Advances on High Performance Wireless Networks for Automation and IIoT

Submission Deadline:  30 November 2022

IEEE Access invites manuscript submissions in the area of Advances on High Performance Wireless Networks for Automation and IIoT.   

High dependability and bounded transmission times are historically the main requirements of any communication networks conceived for automation. The recent pervasive introduction of wireless extensions to the wired backbones has opened new complex challenges, the most critical one being the ability to satisfy such requirements also over intrinsically unreliable communication supports like the radio spectrum.

Technologies for making devices communicate seamlessly over the air are expected to be adopted more and more in future digital ecosystems, including cyber-physical systems. The primary enabler is probably constituted by the Industrial Internet of Things (IIoT), which can be profitably applied to smart industry, smart environment, and smart agriculture, to cite a few. Thanks to IIoT, applications are hidden details about the underlying physical networks, as long as constraints on reliability and timeliness of end-to-end data transfers are overall met. Additional requirements often have to be considered, which impact on feasibility (technical, economical, and ecological); for example, power consumption may affect maintenance costs and battery waste, whereas communication range is a critical aspect in brownfield scenarios.

Because of the inherent complexity of wirelessly interconnected distributed systems, the relevant key performance indicators (KPI) to be used for design and optimization are application-driven, and usually the work of designers involves finding a compromise between a plurality of aspects, e.g., dependability, latency and jitter, power consumption, covered area, and node density.

It is worth stressing that, when dealing with IIoT, the term “high performance” does not refer simply to raw throughput, but rather to the ability of the network to satisfy in the best way and at the same time all the increasingly demanding requirements and constraints, both functional (mobility through wireless communication, ability to operate self-powered for very long times, support for safety and security, clock synchronization, etc.) and about performance (as expressed by above KPIs), dictated by modern distributed control applications for specific classes of (cyber-)physical systems. As an example, time, and consequently bounded latencies and synchronization, are essential for control applications: if the density of nodes is high, coordinated access to the channel is needed. If nodes are not fixed, low energy consumption and seamless mobility are other main requirements that need to be optimized to achieve high performance in this kind of network.

While a single winning wireless IIoT technology cannot be clearly identified, several competing solutions are currently available off-the-shelf. In the context of unlicensed bands, which are particularly appealing to users because they do not imply any fees, some of the most important ones are IEEE 802.11 (Wi-Fi), wireless sensor and actuator networks (WSN/WSAN) based on IEEE 802.15.4, including DSME and TSCH (Zigbee, WIA-PA, WirelessHART, ISA100.11a, 6TiSCH, etc.), Bluetooth Low Energy (IO-Link Wireless), and LoRaWAN. Concerning solutions operating in licensed bands, recent additions to 5G/6G, like URLLC and mMTC, are deemed particularly relevant in view of their use in the context of automation and sensing.

Current research on high-speed, highly dependable, and low-power wireless networks opens a promising door for the evolution of communications in automated systems, which will be heterogeneous in nature but, at the same time, capable of meeting very demanding constraints.

This Special Section aims to provide a forum for the academic and industrial communities to present the latest advances on wireless communication, with a specific focus on automation.

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

  • Dependable and timely wireless networking: protocols, algorithms, and architectures
  • Ultra-Reliable, Low-Latency, and Quasi-Deterministic wireless networks
  • Ultra-Low Power and Green wireless networks
  • Mesh, Long-Range, and Ultra-Dense wireless networks
  • Cross-Layer optimization of wireless protocol stacks
  • Software-Defined Radios (SDR) and Networks (SDN for wireless) to enhance communication KPIs
  • Coexistence and compatibility among wireless networks with performance optimization
  • High performance Mobile Ad Hoc Networks and opportunistic networking
  • Analysis, simulation, and modeling techniques in time-critical wireless systems
  • Extension of TSN features to wireless including IEEE 802.11 and 5G/6G cellular networks
  • Performance optimized integration and adaptation of 5G/6G systems with legacy industrial protocols
  • Standardization efforts on next generation wireless networks and convergence toward TSN
  • Precise time synchronization and localization over wireless networks
  • Reliable roaming and fast handover in wireless networks
  • Data compression techniques for high performance wireless networks
  • Machine learning to improve the quality of wireless communication
  • Wireless design for high performance applications in smart factories, smart agriculture, and smart environment
  • Non-5G high performance wireless networks for rural areas
  • PHY layer security mechanisms for URLLC wireless communication links
  • Fault mitigation for reliable wireless networks
  • Future demanding industrial applications that require high performance wireless networks

