IEEE Transactions on Industrial Informatics

Calls for Papers in Special Sections

* Information Technology in Automation (due: March 31, 2017)

Information technologies play crucial role in the current and future developments of industrial automation. There are numerous strategic agendas on future manufacturing that have appeared recently worldwide and all of them emphasizes the role of information technologies in automation in shaping up the future of production industries. This special section is devoted to the latest research results in this arena focusing on (but not limited to) the following topics:

  • IT Modeling Techniques (e.g., Object-Orientation, Components, Agents, Services) for Automation Systems
  • Model-driven Development and Model Based Engineering in Automation and Mechatronic Systems (e.g., UML, SysML)
  • Data Modeling (e.g. CAEX, AutomationML, OPC UA, ISO 15926) along the Plant Life Cycle and according integration
    methods for engineering tools
  • Domain Specific Modeling and Programming Languages (e.g. IEC 61131, IEC 61499)
  • Knowledge-Based Systems, Ontologies and Semantic Web in Automation Systems
  • IT Architecture techniques (e.g. Virtualization, Cloud computing, Big Data) in Automation Systems
  • Vertical Integration, integration with MES and ERP Systems (Databases, Semantic Web Services)
  • Engineering Methods for Distributed Automation Systems, Industrial Internet of Things and Cyber-Physical Systems
  • Software Reuse, Software Product Lines, agile software development, and other software engineering methods in automation
  • Simulation-driven engineering, virtual commissioning, model-checking, runtime verification and other advanced testing methods for control software and/or automation systems
  • Dynamically Reconfigurable, Adaptive, and Emergent Automation Software/Systems
  • Security and Safety in Factory, Energy, Home and Building Automation
  • Interoperability solutions for Smart Manufacturing
  • Real-Time platforms for automation
  • Product Life-Cycle Management Systems
  • Case Studies and Application Reports especially from:
    o Digital Factory, Smart Manufacturing, Web-of-Things in the Factory Line
    o Home and Building Automation
    o Renewable Energy Systems and Smart Grids (Production and Integration).
  • Emerging topics in software and systems engineering in automation.

Source: http://tii.ieee-ies.org/ss17/CfP-Information%20Technology%20in%20Automation.pdf

* Engineering Industrial Data Analytics Platforms for Internet of Things (due: April 1, 2017)

Over the last few years, a large number of Internet of Things (IoT) solutions have come to the IoT marketplace. Typically, each of these IoT solutions is designed to perform a single or minimal number of tasks (primary usage). For example, a smart sprinkler may only be activated if the soil moisture level goes below a certain level in the garden. Further, smart plugs allow users to control electronic appliances (including legacy appliances) remotely or create automated schedules. Undoubtedly, such automation not only brings convenience to their owners but also reduces resource wastage. However, these IoT solutions act as independent systems. The data collected by each of these solutions is used by them and stored in access-controlled silos. After primary usage, data is either thrown away or locked down in independent data silos. We believe a significant amount of knowledge and insights are hidden in these data silos that can be used to improve our lives; such data includes our behaviours, habits, preferences, life patterns and resource consumption.

To discover such knowledge, we need to acquire and analyses this data together in a large scale. Typical data analytics approaches are expected to facilitate process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Large scale data analysis is the process of applying data analysis techniques to a large amount of data, typically in big data repositories. Such large scale analysis requires specialized algorithms, systems and processes to be developed in order to review, analyze and present information in a form that is more meaningful for organizations or end users. IoT middleware platforms have been developed in both academic and industrial settings in order to facilitate IoT data management tasks including data analytics. However, engineering these general purpose industrial-grade big data analytics platforms need to address many
challenges as listed below to be able to support data analytical needs in different types of IoT applications.

This Special Section is focused on consolidating research efforts that aim at engineering big data analytics platforms for Internet of Things paradigms. Topics include, but are not limited to, the following research topics and technologies:

  • Big data analytics, new algorithms and approaches
  • Privacy preserving data analysis
  • Big data for urban informatics
  • Internet of Things middleware platforms
  • Engineering IoT systems
  • Experience reports on software development challenges for the IoT and takeaways;
  • Software engineering challenges for mission-critical IoT systems;
  • high reactivity, scalability, heterogeneity, configurability, resource-constrained systems, and robustness;
  • Software methods and development techniques for the IoT
  • Industry grade tools, platforms, and environments for developing software for the IoT
  • Big data analytics software architectures
  • Developing reusable analytics tools and frameworks
  • Data analysis tools for developer community

Papers discussing new application areas and the resulting new developments data analytics in Internet of Tings platforms are especially welcome. Results obtained by simulations must be validated in bounds by experiments or analytical results.

Source: http://tii.ieee-ies.org/ss17/CfP-Engineering%20Industrial%20Big%20Data%20Analytics%20Platforms%20for%20Internet%20of%20Things.pdf

* Recent trends and developments in Industry 4.0 motivated robotic solutions (due: April 15, 2017)

The fourth industrial revolution, which becomes matter of fact in the recent and next years, is expected to deeply change the future manufacturing and production processes, and lead to Smart Factories that will benefit from the main design principles of Industry 4.0: interoperability, virtualization, decentralization, real-time capability, service orientation, modularity. Robotics will have a key role in this development since innovative technologies and solutions, traditionally associated with the service robotics sector, are going to migrate to industrial smarter robots, exploiting the maturing of sensing, mapping, localization, navigation, and motion control technologies. These smarter robots will draw on a much broader range of technology, allowing higher levels of dexterity and flexibility, the ability to learn tasks without formal programming, and to autonomously collaborate with other autonomous devices and human operators, thus reaching non-manufacturing industries and fields. A deeper attention will have to be paid on safety in dynamic and shared environments with the human-beings and to energy consumption. The development of robotic solutions for the Smart Factories of Industry 4.0 is already going on, taking advantage also from industry-academia collaboration. Aim of this Special Section is the illustration of trends and
advanced robotic solutions that can significantly contribute to the Smart Factories of Industry 4.0. Topics of interest include, but are not limited to the following ones:

  • Autonomous robotics and mobile robots applications in industrial environments
  • Sensor fusion and intelligent sensing for robotics applications in smart factories
  • Monitoring, fault detection and safety of robotic systems
  • Advanced robotic solutions for smart factories developed through industry-academia collaboration
  • Smart robotic applications in industrial complex situations and innovative application fields

All contributions must focus robotic industrial applications. Case studies and results experimentally validated in an industrial context are
especially welcome.

Source: http://tii.ieee-ies.org/ss17/CfP-V2-Recent%20trens%20anddevelopments%20in%20Industry%204.0motivatedroboticsolutions.pdf

 

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