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Sensors 2016, 16(9), 1542; doi:10.3390/s16091542

Markov Task Network: A Framework for Service Composition under Uncertainty in Cyber-Physical Systems

1
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
School of Engineering, University for Development Studies, Tamale 00233, Northern Region, Ghana
*
Author to whom correspondence should be addressed.
Academic Editor: Albert M. K. Cheng
Received: 21 June 2016 / Revised: 9 September 2016 / Accepted: 13 September 2016 / Published: 21 September 2016
(This article belongs to the Special Issue Real-Time and Cyber-Physical Systems)
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Abstract

In novel collaborative systems, cooperative entities collaborate services to achieve local and global objectives. With the growing pervasiveness of cyber-physical systems, however, such collaboration is hampered by differences in the operations of the cyber and physical objects, and the need for the dynamic formation of collaborative functionality given high-level system goals has become practical. In this paper, we propose a cross-layer automation and management model for cyber-physical systems. This models the dynamic formation of collaborative services pursuing laid-down system goals as an ontology-oriented hierarchical task network. Ontological intelligence provides the semantic technology of this model, and through semantic reasoning, primitive tasks can be dynamically composed from high-level system goals. In dealing with uncertainty, we further propose a novel bridge between hierarchical task networks and Markov logic networks, called the Markov task network. This leverages the efficient inference algorithms of Markov logic networks to reduce both computational and inferential loads in task decomposition. From the results of our experiments, high-precision service composition under uncertainty can be achieved using this approach. View Full-Text
Keywords: cyber-physical systems; Markov logic networks; hierarchical task networks; ontology; uncertainty reasoning cyber-physical systems; Markov logic networks; hierarchical task networks; ontology; uncertainty reasoning
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MDPI and ACS Style

Mohammed, A.-W.; Xu, Y.; Hu, H.; Agyemang, B. Markov Task Network: A Framework for Service Composition under Uncertainty in Cyber-Physical Systems. Sensors 2016, 16, 1542.

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