Advanced Internet of Things for Smart Cyber-Physical Infrastructure Systems

Submission Deadline:  20 March 2021

IEEE Access invites manuscript submissions in the area of Advanced Internet of Things for Smart Cyber-Physical Infrastructure Systems.

The Internet of Things (IoT) is rapidly gaining ground as a priority, multidisciplinary research topic in many
academic and industrial disciplines, especially in Cyber-Physical infrastructures, such as renewable
energy generation, smart vehicle management, and water quality monitoring. Due to the rapid
proliferation of smart sensors and meters, wearable devices and smartphones, the Internet of Things-
enabled technology is evolving infrastructure from conventional operation and maintenance business
models to more efficient, sustainable, smart and resilient systems. The successful utilization of IoT
enabled technology in Cyber Physical Infrastructure Systems (CPIS) will enable them to be faster and
more proactive, have a lower overall cost, provide improved business practices and enhance
sustainability. Future IoT enabled infrastructures will be realized to provide timely information
communication and effective decision-making for intelligent society and industry.

There are already IoT smart applications used without human intervention in areas such as traffic
congestion monitoring, household waste recycling, water consumption monitoring and alert systems in
cities. People will play a major role as they generate and use the data coming from these devices. IoT
technology provides potential for optimal feedback and decisions on various infrastructure needs, where it
enables seamless alignments of the local providers with the global potential of wider communities. It
leads to new smart services and increases the efficiency for infrastructure.

However, empowering the utility of IoT enabled technology in Cyber-Physical infrastructure is still
significantly challenging, considering the shortage of cost-effective and accurate smart sensors and
meters, unstandardized IoT system architectures, heterogeneity of connected wearable devices,
multidimensionality and high volume of data generated, and the high demand for interoperability.

From a
user-centric perspective, the successful use of IoT in infrastructure will also need an interoperable IoT
environment for delivery and research, tightly-coupled data mining applications, adequate data and
knowledge standards of self-empowerment, and a sound decision-making foundation. Research will focus
on designing and developing more connected complex systems, dealing with the current lack of
standards, and figuring out ways to analyze the deluge of data. Complexity will be an important factor to
study and control: The behavior of every single node in IoT will need to be considered to determine its
potential impact on the whole CPIS. These challenges and needs provide a lot of opportunities to explore
and investigate new concepts, algorithms and applications in IoT enabled smart infrastructure.

The goal of this Special Section on Internet of Things for smart CPIS is to bring together researchers and
practitioners from both academia and industry into a forum, and to show the state-of-the-art research and applications in utilizing IoT enabled technology for cyber-physical infrastructure, by presenting efficient
scientific and engineering solutions, addressing the needs and challenges for integration with new
technologies, and providing visions for future research and development.

The central theme of the proposed Special Section is on advanced internet of things technologies for
smart cyber-physical infrastructure systems, where smart sensing technologies, IoT architectures,
services, applications, and data analytics for infrastructure applications are the focus areas, and broad
aspects and issues will be well discussed.

This topic should be of great interest to IEEE Access readers.
The theme of the Special Section (SI) is especially focused on the three major aspects of IoT for
infrastructure: (1) intelligent monitoring with increased security and validity in CPS by using a variety of
IoT assets or technologies, including sensors, devices and mobile applications, (2) Interoperability and
data sharing services across IoT infrastructures supporting heterogeneous elements to cooperate
seamlessly to share information, and (3) Creation of ecosystems of “Platforms for Connected Smart
Objects”; integrating the future generations of smart devices (i.e. sensors) and network technologies and
other evolving ICT advances.

The Special Section aims to provide a forum for experts to disseminate their recent advances and views
on future perspectives in the field. The Special Section aims to publish original, significant and visionary
articles which present ideas, innovations, and applications of utilizing IoT enabled technology for
improving the efficiency, sustainability and reliability of infrastructure.

