Call for Papers on Digital Twin Empowered Internet of Intelligent Things for Engineering Cyber-Physical Human Systems

2022-01-03

Theme
Future engineering systems will rely heavily on Cyber-Physical Human Systems (CPHSs) to design and build new capabilities that exceed today's levels of autonomy, flexibility, affordability, durability, and cyber security. A digital twin is a digitized representation of a physical object or action connected to the Internet of Things (IoT) in real-time. It can be used for multiple things such as monitoring, diagnosing, optimizing, and controlling asset performance and utilization. Recent breakthroughs in healthcare are increasingly reliant on medical devices and systems networked via the Internet of Intelligent Things (IoIT ) and must address the needs of patients in unusual circumstances.
As a result, dynamically reconfigured intelligent patient interaction complex IoIT will be required. There are numerous challenges in cyber-physical human systems when integrated with IoT. Appropriate digital twin configuration through advanced multi-sensors can be used to overcome the limitations in the CPHSs. Similarly, the design and management of smart renewable energy systems and power distribution using smart grids can be optimized using IoIT.   To produce high-performance, dependable, and low-cost CPHSs that serve humanity, several key problems must be answered.
CPHSs become more complicated, capable, and prevalent in society as human health, safety, and welfare have to be protected. Misuse of automation as a result of a lack or excess of human trust is another major challenge. Many human behavior limits such as bias, cognitive exhaustion, and computational constraints have to be foreseen, modeled, and exploited in the design of CPHSs. Different sensors, sensing techniques, and algorithms that can accurately track and quantify human body movements and emotions have to be properly integrated with IoIT. This special issue intends to offer a detailed view of the emerging field of  IoIT in engineering CPHSs. Relatively new, innovative, and unpublished researches that focus on intelligent IoT for CPHS applications are welcomed.
Topics of interest include, but are not limited to, the following areas:
•    Design and challenges in building novel cyber medical systems
•    CPHSs in green energy grids
•    New architectures and models for cognitive CPHSs
•    IoT and connected devices in CPHSs
•    Computer-aided detection and diagnosis
•    Advance unmanned autonomous systems using multi-sensors
•    Precision agriculture and water management
•    Security and privacy issues in AI-based healthcare application
•    Intelligent sensors in human life development
•    Machine learning algorithms for building CPHS
•    Cyber cities and smart cities with swarm optimization
•    Sustainable high-performance computing methods
•    Cyber-physical human system for medical informatics
•    Wearables devices for remote healthcare
•    Data mining and big data analytics for IoT


Manuscript preparation and submission
Computer Assisted Methods in Engineering and Science (CAMES) is a refereed international journal published quarterly and indexed by Scopus and EBSCO databases.
All manuscripts must be submitted through the journal website: https://cames.ippt.pan.pl/index.php/cames/about/submissions
Submission must be clearly marked in the system as “Digital Twin Empowered IoIT for Engineering Cyber-Physical Human Systems”. The publication in this Special Issue is free of charge for the Authors, and the published manuscript will be freely available for the Readers through the Journal website (https://cames.ippt.pan.pl).

Important dates:
Deadline for full-length paper submission: May 30, 2022
Notification of the reviewers’ first feedback: June 30, 2022
Deadline for the revised version submission: July 30, 2022
Announcement of acceptance by Guest Editors: August 30, 2022
Deadline for the final manuscript submission: September 30, 2022

Guest Editors:
Dr. C. Venkatesan (Leading Guest Editor), Professor, Department of Electronics and Communication Engineering, HKBK College of Engineering, Bangalore, India.
Dr. Yu-Dong (Eugene) Zhang, Professor, Chair in Knowledge Discovery and Machine Learning, Department of Informatics, University of Leicester, UK
Dr. Prof. Qin Xin, Full Professor of Computer Science and Faculty Research Leader, Faculty of Science and Technology, University of the Faroe Islands, Torshavn, Faroe Islands

Editor-in-Chief
Prof. Michał Kleiber – Past ECCOMAS President, Institute of Fundamental Technological Research, Warsaw, Poland
Co-Editor-in-Chief
Prof. Tadeusz Burczyński – Chairman of TC on Computational Solids & Structural Mechanics of ECCOMAS, Director of the Institute of Fundamental Technological Research, Warsaw, Poland