 

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Associate Editor:  Stefano Scanzio, CNR-IEIIT, Italy

Guest Editors:

    1. Hans-Peter Bernhard, Silicon Austria Labs and Johannes Kepler University, Austria
    2. Dave Cavalcanti, Intel Corporation, USA
    3. Gianluca Cena, CNR-IEIIT, Italy
    4. Lei Shu, Nanjing Agricultural University, China
    5. Iñaki Val, IKERLAN, Spain
    6. Lukasz Wisniewski, Institute Industrial IT – inIT of Technische Hochschule OWL, Germany

 

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

Article submission: Submit manuscripts to: http://ieee.atyponrex.com/journal/ieee-access

For information regarding IEEE Access, including its peer review policies and APC information, please visit the website http://ieeeaccess.ieee.org

For inquiries regarding this Special Section, please contact: stefano.scanzio@ieiit.cnr.it.

Metal Additive Manufacturing

Submission Deadline:  15 December 2021

IEEE Access invites manuscript submissions in the area of Metal Additive Manufacturing.   

Additive manufacturing (AM) is a main driver of the Industry 4.0 paradigm. While the additive manufacturing of plastics is common, metal additive manufacturing processes still face several research challenges. The high cost and unpredictable defects in final parts and products are preventing complete deployment and adoption of additive manufacturing in the metalworking industries. Several aspects need improvement, including robustness, stability, repeatability, speed and right-first-time manufacturing. Nevertheless, its potential to the production of structural parts is significant, from the medical to the aeronautics industry.

The industrialization of additive manufacturing requires holistic data management and integrated automation. End-to-end digital manufacturing solutions have been developed in the last few years, enabling a cybersecure bidirectional dataflow for a seamless integration across the entire AM chain. Novel manufacturing methodologies need to be developed to ensure the manufacturability, reliability and quality of a target metal component from initial product design, implementing a zero-defect manufacturing approach ensuring robustness, stability and repeatability of the process.

This Special Section in IEEE Access will bring together academia and industry to discuss technical challenges and recent results related to additive manufacturing. Theoretical, numerical and experimental development in this domain are welcome. The articles are expected to report original findings or innovative concepts featuring different topics related to metal additive manufacturing. Industry-related studies are welcome, especially the ones demonstrating advanced applications of metal additive manufacturing in challenging scenarios.

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

  • Data interoperability
  • Data analytics
  • Digitalization and data security
  • Topologic optimization
  • Additive manufacturing building strategy
  • Multi-physics process simulation and modeling
  • Product engineering optimization
  • Testing and characterization
  • Zero defect manufacturing and process control
  • Quality assurance
  • From CAD design to real part production
  • Advanced industry applications

 

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

 

Associate Editor:  Pedro Neto, University of Coimbra, Portugal

Guest Editors:

    1. Mustafa Megahed, ESI Group, Germany
    2. Matthew Gilbert, The University of Sheffield, UK
    3. Kaixiang Peng, University of Science and Technology Beijing, China
    4. Felix Vidal, AIMEN Technology Centre, Spain
    5. Leroy Gardner, Imperial College London, UK
    6. Xuemin Chen, Texas Southern University, USA
    7. Stasha Lauria, Brunel University London, UK

 

Relevant IEEE Access Special Sections:

    1. Advanced Artificial Intelligence Technologies for Smart Manufacturing
    2. Key Technologies for Smart Factory of Industry 4.0
    3. Advances in Machine Learning and Cognitive Computing for Industry Applications

 

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

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

 For inquiries regarding this Special Section, please contact: pedro.neto@dem.uc.pt.

