IEEE Access: Now Over 25,000 Articles Published

IEEE Access now has over 25,000 articles published on IEEE Xplore since its inception in 2013. The journal has an Impact Factor of 4.098 and an expedited peer review process of only 4 to 6 weeks. Submit your research today and become a part of this growing publication.

IEEE Access: Now Over 20,000 Articles Published

IEEE Access now has over 20,000 articles published on IEEE Xplore since its inception in 2013. The journal has an Impact Factor of 4.098 and an expedited peer review process of only 4 to 6 weeks. Submit your research today and become a part of this growing publication.

Data Analytics and Artificial Intelligence for Prognostics and Health Management (PHM) Using Disparate Data Streams

Submission Deadline: 30 November 2019

IEEE Access invites manuscript submissions in the area of Data Analytics and Artificial Intelligence for Prognostics and Health Management (PHM) Using Disparate Data Streams.

As the key technology of condition-based maintenance and autonomous protection for engineering systems and critical components, prognostics and health management (PHM) is of increasing interest to industry, government, and academia. Due to the complexity, integration and intelligence of engineered systems and critical components, there are two significant challenges in implementing fault prediction and health management of complex modern engineered systems. First, health monitoring data obtained by sensing systems have characteristics such as large volume, high velocity, and inhomogeneity. In engineering practice, in most cases, indirect health monitoring data streams are obtained rather than direct health monitoring data. Therefore, it is necessary to extract and derive valuable and meaningful system health indices from a large amount of disorganized/unstructured and difficult to understand raw data through data analytics. Second, the health degradation process of engineered systems and critical components exhibits a high degree of nonlinearity due to significant influencing factors, such as complex structure and varying environments, and uncertain tasks. These nonlinearities are difficult to be analytically derived. By simulating and extending human intelligence, artificial intelligence (AI) can help solve such highly nonlinear problems and achieve more accurate health prediction. Therefore, the application of data analytics and artificial intelligence possesses great potential to advance the innovations and implementation of prognostics and health management using large quantities of disparate data streams.

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

  • Advanced information sensing technologies for emitting excitations and receiving degradation data
  • Advanced Big Data Analytics for degradation data
  • Signal processing algorithms for reprocessing degradation data
  • Data fusion for degradation data
  • Extraction of health indicators from engineered systems and critical components
  • Abnormal health detection algorithms
  • Advanced modeling of complex engineered systems with nonlinearity and uncertainty
  • Advanced learning technologies for diagnostics and prognostics
  • Deep learning based diagnostic and prognostic algorithms
  • Uncertainty interpretation, uncertainty quantification, uncertainty propagation and uncertainty management
  • Intelligent performance evaluation
  • Decision Making for maintenance
  • Industrial applications and their success in prognostics and health management

 

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

 

Associate Editor:  Zhaojun (Steven) Li, Western New England University, USA

Guest Editors:

    1. Qiang Miao, Sichuan University, China
    2. Faisal Khan, Memorial University of Newfoundland, Canada
    3. Lance Fiondella, University of Massachusetts Dartmouth, USA
    4. Janet Lin, Lulea University of Technology, Sweden
    5. Jeff Voas, National Institute of Standard and Technology, USA

 

Relevant IEEE Access Special Sections:

  1. AI-Driven Big Data Processing: Theory, Methodology, and Applications
  2. Advances in Prognostics and System Health Management
  3. Data Mining for Internet of Things


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: stevenli777@ieee.org.

Complex Networks Analysis and Engineering in 5G and beyond towards 6G

Submission Deadline: 31 March 2020

IEEE Access invites manuscript submissions in the area of Advances in Complex Networks Analysis and Engineering in 5G and beyond towards 6G.