The topics of interest include, but are not limited to:

  • Intelligent sensing and monitoring techniques and applications for CPIS
  • Challenges and issues of intelligent sensing and data collection techniques in CPIS
  • Integrated communication and distributed computing design for CPIS
  • Internet of Things architecture design for CPIS
  • Theoretical computing foundation and models for CPIS
  • Intelligent real time data analytics for CPIS
  • Security and privacy issues in the distributed computing infrastructure of CPIS
  • Machine learning techniques for CPIS
  • Co-design of distributed computing and physical systems in CPIS
  • Large-scale data analysis in CPIS
  • Scalable data and resource management in CPIS
  • Robotics and autonomous systems for CPS
  • Human in the loop robotics and interaction tech for CPS
  • Innovative and cutting-edge technologies for CPIS
  • Applications of Cyber-physical system

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.


 Associate Editor:  Po Yang, Sheffield University, UK

 Guest Editors:

    1. Wenyan Wu, Birmingham City University, UK
    2. Zofia Lukzos, Delft University of Technology, Netherlands
    3. Zahid Akhtar, State University of New York Polytechnic Institute, USA


Relevant IEEE Access Special Sections:

  1. Big Data Technology and Applications in Intelligent Transportation
  2. Innovation and Application of Internet of Things and Emerging Technologies in Smart Sensing
  3. Emerging Trends, Issues and Challenges for Array Signal Processing and Its Applications in Smart City


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

 Article submission: Contact Associate Editor and submit manuscript to:

 For inquiries regarding this Special Section, please contact:

Data Mining and Granular Computing in Big Data and Knowledge Processing

Submission Deadline: 30 September 2018

IEEE Access invites manuscript submissions in the area of  Data Mining and Granular Computing in Big Data and Knowledge Processing.

Researchers continue to encounter an explosive growth in big data with high volume, variety, velocity, veracity and value. The “Five Vs” are the key features of big data, and also the causes of inherent uncertainties in the representation, processing, and analysis of big data. Data is continuously recorded in a fast sampling rate and is leading to an explosion of data volume which calls for a specific strategy to increase scalability of a computational method. Installation of various sensors make it possible for numerous variables to be captured. This issue not only results in exponential growth of data volume but also generates heterogeneous data samples of various types: images, videos, texts, sounds, etc. The benefits from the management of big data are clear: the larger the data, the higher the degree of knowledge that can be extracted from it. Therefore, data mining becomes an essential technique to process big data. Moreover, real-life big data is now available everywhere from the Internet, sensor networks, social networks, and proprietary databases. The big data mining remains an open issue for both academia and practitioners because of the issue of uncertainty caused by inaccurate measurement, faulty sensors, missing values, etc.

In the past few years, a great number of challenging problems have emerged, such as the problem of imbalanced data, multi-label and multi-instance problems, low quality and/or noisy data or semi-supervised learning, among others. In the realm of big data itself, research on big data processing still attracts a growing research interest where the main objective is to set up a scalable big data processing environment which allows one to perform efficient data collection, storage and analytics in an integrated manner.  Parallelization is among the most widely applied technique to handle big data. Instead of relying on a single node, it utilizes a distributed computational framework which makes possible for information to be segregated into a number of computational nodes and to be executed in parallel, thereby increasing scalability of big data processing and memory management of big data.  The Map Reduce, Apache Spark, Flink, etc. are some popular examples of big data analytics which adopt a parallelization scheme.

In the recent past, the evolution of research interest has focused on a relatively new area—granular computing (GrC), based on such technologies as fuzzy sets and rough sets. GrC provides a powerful tool for multiple granularity and multiple-view data analysis. It offers a promising solution to cope with the uncertainty of big data which often contains a significant amount of unstructured, uncertain and imprecise data. GrC can exhibit better capability and advantages in intelligent data analysis, pattern recognition, machine learning and uncertain reasoning for a noticeable amount of data. GrC aims to find a suitable level of granularity of given problems which can be adjusted according to the degree of fuzziness of the given problem. It refers to those advantages, and also challenges, derived from collecting and processing vast amounts of data. There are new challenges regarding the scalability of GrC when addressing very big data.

The exploration of data mining and granular computing in big data and knowledge processing is a multidisciplinary field, which crosses multiple research disciplines and industry domains, including transportation, communications, social network, medical health, and so on.

The goal of this Special Section in IEEE Access is to provide a specific opportunity to review the state-of-the-art of recent data mining and granular computing in big data and knowledge processing, and bringing together researchers in the relevant areas to discuss the latest progress, new research methodologies and potential research topics.

