Mobile Networks and Applications

, Volume 22, Issue 6, pp 1157–1158 | Cite as

Cloud-Assisted Cyber-Physical Systems for the Implementation of Industry 4.0

  • Jiafu WanEmail author
  • Min Xia

Guest Editorial: In recent years, modern industry has been struggling against personalized consumption demands which feature multiple types, small batches, and random orders. The promising solution relies on Cloud-assisted Cyber-Physical Systems (CCPS) that addresses the integration of virtual information systems with physical devices. When combining the big data, cloud computing, internet of things, and even artificial intelligence with industrial automation, we may achieve a flexible, efficient, and transparent industry system. Since German government released the Industry 4.0 initiative, three kinds of integration with the support of cloud technologies have been widely discussed, i.e., 1) horizontal integration through value networks; 2) vertical integration and networked industrial systems; and 3) end-to-end digital integration of engineering across the entire value chain. However, many difficulties exist in integration, e.g., virtualized resource management, high-bandwidth real-time industrial wireless networks, industrial big data analytics, dynamical reconfiguration mechanics, and unified network standards. Therefore, industrial and academic researchers should cooperate to promote the progress of smart industrial technologies and applications. This special issue features six selected papers with high quality related to CCPS for the implementation of industry 4.0.

This special issue kicks off with an article on smart home system, namely “Smart Home 2.0: Innovative Smart Home System Powered by Botanical IoT and Emotion Detection,” co-authored by M. Chen, et al. The authors propose an innovative smart home solution, in which users interconnect with home appliances and greeneries harmoniously, to achieve the organic integration between users and greeneries.

The second article “A Lightweight Intelligent Manufacturing System Based on Cloud Computing for Plate Production” by Q. Liu et al. proposes a flexible Lightweight Plate Intelligent Manufacturing System (LPIMS) based on cloud computing and assembly manufacturing process for industry 4.0. The framework structure and functions is described, a real-time manufacturing information model of the LPIMS to meet the needs of large-scale information processing requirements is given, and a key concept for the system, i.e. the optimal state is defined in this paper.

In the article “TempoRec: Temporal-Topic Based Recommender for Social Network Services”, Y. Zhang et al. propose a hybrid recommendation algorithm based on social relations and time-sequenced topics, which has been evaluated through datasets from Sina Weibo that the improved hybrid recommendation algorithm achieves better mean average precision (MAP) than other related approaches.

The fourth article “Exploiting Energy Efficient Emotion-Aware Mobile Computing”, co-authored by Y. Peng, et al., proposes a framework of energy efficient emotion-aware mobile computing system to consider the energy saving from both local user part and remote data centers part, and provide energy saving while keeping quality of service.

The fifth paper “Underwater Optical Image Processing: A Comprehensive Review” by H. Lu, et al., introduces a comprehensive review of recent trends of underwater optical image processing technologies, including underwater image restoration, underwater image enhancement, underwater image quality assessment.

In the sixth paper “Cloud-Assisted Mobile Crowd Sensing for Traffic Congestion Control”, H. Yan, et al. propose a cloud-assisted MCS architecture for urban transportation system. The authors make the case for cloud-assisted MCS traffic congestion control by sensing data obtained continuously from a large set of smartphones carried by drivers. In this case, a Mechanism of more Contributions and more Feedback Services (MCFS) to recruit, engage, and retain the participants is considered.



The guest editors are thankful to our reviewers for their effort in reviewing the manuscripts. We also thank the Editor-in-Chief, Dr. Imrich Chlamtac for his supportive guidance during the entire process.

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Mechanical & Automotive EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.University of British ColumbiaVancouverCanada

Personalised recommendations