Modern telecommunications networks represent one of the largest scale construction and deployment efforts with renovations occurring nearly continuously over the course of decades. The resulting networks consist of numerous subsections, each following its own trajectory of development, commingled into a complex ecosystem. Typical attributes used to characterize networks (e.g., interference, coverage, throughput, robustness, cost) fail to fully capture a key feature of future wireless networks, namely the degree of organization. This is increasingly important when we consider the trajectory of the evolution of 5G wireless networks and beyond towards 6G, with respect to densification, heterogeneity and distributed and self-organizing decision-making.

This Special Section tries to shed light on whether such a self-organizing and highly dynamic world can be treated as a complex system and whether complex systems science can give insights on the emergent properties of these kinds of networks and their design and deployment. One of the most widely accepted definitions of complex system, is that of “a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution” (M. Mitchell, “Complexity – A Guided Tour”, Oxford University Press, 2011). This view resonates with the trends we are seeing in wireless networks.

In this Special Section in IEEE Access, we invite submissions of high-quality, original technical and survey papers, which have not been published previously, on complex systems science approaches and techniques and their applications for communications networks.

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

  • Network science and statistical mechanics models for self-organizing communication networks
  • Relation between information theory and complex systems science
  • Measuring complexity and organization structure in cellular and IoT networks
  • Cellular automata and agent-based modeling of 5G networks and beyond
  • Application of complex systems science to industrial cyber-physical systems and machine-type communications for control, coordination or optimization
  • Nonlinear-system-based analysis and design in beyond 5G communication networks
  • Chaos-based communication systems for 5G and beyond
  • Design and applications of complex cyber-physical systems based on 5G and beyond
  • Emergence-driven network engineering communication and computation

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

 

Associate Editor:  M. Majid Butt, Nokia Bell Labs, France

Guest Editors:

    1. Celso Grebogi, University of Aberdeen, Scotland/UK
    2. Irene Macaluso, Trinity College Dublin, Ireland
    3. Murilo S. Baptista, University of Aberdeen, Scotland/UK
    4. Nicola Marchetti, Trinity College Dublin, Ireland
    5. Pedro H. Juliano Nardelli, LUT University, Finland
    6. Robert Hunjet, Defence Science and Technology Group, Australia
    7. Lt Col Ryan Thomas, US Air Force Academy, USA

 

Relevant IEEE Access Special Sections:

  1. Cyber-Physical Systems
  2. Intelligent and Cognitive Techniques for Internet of Things
  3. Modelling, Analysis, and Design of 5G Ultra-Dense Networks


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: majid.butt@nokia-bell-labs.com.

IEEE Access: Now Over 15,000 Articles Published

IEEE Access now has over 15,000 articles published on IEEE Xplore since its inception in 2013. The journal has an Impact Factor of 3.557 and an expedited peer review process of only 4 to 6 weeks. Submit your research today and become a part of this growing publication.

Scalable Deep Learning for Big Data

Submission Deadline: 29 February 2020

IEEE Access invites manuscript submissions in the area of Scalable Deep Learning for Big Data.

Artificial Intelligence (AI), and specifically Deep Learning (DL), are trending to become integral components of every service in our future digital society and economy. This is mainly due to the rise in computational power and advances in data science. DL’s inherent ability to discover correlations from vast quantities of data in an unsupervised fashion has been the main drive for its wide adoption.

Deep Learning also enables dynamic discovery of features from data, unlike traditional machine learning approaches, where feature selection remains a challenge. Deep Learning has been applied to various domains such as speech recognition and image classification, nature language processing, and computer vision.

Typical deep neural networks (DNN) require large amounts of data to learn parameters (often reaching to millions), which is a computationally intensive process requiring significant time to train a model. As the data size increases exponentially and the deep learning models become more complex, it requires more computing power and memory, such as high performance computing (HPC) resources to train an accuracy model in a timely manner. Despite existing efforts in training and inference of deep learning models to increase concurrency, many existing training algorithms for deep learning are notoriously difficult to scale and parallelize due to inherent interdependencies within the computation steps as well as the training data. The existing methods are not sufficient to systematically harness such systems/clusters.