The topics of interest include, but are not limited to:

  • Latest classification algorithms and clustering algorithms for big data processing
  • Supervised/semi-supervised learning method for big data
  • Feature selection/extraction/construction/recognition for big data
  • Data streams and concept drift
  • Data mining in evolutionary computation for real-world applications
  • Large-scale biomedical image mining, assessment and analysis
  • Social network analysis and mining for big data
  • Multi-label/Multi-instance learning in big data and knowledge processing
  • Deep learning and transfer learning for big data analysis
  • Structured spare representation for large-scale image classification
  • Granular soft computing techniques for big data
  • Granular computing theory and application in big data
  • Granular data mining algorithm and application in big data
  • Granular data mining model based MapReduce/Apache Spark
  • Fuzzy granular support vector machines and application in big data
  • Big data analysis for decision-making
  • Multi-criteria knowledge-based systems
  • Evolutionary computation and hybrid systems in big data
  • Cooperation co-evolution for big data
  • Large-scale image and multimedia processing
  • Intelligent adaptive control and analysis in big data applications
  • Multi-agent systems and distributed control of big data
  • Application of data processing technology in large-scale medicine and healthcare data

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.


Associate Editor: Weiping Ding, Nantong University, China

Guest Editors:

  1. Gary G. Yen, Oklahoma State University, USA
  2. Gleb Beliakov, Deakin University, Australia
  3. Isaac Triguero, University of Nottingham, United Kingdom
  4. Mahardhika Pratama, Nanyang Technological University, Singapore
  5. Xiangliang Zhang, King Abdullah University of Science and Technology, Saudi Arabia
  6. Hongjun Li, Nantong University, China

Relevant IEEE Access Special Sections:


  1. Recent Computational Methods in Knowledge Engineering and Intelligence Computation
  2. Advanced Big Data Analysis for Vehicular Social Networks
  3. Big Data Analytics in Internet-of-Things and Cyber-Physical System

IEEE Access Editor-in-Chief:
Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact:


Cyber-Physical Systems

Submission Deadline: 31 December 2018

IEEE Access invites manuscript submissions in the area of  Cyber-Physical Systems.  

Recent years have witnessed the increasing synergy between the computational technologies and physical components. A Cyber-Physical System (CPS) is composed of a collection of devices interacting with each other and communicating with the physical world. It integrates computation and communication aspects together with control and monitoring techniques. Various CPS applications can be found in almost all areas of human life, such as manufacturing systems, smart grids, robotics, transportation systems, medical devices, military, home area networks and smart buildings, etc. 

The aim of this Special Section in IEEE Access is to discuss recent advances of the design, modeling, specification, analysis, verification and application merits of CPS. Such aspects involve interdisciplinary fields of science, thus the following wide range of topics is covered (but not limited to):

Control techniques of CPS:

      • Control systems, concurrent control systems, automatic control and robotics.
      • Distributed and networked control systems.
      • Control algorithms and methodologies.

Design, analysis and verification of CPS:

      • Design methodologies of CPS.
      • Model-based design, including Model-Driven Development, Unified Modeling Language (UML, SysML), etc.
      • Mixed-signal design.
      • Concurrency modeling and analysis, including Petri net-based systems.
      • Optimization techniques.
      • Verification and validation techniques, including formal verification methods.
      • Performance evaluation.
      • Integrated tool suits for CPS design, analysis and verification.

Software and hardware aspects of CPS:

      • Computation models, including mathematical descriptions and models.
      • Cloud computing.
      • Real-time systems, including real-time sensing and computing.
      • Embedded systems.
      • Programmable devices, including logic synthesis and implementation methods.

Networking in CPS:

      • Networked embedded systems.
      • Wireless sensor networks.
      • Internet of things, including aspects of designing, organization and implementation.

Applications of CPS:

      • Autonomous, adaptive and cooperative CPS.
      • Mobile, wearable, and implantable CPS in healthcare.
      • Cognitive CPS with perception, learning, and optimal decision making.
      • Reference architectures for various application domains.
      • Smart grids, power generation and distribution, power systems.
      • Smart cities, home area networks (HANs).
      • Manufacturing, flexible manufacturing systems, smart factories, Industry 4.0.
      • Reconfigurable control systems (including distributed and integrated systems).
      • Dependable CPS (cryptology, security algorithms, security aspects).

