Self-configuration in humanized Cyber-Physical Systems

Most works on Cyber-Physical Systems (CPS) are based on classic hardware infrastructures made of sensors, actuators and processing devices. Usual self-configuration technologies, then, do not allow humans to be integrated in CPS as service providers. Therefore, in this work we propose a new self-configuration technology for humanized CPS. The proposed technology uses simple binary and mathematical operations in order to reduce the convergence time, improve the scalability and address the dynamism introduced by humans into CPS. Besides, a human-oriented quality-of-service algorithm based on the Maslow pyramid is also introduced. Moreover, an experimental validation is conducted in order to validate the proposed solution as a useful and scalable self-configuration technology for humanized Cyber-Physical Systems.

[1]  A. Knoll,et al.  Towards adaptable manufacturing systems , 2013, 2013 IEEE International Conference on Industrial Technology (ICIT).

[2]  Jean-Louis Millot,et al.  Effects of ambient odors on reaction time in humans , 2002, Neuroscience Letters.

[3]  Ramón Alcarria,et al.  TF4SM: A Framework for Developing Traceability Solutions in Small Manufacturing Companies , 2015, Sensors.

[4]  Uwe Mönks,et al.  Assisting the Design of Sensor and Information Fusion Systems , 2014 .

[5]  Nelson Souto Rosa,et al.  An Energy-Aware Middleware for Integrating Wireless Sensor Networks and the Internet , 2011, Int. J. Distributed Sens. Networks.

[6]  A. Maslow A Theory of Human Motivation , 1943 .

[7]  Tharam S. Dillon,et al.  Web‐of‐things framework for cyber–physical systems , 2011, Concurr. Comput. Pract. Exp..

[8]  Jay Lee,et al.  A Cyber Physical Interface for Automation Systems—Methodology and Examples , 2015 .

[9]  Alex Talevski,et al.  Cyber-physical systems: Providing Quality of Service (QoS) in a heterogeneous systems-of-systems environment , 2011, 5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011).

[10]  Deniz Erdogmus,et al.  The Future of Human-in-the-Loop Cyber-Physical Systems , 2013, Computer.

[11]  T. Kamarck,et al.  A global measure of perceived stress. , 1983, Journal of health and social behavior.

[12]  Kuldeep Kumar,et al.  User cube: a taxonomy of end users , 1989, CACM.

[13]  S. Shankar Sastry,et al.  Secure Control: Towards Survivable Cyber-Physical Systems , 2008, 2008 The 28th International Conference on Distributed Computing Systems Workshops.

[14]  Bradley R. Schmerl,et al.  Software Engineering for Self-Adaptive Systems: A Second Research Roadmap , 2010, Software Engineering for Self-Adaptive Systems.

[15]  F. Jaroszyk,et al.  The effect of stimulus intensity on force output in simple reaction time task in humans. , 1995, Acta neurobiologiae experimentalis.

[16]  Tao Wang,et al.  A Two-Phase Context-Sensitive Service Composition Method with the Workflow Model in Cyber-Physical Systems , 2014, 2014 IEEE 17th International Conference on Computational Science and Engineering.

[17]  Ramón Alcarria,et al.  Building Smart Adaptable Cyber-Physical Systems: Definitions, Classification and Elements , 2015, UCAmI.

[18]  Stamatis Karnouskos,et al.  Factory of the Future: A Service-oriented System of Modular, Dynamic Reconfigurable and Collaborative Systems , 2010 .

[19]  Hossein Shayeghi,et al.  PID Type Stabilizer Design for Multi Machine Power System Using IPSO Procedure , 2012 .

[20]  Jeongmin Park,et al.  Designing Goal Model for Autonomic Control Point of Cyber-Physical Systems (CPS) , 2015 .