Advances on Deep Learning in Cyber Physical Systems (DL-CPS)
A special issue of Future Internet (ISSN 1999-5903).
Deadline for manuscript submissions: closed (10 December 2019) | Viewed by 441
Interests: human-computer interaction; accesibility; aging and computational interaction
Interests: cybersecurity; computer networks; wireless networks; information-centric networking and software-defined networking; machine intelligence
Special Issues, Collections and Topics in MDPI journals
Cyber-physical systems (CPS) comprise software components and physical objects that are deeply intertwined, each operating on different spatial and temporal scales, exhibiting multiple behavioural modalities and interacting with both each other and with users. They can interact with data and access services using a myriad of methods that change with their context of use. Smart grids, global environmental and disaster monitoring systems, medical and homeland security systems, and autonomous transportation and automatic pilot avionics are the main applications of cyber-physical systems.
In recent years, deep learning approaches have emerged as powerful computational models, and have shown significant success for dealing with a massive amount of data in unsupervised settings. Deep learning is revolutionizing, because it offers an effective way of learning representation and allows the system to learn features automatically from data, without the need for explicitly designing them. With the emerging technologies on the CPS infrastructure, specifically, Internet of things, wearable devices, cloud computing, and data analytics, there is potential for acquiring and processing a tremendous amount of data from the physical world. Promising computing paradigms and advanced technologies (e.g., smart home or city) relating to context awareness systems, activity recognition, distributed smart sensing, heterogeneous big data analytics, deep learning, and so on, have been increasingly developed and integrated into this CPS system, in order to make it a reality.
This Special Issue solicits contributions from the field of CPS data analytics using deep learning. Each submitted paper should cover the solutions with the state-of-the-art and novel approaches for the CPS problems and challenges in deep learning perspectives. Topics to be discussed in this workshop include, but not are limited to, the following:
- Introduction to deep learning and CPS
- Recent trends and advances in DL-based CPS
- Novel network structures and system design for DL enabled CPS
- Deep learning architecture for CPS security
- Deep learning experiments, test-beds, and prototyping systems for CPS security
- Context-aware deep learning-based approaches for CPS
- Data confidentiality, trust, and privacy in DL enabled CPS applications
- Analysis of network dynamics in Internet of things
- Authentication and access control for data usage in Internet of things
- Data mining and statistical modelling for the secure CPS
- User interface design and implementation for DL enabled CPS
- User interface design and evaluation for DL enabled CPS
- IoT/big data visualization techniques and application of DL
- Activity recognition in a smart home/city using deep learning
Dr. Sayan Sarcar
Dr. Uttam Ghosh
Manuscript Submission Information
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