Advanced Energy Conversion Systems Based on Multi-Port Electrical Machines

Submission Deadline:  31 July 2021

IEEE Access invites manuscript submissions in the area of Advanced Energy Conversion Systems Based on Multi-Port Electrical Machines.

Over the last decade, energy conversion systems based on multiple-electrical-port and multiple-mechanical-port electrical machines have attracted widespread attention from both academia and industry, with their benefits of high efficiency, compactness and flexibility. This concept has been adopted in many industrial applications, such as wind power generation, ship shaft power generation, ship electric propulsion, electric vehicle, rail transportation, more/all electric aircraft, and AC/DC-micro-grids, among others. Due to the ever-increasing demand for highly reliable and cost-effective energy conversion systems, advanced machine/converter topologies, modeing approaches, control strategies, and reliability evaluations of the multi-port electrical machine and drive systems are in great need.

The objective of this Special Section in IEEE Access is to identify, address and disseminate state-of-the-art research works on design, modeling and control of multi-port electrical machines, from theory to applications. Prospective authors are invited to submit original contributions, including survey papers, in this Special Section.

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

  • Novel topologies of multi-port electrical machine and drive systems
  • Multi-physics modeling and analysis of multi-port electrical machines and drive systems
  • Advanced control strategies for highly reliable and cost-effective energy conversion using multi-port electrical machines
  • Fault monitoring, diagnosis and tolerance operation of multi-port electrical machines and drives
  • Lifetime prediction and reliability assessment
  • New applications of multi-port electrical machines in energy conversion

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

  Associate Editor:  Wei Xu, Huazhong University of Science and Technology, China

  Guest Editors:

    1. Yi Liu, Huazhong University of Science and Technology, China
    2. Jianguo Zhu, The University of Sydney, Australia
    3. Z. Q. Zhu, The University of Sheffield, UK
    4. Ayman El-Refaie, Marquette University, USA
    5. David G. Dorrell, University of the Witwatersrand, South Africa
    6. Shiyi Shao, Wuxi Silent Electric System Technology Co, Ltd., China

Relevant IEEE Access Special Sections:

    1. Addressing Challenging Issues of Grids with High Penetration of Grid Connected Power Converters: Towards Future and Smart Grids
    2. Evolving Technologies in Energy Storage Systems for Energy Systems Applications
    3. Emerging Technologies for Energy Internet

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

  Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

  For inquiries regarding this Special Section, please contact: weixu@hust.edu.cn.

Real-Time Machine Learning Applications In Mobile Robotics

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Real-Time Machine Learning Applications In Mobile Robotics.

In the last ten years, advances in machine learning methods have brought tremendous developments to the field of robotics. The performance in many robotic applications such as robotics grasping, locomotion, human-robot interaction, perception and control of robotic systems, navigation, planning, mapping, and localization has improved since the appearance of modern machine learning methods. In particular, deep learning methods have brought significant improvements in a broad range of robot applications, including drones, mobile robots, robotics manipulators, bipedal robots, and self-driving cars. The availability of big data and more powerful computational resources, such as Graphics Processing Units (GPUs), have made numerous robotics applications feasible that were not possible previously.

Despite recent advances, there are still gaps in applying available machine learning methods to real robots. Directly transferring algorithms that work successfully in the simulation to the real robot and self-learning of robots are among the current challenges. Moreover, there is also a need for new real-time algorithms and more explainable and interpretable models that receive and process data from the sensors such as cameras, Light Detection and Ranging (LIDAR), Inertial Measurement Unit (IMU), and Global Positioning System (GPS), preferably in an unsupervised or semi-supervised fashion.

This Special Section in IEEE Access aims to present the works relating to the design and usage of the real-time machine learning methods on all mobile robots including legged and humanoid platforms, focusing on state-of-the-art methods, such as deep learning, generative adversarial networks, scalable evolutionary algorithms, reinforcement learning, probabilistic graphical models, Bayesian methods, and explainable and interpretable approaches. The Special Section will present original research articles covering the implementations and applications of mobile robots and incorporating up-to-date results, theorems, algorithms, and systems.

