Advances on Deep Learning in Cyber Physical Systems (DL-CPS)

Dear Colleagues,

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
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.