Special Issue "Cognitive Computing with Big Data System over Secure Internet of Things "

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 January 2020.

Special Issue Editors

Guest Editor
Dr. Xiaochun Cheng

Department of Computer Science, Middlesex University, London, UK
Website | E-Mail
Interests: AI computing (decision support, reasoning, pattern recognition, machine learning, deep learning, optimization) and security (intrusion detection, malware detection, spam detection, fraud detection, security protocol verification, biometrics, crime intelligence analysis, cryptography, water marking, data origin provenance and tracing)
Guest Editor
Prof. Ding-Zhu Du

Department of Computer Science, University of Texa at Dallas, 800 W. Campbell Road; MS EC31, Richardson, TX 75080, USA
Website | E-Mail
Interests: greedy Approximation with nonsubmodular potential function; nonlinear combinatorial optimization; linear programming and approximation algorithms; Internet of Things; wireless sensor networks
Guest Editor
Prof. Arun Kumar Sangaiah

School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India
Website | E-Mail
Interests: Internet of Things; big data; machine learning
Guest Editor
Dr. Rongxing Lu

Faculty of Computer Science, University of New Brunswick, Canada
Website | E-Mail
Interests: wireless network security; cloud and fog computing security; big data security and privacy; secure and privacy in IoT; secure opportunistic computing; secure social network; applied cryptography

Special Issue Information

Dear Colleagues,

Cognitive computing has broad horizons, which cover different characteristics of cognitive activities. The field is highly transdisciplinary in nature, combining principles, methods, and/or technologies from multiple subjects’ areas, such as: Psychology, computer science, artificial intelligence, computer network communication, linguistics, philosophy, neuroscience, etc.

The Internet of Things (IoT) has become a key component for intelligent systems, such as medical systems, intelligent vehicular networks, intelligent building, or smart cities. Low-cost sensing and actuation are available in IoT applications. They enable seamless information exchange and networked interactions of physical and digital objects, such as in personalized human healthcare. This interconnectivity together with large-scale data processing, advanced machine learning, robotics, and new electronic techniques steadily brings innovation and business models of the digital space into the physical world. Secure IoT systems are expected to improve the intelligence of the systems, to improve the interaction between the human and the environment, to enhance reliability, resilience, and agile access control, to improve operational efficiency and energy efficiency, and to optimize resource utilization. Many of the IoT systems and technologies are relatively novel; there are still many untapped applications areas, and numerous challenges and issues that need to be researched further for.

This Special Issue aims for data analysis, knowledge extraction, and decision support solutions based on data technologies and cognitive methods over the secure Internet of Things. This would extend tradition data technologies by incorporating knowledge from domain experts as well as the latest artificial intelligence solutions, such as how to perform medical decision support with the healthcare knowledge and patient data collected by secure IoT systems. The main focus is on research on the latest cognitive computing embedded data technologies to process and to analyse the large amount of data collected through secure IoT systems, and to help human expert decision-making, such as in health care services. Cognitive computing supported data processing facilitates a platform for the scientific community to work for the latest solutions for challenges related to secure IoT application towards smart infrastructure, such as to meet the real-world requirement of healthcare service. We cordially invite investigators to contribute their original research articles, with an emphasis on real-life applications, as well as review articles that will stimulate further activities in this area and improve our understanding of the key scientific problems.

The scope includes (but is not limited to) the following:

  • Cognitive computing models and prediction analytics (such as for e-health);
  • Cognitive semantic collective intelligence (such as in medical applications);
  • Cognitive computing algorithms (such as for smart healthcare systems);
  • Cognitive design principles and best practices for IoT application development (such as for human health services);
  • Cognitive reasoning about IoT smart objects (such as for health care);
  • Cognitive models for big data systems, theory, and applications (such as in e-health);
  • Cognitive data models (such as for telemedicine);
  • Edge/fog/IoT for mobile/wireless/pervasive/proactive/personalized service (such as healthcare);
  • IoT sensors data management;
  • IoT data mining and analytics (such as for smart medical devices).

Dr. Xiaochun Cheng
Prof. Ding-Zhu Du
Prof. Arun Kumar Sangaiah
Prof. Rongxing Lu
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 papers will be 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1500 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.


  • Cognitive Computing
  • Big Data
  • Security
  • Internet of Things

Published Papers (1 paper)

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Open AccessArticle
Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
Appl. Sci. 2019, 9(14), 2910; https://doi.org/10.3390/app9142910
Received: 25 June 2019 / Revised: 11 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
PDF Full-text (6359 KB) | HTML Full-text | XML Full-text
To combine homomorphic public key encryption with reversible data hiding, a reversible data hiding scheme in homomorphic encrypted image based on EC-EG is proposed. Firstly, the cover image is segmented. The square grid pixel group randomly selected by the image owner has one [...] Read more.
To combine homomorphic public key encryption with reversible data hiding, a reversible data hiding scheme in homomorphic encrypted image based on EC-EG is proposed. Firstly, the cover image is segmented. The square grid pixel group randomly selected by the image owner has one reference pixel and eight target pixels. The n least significant bits (LSBs) of the reference pixel and all bits of target pixel are self-embedded into other parts of the image by a method of predictive error expansion (PEE). To avoid overflowing when embedding data, the n LSBs of the reference pixel are reset to zero before encryption. Then, the pixel values of the image are encrypted after being encoded onto the points of the elliptic curve. The encrypted reference pixel replaces the encrypted target pixels surrounding it, thereby constructing the mirroring central ciphertext (MCC). In a set of MCC, the data hider embeds the encrypted additional data into the n LSBs of the target pixels by homomorphic addition in ciphertexts, while the reference pixel remains unchanged. The receiver can directly extract additional data by homomorphic subtraction in ciphertexts between the target pixels and the corresponding reference pixel; extract the additional data by subtraction in plaintexts with the directly decrypted image; and restore the cover image without loss. The experimental results show that the proposed scheme has higher security than the similar algorithms, and the average embedding rate of the scheme is 0.25 bpp under the premise of ensuring the quality of the directly decrypted image. Full article

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