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

  • Robotic learning by simulations
  • Learning and adaptive algorithms for robotic mobile manipulators and humanoid robots
  • Human-robot interaction, learning from human demonstrations
  • Learning for human-robot collaborative tasks
  • Autonomous grasping and manipulation by using mobile robots
  • Multi-robot systems, networked robots, and robot soccer
  • Control, complex action learning, and predictive learning from sensorimotor information for bio-inspired social robots
  • Autonomous driving, navigation, planning, mapping, localization, collision avoidance, and exploration
  • Robotics and autonomous system design and implementation
  • Nonlinear control and visual servoing in robotic systems
  • Soft robotics
  • Usage of sensors, such as EEG, ECG, and IMU in robotics
  • Usage of explainable machine learning and interpretable artificial intelligence in robotics

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

Associate Editor:  Aysegul Ucar, Firat University, Turkey

Guest Editors:

    1. Jessy W. Grizzle, University of Michigan, USA
    2. Maani Ghaffari Jadidi, University of Michigan, USA
    3. Mattias Wahde, Chalmers University of Technology, Sweden
    4. Levent Akin, Bogazici University, Turkey
    5. Jacky Baltes, National Taiwan Normal University, Taiwan
    6. Işıl Bozma, Bogazici University, Turkey
    7. Jaime Valls Miro, University of Technology Sydney, Australia

Relevant IEEE Access Special Sections:

  1. Advances in Machine Learning and Cognitive Computing for Industry Applications
  2. Integrative Computer Vision and Multimedia Analytics
  3. Uncertainty Quantification in Robotic Applications


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

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: agulucar@firat.edu.tr.

Advances in Machine Learning and Cognitive Computing for Industry Applications

Submission Deadline: 31 May 2020

IEEE Access invites manuscript submissions in the area of Advances in Machine Learning and Cognitive Computing for Industry Applications.

Over the past few years, great progress has been made due to advances in machine learning and cognitive computing. For example, with the adoption of Convolutional Neural Networks (CNNs), computer vision has surpassed human vision in the task of image recognition. Moreover, the improvement in Natural Language Processing (NLP) makes machine translation, speech recognition, and other sequence applications more powerful than ever. It is the significant progress of machine learning algorithms, computing capability, and big data that makes machine learning and cognitive computing increasingly powerful in many applications. Compared to machine learning, cognitive computing places more emphasis on how the human brain works. Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. In industrial scenarios, the data amount, as well as data generation speed, is very different compared to standard machine learning data sets. It is a challenge to utilize these heterogeneous data and find meaningful insights for practical applications.

As the basis, the Internet of Things (IoT) middleware platforms, communication, and network ecosystems should be involved. Considering the heterogeneity of industrial data, we are expected to inspect, clean, transform, and model data with the goal of specific industrial applications. Moreover, specialized algorithms, computing architectures, and feature engineering are needed to review, analyze, and present information. With the support of machine learning and cognitive computing, the significant insights and knowledge hidden behind industrial data can be capitalized for process optimization, anomaly detection, energy management, and so on. This Special Section focuses on consolidating research efforts that aim at machine learning and cognitive computing for industrial applications.

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

  • Development of new machine learning algorithms for industrial applications
  • Big data analytics, new algorithms, and approaches
  • IoT system engineering
  • Cognitive computing, affective computing (artificial emotional intelligence), and other innovative approaches for industrial scenarios
  • Context-aware, emotion-aware, and other novel information and communication technologies based on machine learning and cognitive computing
  • Machine learning for human, computer and machine interface
  • Multi-modal sensor fusion, unstructured data mining and knowledge discovery in industrial applications
  • Big data analytics software architectures
  • Developing reusable and analytic tools and frameworks
  • Development of autonomous systems for industrial applications
  • New theories and applications of deep learning in industrial informatics

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

 

Associate Editor: Min Xia, Lancaster University, United Kingdom

Guest Editors:

    1. Zheng Liu, University of British Columbia, Canada
    2. Hong-Ning Dai, Macau University of Science and Technology, China
    3. Yu Zhang, University of Lincoln, UK
    4. Jixiang Yang, Huazhong University of Science and Technology, China
    5. Tsuyoshi Ide, IBM T. J. Watson Research Center, USA
    6. Jiong Jin, Swinburne University of Technology, Australia

 

Relevant IEEE Access Special Sections:

  1. Data Mining for Internet of Things
  2. Artificial Intelligence in CyberSecurity
  3. Intelligent Data Sensing, Collection and Dissemination in Mobile Computing


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

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: m.xia3@lancaster.ac.uk.

Design and Analysis Techniques in Iterative Learning Control

Submission Deadline:  31 July 2019

IEEE Access invites manuscript submissions in the area of Design and Analysis Techniques in Iterative Learning Control.

Recently, great progress has been witnessed in both theory developments and practical applications of iterative learning control (ILC). ILC has been widely used in industry, for example, in chemical processes, robotic manipulators, hard disk drives, milling and laser cutting, traffic flow control systems, and rehabilitation robotic systems. With its rapid development, ILC has also encountered many theoretical and practical challenges including new system applications such as fractional-order systems, new operation environments such as networked structure and complex networks, and new technical innovations such as convergence analysis methods. Therefore, ILC is at a significant stage for making fundamental breakthroughs, which motivates this Special Section in IEEE Access.

Since the proposal of its original concept by Arimoto, et al., in 1984, ILC has developed rapidly over the last three decades. A survey by Bristow, Tharayil, and Alleyne, in IEEE Control Systems provided a comprehensive review of the fundamental framework of ILC and common design and analysis techniques. A comprehensive review was presented in another survey published by Ahn, Chen, and Moore, in IEEE Transactions on Systems, Man, and Cybernetics, Part C, which covered the field from 1998 to 2004. Later surveys reported on other directions of ILC. From these surveys and the references therein, it is observed that the exploration of ILC in various directions has been a mainstream in the past few decades. These explorations have greatly enriched the system of ILC and established the advantages of ILC compared with other traditional control methodologies. However, we are facing a bottleneck in developing ILC due to the lack of current growth.

Scholars in the community have reached a consensus that ILC requires an in-depth review of the past contributions as well as an exciting look at the future directions for ILC. It is necessary to collect fresh ideas from the community to contribute to an understanding of the future developments of ILC. In other words, while exploring ILC for more system and operation conditions, we should also explore the essential advantages of ILC so that we can establish a comprehensive system of ILC. In particular, three major directions should be explored. First, we can apply ILC to new systems, especially newly formed system formulation. This extension can help broaden the potential application range and promote associated research. Second, we should pay attention to new operation environments, especially the emerging conditions. For example, Cyber Physical Systems (CPS) has gained attention from the community; how to implement ILC in CPS is interesting, but security issues should also be considered. Third, we must propose new design and analysis techniques, to carry forward the merits of ILC.

This Special Section in IEEE Access invites original articles addressing both design and analysis techniques in the area of learning control, including novel applications, design frameworks, analysis tools, essential performance improvements, and other related topics in learning control. It aims to provide an in-depth review of the recent advances and a comprehensive outlook of the development trends in learning control.

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

  • Learning control for new types of systems
  • Nonlinear design framework of learning control
  • Novel convergence and performance analysis techniques
  • Learning control with new operation conditions
  • New applications
  • Integration with artificial intelligence
  • Big data driven learning control

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

 

Associate Editor:  Youqing Wang, Shandong University of Science and Technology, China

Guest Editors:

  1. Dong Shen, Beijing University of Chemical Technology, China
  2. Wojciech Paszke, University of Zielona Gora, Poland
  3. YangQuan Chen, University of California, Merced, USA

 

Relevant IEEE Access Special Sections:

  1. Cyber-Physical Systems
  2. Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things
  3. Big Data Learning and Discovery


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

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: wang.youqing@ieee.org.

Deep Learning: Security and Forensics Research Advances and Challenges

Submission Deadline: 30 October 2019

IEEE Access invites manuscript submissions in the area of Deep Learning: Security and Forensics Research Advances and Challenges.

Generative and discriminative deep learning models have been utilized in a broad range of artificial intelligence-related applications (e.g., computer vision, natural language processing), cybersecurity (e.g., facial authentication, and vulnerability and exploitation detection), and forensic-related tasks. However, cyber attackers could breach the trustworthiness and efficiency of deep learning models (i.e., adversarial machine/deep learning). There are different methods that have been used to hack machine/deep learning models, for example, exploiting the model structure, injecting malicious data in the training, validation, and/or testing sets, and/or modifying hyper-parameters of the models.

The objective of this Special Section in IEEE Access is to compile recent research efforts dedicated to the study of Deep Learning in security and forensic-related applications, to enhance performance in biometrics, spoofing detection, intrusion detection, authentication, digital forensics, access control, image steganography and steganalysis, deep learning computation and training security, and malicious web content identification, etc. Specifically, we are soliciting for high quality and unpublished work on recent advances in new deep learning methodologies that can be applied to a broad range of applications.

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

  • Adversarial attacks in deep learning
  • Cryptography protocols and algorithms for deep learning
  • Deep learning computation and training security
  • Deep learning for cyber security applications (e.g., malicious web content identification, intrusion detection and privacy-preserving, vulnerability and exploitation Identification, and facial and/or biometric spoofing detection)
  • Deep learning for natural language processing
  • Deep learning for video and image processing
  • Deep learning-based forensics and anti-forensics
  • Gait authentication and deep learning
  • Generative adversarial deep learning
  • Object detection and transfer learning
  • Privacy and trust challenges associated with deep learning
  • Trends in specific deep learning domains

 

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

 

Associate Editor:  Kim-Kwang Raymond Choo, University of Texas at San Antonio, USA

Guest Editors:

  1. Zhen Qin, University of Electronic Science and Technology of China, China
  2. Nour Moustafa, University of New South Wales @ADFA, Australia
  3. William Bradley Glisson, Sam Houston State University, USA
  4. Sheikh Mahbub Habib, Continental AG, Germany

 

Relevant IEEE Access Special Sections:

  1. Advanced Software and Data Engineering for Secure Societies
  2. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
  3. Trusted Computing


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

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: raymond.choo@fulbrightmail.org.

Data Mining for Internet of Things

Submission Deadline: 31 March 2020

IEEE Access invites manuscript submissions in the area of Data Mining for Internet of Things.

The Internet of Things (IoT) has become an important research domain as mature appliances, systems, infrastructures, and their applications have shown their potential in recent years. We can foresee that smart homes and smart cities using these technologies will be realized in the near future. However, many consumers have concerns with the “smart” information system and environment, especially when entering the era of IoT. The expectations of IoT and its relevant products in this new era are quite high. Instead of smartness alone, consumers of IoT products and services would like to see IoT technologies bring about more intelligent systems and environments. The main difference between the “smart thing” and “intelligent thing” is that the former will use predefined rules to provide services to a user whereas the latter will not only use predefined rules, but will also use the analytical results from intelligent mechanisms to discover suitable services for users. More precisely, using only the predefined rules may not be sufficient to consider every possible situation because the number of rules is limited. Using the results obtained after data analysis we can provide additional information to an IoT system to make it better understand the needs of a user. This is why data analytics has become a promising technology for IoT.

Although most researchers of data mining have recognized how to analyze large-scale data is an important research topic for many years, considerations for the IoT environment are quite different from those for the traditional environment because data for the IoT will be created more quickly and in different formats. That is why research on data analytics for IoT have typically been relevant to big data analytics and cloud computing technologies in recent years. This does not mean that traditional data mining and intelligent algorithms are no longer useful for IoT. In fact, how to redesign these algorithms to make them more efficiently and effectively work for IoT has been a critical research trend. In addition to modifying the traditional data mining and intelligent algorithms, an alternative is to develop new data analysis algorithms. Using deep learning technologies for supervised learning to construct a set of classifiers to recognize data entering an IoT system, and using metaheuristic algorithms for unsupervised learning to find out good solutions for classifying unknown data are two promising technologies today. Moreover, how to determine interesting patterns from a series of events of an IoT system is also a critical research topic. In summary, many modern technologies, such as big data analytics, statistical technologies, and other analysis technologies, have also been used for finding out useful information from an IoT system to provide needed services to a user and to enhance the performance of the IoT system as a whole today.

This Special Section in IEEE Access will focus on data mining technologies for the IoT and its applications, such as smart home, smart city, industry, online social network, and even internet of vehicles. We also welcome research on IoT related technologies, such as cloud computing, network security, wireless sensor network, vehicular networking, smart grids, and big data.

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

  • Data Mining for the IoT
  • Machine and Deep Learning for the IoT
  • Metaheuristic Algorithms for the IoT
  • Cloud Computing for the IoT
  • Big Data for the IoT
  • Mobile Computing and Sensing for the IoT
  • Security Framework for the IoT
  • Privacy Protection for the IoT
  • IoT in Smart Home and Smart City
  • IoT in Energy Management
  • Industry IoT
  • IoT in Agriculture and Environment
  • IoT in eHealth and Ambient Assisted Living
  • Internet of Vehicles
  • Edge Computing
  • Applications of the 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: Chun-Wei Tsai, National Chung-Hsing University, Taiwan

Guest Editors:

  1. Mu-Yen Chen, National Taichung University of Science and Technology, Taiwan
  2. Francesco Piccialli, University of Naples Federico II, Italy
  3. Tie Qiu, Tianjin University, China
  4. Jason J. Jung, Chung-Ang University, Republic of Korea
  5. Patrick C. K. Hung, University of Ontario Institute of Technology, Canada
  6. Sherali Zeadally, University of Kentucky, USA

 

Relevant IEEE Access Special Sections:

  1. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
  2. Healthcare Information Technology for the Extreme and Remote Environments
  3. Internet-of-Things (IoT) Big Data Trust Management


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

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: cwtsai0807@gmail.com.

Emerging Trends, Issues and Challenges for Array Signal Processing and Its Applications in Smart City

Submission Deadline: 31 December 2019

IEEE Access invites manuscript submissions in the area of Emerging Trends, Issues and Challenges for Array Signal Processing and Its Applications in Smart City.

The array signal processing technique, including what is also known as direction-of-arrival (DOA) estimation, has been widely applied in radar, sonar, wireless communications and other traditional fields. The source detection and localization capability offered by array signal processing also makes it an invaluable tool in smart cities. As an emerging research area, smart city has brought up a unique set of challenges and opportunities for sensor array research. These include low signal-to-noise ratio (SNR), limited number of measurements, high interference from a multitude of sources, and the need for energy efficiency.

Traditionally, the challenge of sensor array signal processing arises from the fact that the observed snapshots are nonlinear functions of the directions of interest. The performance of traditional DOA estimation algorithms, such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT), may greatly degrade at a low SNR or with a small snapshot number. Recently, sparse representation (SR) based methods have been rapidly developed in order to deal with these problems. These methods have many advantages such as flexibility in incorporating different noise types and measurement schemes, high estimation accuracy and spatial resolution, as well as the ability to handle correlated sources. However, the disadvantages are obvious as well, particularly the model mismatch and the grid mismatch issues. As a solution, the gridless method based on atomic norm was developed as an alternative that addresses many of the issues. However, this approach suffers from high computational complexity.

This Special Section in IEEE Access is intended to encourage high-quality research in array signal processing and its applications in smart city. Authors are invited to submit articles presenting new research related to the theory or practice about array signal processing techniques, including algorithms, models, technology and applications. All submissions must describe original research, and not published or currently under review for another workshop, conference, or journal. The topics suggested can be discussed in terms of concepts, the state of the art, implementations, and running experiments or applications.