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.


Associate Editor:  Remigiusz Wisniewski, University of Zielona Gora, Poland

Guest Editors:

  1. Grzegorz Benysek, University of Zielona Gora, Poland
  2. Luis Gomes, Universidade Nova de Lisboa, Portugal
  3. Dariusz Kania, Silesian University of Technology, Poland
  4. Theodore Simos, University of Peloponnese, Greece
  5. MengChu Zhou, New Jersey Institute of Technology, USA

Relevant IEEE Access Special Sections:

  1. Security Analytics and Intelligence for Cyber Physical Systems
  2. Big Data Analytics in Internet-of-Things And Cyber-Physical System
  3. Data-Driven Monitoring, Fault Diagnosis and Control of Cyber-Physical Systems

IEEE Access Editor-in-Chief:
Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact:

Towards Service-Centric Internet of Things (IoT): From Modeling to Practice

Submission Deadline: 31 October 2018

IEEE Access invites manuscript submissions in the area of Towards Service-Centric Internet of Things (IoT): From Modeling to Practice

The Internet of Things (IoT) refers to an emerging paradigm, to seamlessly and ubiquitously integrate a large number of smart things with intra/inter links to the physical and cyber worlds. The sensor enabled communication technologies are connecting billions of things, by efficiently utilizing their locations in the real world. It has brought active participation and tangible creation of benefits to the economy and ultimately the society. As the so-called smart things are extremely diverse and heterogeneous in terms of computing and communication technology and resource capability, there is no stand-alone solution towards realization of service-centric provisioning in IoT. The service domain in IoT environments is also diverse including energy efficiency, computing capability, coordination time, resource harvesting capacity, dimension of things, etc. The growing diversity enforces the necessity to address the most significant issue of technological complexity, for practical enhancement and evaluation of quality of service (QoS) and quality of experience (QoE) for service-oriented use cases in IoT environments.

The literature has vastly contributed towards architecture design for cooperative communication, computing and application-centric development. However, the efforts towards realistic modeling and evaluation of QoS and QoE in IoT environments have not witnessed enough attention in academia and industrial research labs. Although the realistic implementations of IoT environments have been realized in various domains including product engineering, marketing, health, sports, and security, the scalability of these implementations is not guaranteed due to the lack of QoS and QoE evaluation in IoT environments. The R&D towards service-centric technologies in IoT environments will support the mass realization of realistic IoT implementations resulting in effective economic and social benefits. Driven by the enhancement of QoS and QoE for diverse use cases in IoT, new technologies are keen to the rise of spanning different service-centric aspects in IoT environments.

The aim of this Special Section in IEEE Access is to provide opportunities for researchers and practitioners to publish their latest and innovative contributions with new methodologies and modeling techniques towards service-centric IoT framework. Theoretical investigation and prototype implementation-based studies are particularly welcomed, as IEEE Access attracts practical articles discussing new experiments or measurement techniques and interesting solutions to engineering, including negative results. The topics of interest include, but are not limited to:

  • Standardization progress in service-centric IoT
  • Business model driven network services management in service-centric IoT
  • Resource management and scheduling for service-centric IoT
  • Energy and resource efficiency in service-centric IoT
  • Location accuracy enhancements for service-centric IoT
  • Cloud and edge computing for massive service-centric IoT applications
  • New waveforms and frame structures for massive IoT connection
  • High-reliability-low-latency tactile communications for IoT
  • Coverage extension with resource constrained sensors
  • Enhancing privacy, security and trust for service-centric IoT

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Yue Cao, Northumbria University, UK

Guest Editors:

  1. Omprakash Kaiwartya, Northumbria University, UK
  2. Xiaodong Xu, Beijing University of Posts and Telecommunications, China
  3. William Liu, Auckland University of Technology, New Zealand
  4. Jaime Lloret, Polytechnic University of Valencia, Spain
  5. Yuanwei Liu, Queen Mary University of London, UK
  6. Yuan Zhuang, Bluvision Inc., USA


Relevant IEEE Access Special Sections:

  1. Intelligent Systems for the Internet of Things
  2. Multimedia Analysis for Internet-of-Things
  3. Cyber-Physical-Social Computing and Networking


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: