholam Reza Rahimi, Marc Reichenbach, Dietmar Fey

    Abstract
    In this paper we present our solution to master the complex- ity of product adaption cycles of a medical X-ray control unit. We present the real hardware and software platform and our mapping of i…In this paper we present our solution to master the complex-
    ity of product adaption cycles of a medical X-ray control
    unit. We present the real hardware and software platform
    and our mapping of it to a virtual X-ray control unit, im-
    plemented as our hardware-software co-simulation. To re-
    duce complexity for hardware developers, we developed our
    own XML-based abstract system description language which
    is mapped onto instantiations of parameterizable SystemC
    template modules. We verified the correctness of our virtual
    X-ray control unit by co-simulating unmodified software to
    hardware components, which we implemented in our system
    description language from the specification of the real sys-
    tem. Due to reduced complexity of our virtual X-ray control
    unit, it can be used as a time and cost saving test platform
    for future hardware and software adaption cycles.
    more »
IST
Contact Us