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

  • Compressed sensing based methods for DOA estimation
  • Low computational complexity gridless methods for DOA estimation
  • Application of machine learning for DOA estimation
  • DOA estimation for sparse arrays, super-resolution for MIMO radar and DOA and polarization estimation based on SR techniques
  • Robust DOA estimation in low SNR or with a small snapshot number
  • Array geometry optimization for high accuracy DOA estimation
  • Tensor based method for high dimensional parameter estimation
  • Performance analysis for different DOA estimation methods
  • Convex and nonconvex optimizations related to array signal processing
  • Hardware implementation for advanced array signal processing techniques
  • New technologies and research trends for array signal processing
  • Array signal processing in emergency medical system
  • Array signal processing for fault detection
  • Other emerging research field of smart city exploiting array signal processing techniques

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

 

Associate Editor:  Liangtian Wan, Dalian University of Technology, China


Guest Editors:

  1. Xianpeng Wang, Hainan University, Hainan, China
  2. Gongguo Tang, Colorado School of Mines, USA
  3. Wei Liu, University of Sheffield, UK
  4. Guoan Bi, Nanyang Technological University, Singapore


Relevant IEEE Access Special Sections:

 

  1. Green Signal Processing for Wireless Communications and Networking
  2. Underwater Wireless Communications and Networking
  3. GNSS, Localization, and Navigation Technologies


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

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: wan.liangtian.2015@ieee.org

 

Advanced Energy Storage Technologies and Their Applications

Submission Deadline: 31 May 2019

IEEE Access invites manuscript submissions in the area of Advanced energy storage technologies and their applications.

The depletion of fossil fuels, the increase of energy demands, and the concerns over climate change are the major driving forces for the development of renewable energy such as solar energy and wind power. However, the intermittency of renewable energy has hindered the deployment of large scale intermittent renewable energy, which, therefore, has necessitated the development of advanced large-scale energy storage technologies. The use of large scale energy storage can effectively improve the efficiency of energy resource utilization, and increase the use of variable renewable resources, the energy access and the end-use sector electrification (e.g. electrification of transport sector).

The main objective of this Special Section in IEEE Access is to provide a platform for presenting the latest research results on the technology development of large scale energy storage. We welcome research articles about theoretical, methodological and empirical studies, as well as review articles that provide a critical overview on the state-of-the-art of these technologies. This Special Section is open to all types of energy, such as thermal energy, mechanical energy, electrical energy and chemical energy, using different types of systems, such as phase change materials, batteries, supercapacitors, fuel cells, compressed air, etc., which are applicable to various types of applications, such as heat and power generation, electrical/hybrid transportation etc. Original, high quality technical articles as well as original review and survey articles are encouraged.

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

  • Novel energy storage materials and topologies
  • Application in electrical/hybrid driven system and electrical/hybrid vehicles
  • Next generation energy storage and conversion devices, systems or techniques
  • Large scale energy storage system modeling, simulation and optimization, including testing and modeling ageing processes
  • Advanced energy storage management systems, including advanced control algorithms and fault diagnosis/online condition monitoring for energy storage systems
  • Artificial Intelligence in Energy and Renewable Energy Systems
  • Wireless power transfer, charging systems and infrastructures
  • Big Data Analytics in Energy
  • Business model for the application and deployment of energy storage
  • Lifecycle analysis, repurposing, and recycling

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

Associate Editor: Rui Xiong, Beijing Institute of Technology, China

Guest Editors:

  1. Suleiman Sharkh, University of Southampton, UK
  2. Hailong Li, Mälardalen University, Sweden
  3. Kevin Bai, University of Michigan, USA
  4. Weixiang Shen, Swinburne University of Technology, Australia
  5. Peng Bai, Washington University in St. Louis, USA
  6. Joe Zhou, Kettering University, USA

 

Relevant IEEE Access Special Sections:

  1. Energy Management in Buildings
  2. Battery Energy Storage and Management Systems
  3. Advanced Modeling and Control of Complex Mechatronic Systems with Nonlinearity and Uncertainty

 

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

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: rxiong@bit.edu.cn