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Cyber-physical systems research to date has focused on the development of synergy and tight coupling of the physical and computational processes, vis-a-vis, the control of the system. However, this tight coupling is also enabling the accumulation of large amounts of data, which can be analyzed, interpreted, and appropriately leveraged. When multiple systems are interacting with each other, and closed-loop control is implemented, real-time analysis of the large amount of cross-device data becomes a critical requirement. As we evolve towards the Internet of Things, we see the deployment of multitude of wireless sensors and agents spanning many application domains including: environmental, healthcare, avionics components in the latest commercial airplane, smart interconnected automobiles and trucks, and smart buildings. These produce massive amounts of multisystem data that need to be sifted through to facilitate reasonably accurate decision-making and control.Cyber-physical systems research to date has focused on the development of synergy and tight coupling of the physical and computational processes, vis-a-vis, the control of the system. However, this tight coupling is also enabling the accumulation of large amounts of data, which can be analyzed, interpreted, and appropriately leveraged. When multiple systems are interacting with each other, and closed-loop control is implemented, real-time analysis of the large amount of cross-device data becomes a critical requirement. As we evolve towards the Internet of Things, we see the deployment of a multitude of wireless sensors and agents spanning many application domains including: environmental, healthcare, avionics components in the latest commercial airplane, smart interconnected automobiles and trucks, and smart buildings. These produce massive amounts of multi-system data that need to be sifted through to facilitate reasonably accurate decision-making and control. We are looking to convene the community for a workshop to explore challenges and opportunities in moving from IoT to real-time control and CPS. This workshop focuses on the current state of big data real time analytics in CPS (in medical, transportation (automotive, aerospace, rail), energy and other fields), evaluates the promises and shortcomings, and evaluates what needs to be done to enable big data real-time analytics in closed-loop cyber-physical systems.

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重要日期

2015-03-27
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征稿范围

Topics of discussion include: * As we move from IoT sensing to real-time applications, the need for dependability and security emerges, what technologies and research are essential? What are the challenges? How do approaches scale? * How do you integrate IoT and big data into cloud for CPS and get real-time control? * Machine learning and other approaches for real time data analytics in closed-loop CPS * Physical/virtual testbeds for real-time closed-loop control * Real-time meanings in various application spaces: aeronautics, medical, transportation, energy * Opportunities for closing the loop at (or near) real-time in "smart city" cyber-physical systems * Multisystem data analytics: how do we ensure data from multiple systems are input correctly and at right times? * Integrating provenance into IoT and CPS. * Real-time sense making and decision making with big data including (how does the system work when some components are providing data at different rates, and / or are off the grid)? * Human-in-the-loop (behavioral aspects of data analytics) * Moving from IoT (sensing and agent end of CPS) to CPS (real-time control through big data analytics)
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重要日期
  • 04月13日

    2015

    会议日期

  • 03月27日 2015

    摘要截稿日期

  • 04月13日 2015

    注册截止日期

主办单位
IEEE Computer Society
Association for Computing Machinery Special Interest Group on Embedded Systems
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