Therefore, there is a need to develop new parallel and distributed algorithms/frameworks for scalable deep learning which can speed up the training process and make it suitable for big data processing and analysis.

This Special Section in IEEE Access aims to solicit research contributions from academia and industry which addresses key challenges in big data processing and analysis using scalable deep learning.

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

  • Distributed architectures/parallel programming models and tools for scalable deep learning/machine learning
  • Parallel Algorithms and models for efficient training of deep learning models for big data (e.g. partition strategies such as using different parallel approaches for network parallelism/model parallelism)
  • Efficient algorithms and architectures to support parameter optimization (e.g. parameter search, hyper parameter search, architecture search)
  • Parameter and Gradient Compression (e.g. Sparsification, Quantization)
  • Applications of deep learning/machine learning to big data (large data such as images/videos, time series data, etc.)
  • Facilitating very large ensemble-based learning on exascale systems
  • Deep Learning systems architectures: edge, cloud-edge integration

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

 

Associate Editor:  Liangxiu Han, Manchester Metropolitan University, UK

Guest Editors:

    1. Daoqiang Zhang, Nanjing University of Aeronautics and Astronautics, China
    2. Omer Rana, Cardiff University, UK
    3. Yi Pan, Georgia State University, USA
    4. Sohail Jabbar, National Textile University, Faisalabad, Pakistan
    5. Mazin Yousif, T-Systems, International, USA
    6. Moayad Aloqaily, Gnowit Inc., Canada

 

Relevant IEEE Access Special Sections:

  1. Data-Enabled Intelligence for Digital Health
  2. Distributed Computing Infrastructure for Cyber-Physical Systems
  3. Deep Learning: Security and Forensics Research Advances and Challenges


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:  l.han@mmu.ac.uk.

Advanced Communications and Networking Techniques for Wireless Connected Intelligent Robot Swarms

Submission Deadline: 31 May 2020

IEEE Access invites manuscript submissions in the area of Advanced Communications and Networking Techniques for Wireless Connected Intelligent Robot Swarms.

Robot swarm is one of the hottest topics in both robotics and artificial intelligence, and exciting progress is being achieved. As the key enablers in practical robot swarms, communication and networking are attracting attention. Most applications consider centralized control and reliable communication infrastructure, in order to avoid the significantly increased complexity of distributed task allocation, formation control and collision avoidance in robot swarms.

There are many challenges and problems that are yet to be solved in developing real-world applications of wireless connected robot swarms. For example, collaborations of heterogeneous robot swarms need to function reliably and robustly in the absence of communication infrastructures in remote areas or post-disaster rescues. The research of communications and networking for wireless-connected robot swarms demands joint efforts in robotic and communications disciplines. The objective is to develop technologies that enable efficient management of wireless spectrum resources and highly-networked intelligent behaviors to achieve the full potential of wireless-connected robot swarms.

This Special Section in IEEE Access aims to present recent developments in communications and networking for wireless connected intelligent robot swarms, and their applications, as well as to provide a reference for future research of wireless communication and networking, and their integration with autonomous robotics. The contributions of this Special Section will cover a wide range of research and development topics relevant to autonomous robotic design, cognitive communications, cognitive networking and artificial intelligence. We invite submissions of high-quality original technical and survey articles, which have not been published previously, on the analysis, modeling, simulations and field experiments, as well as articles that can fill the gap between theoretical contributions on intelligent swarms and practical demonstrations and applications.

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

  • Channel modeling and simulation for wireless connected robot swarms
  • Cognitive PHY and MAC protocol design for wireless connected robot swarms
  • Ad hoc networking for wireless connected robot swarms
  • Decentralized control and distributed protocol design for wireless connected robot swarms
  • Energy scavenging and power transfer techniques for wireless connected robot swarms
  • Data-driven optimization of wireless networks for robot swarms
  • Joint design of wireless communications and autonomous robot behaviours, e.g. networked control, network-based fault detection and tolerance, path planning, formation control, data sharing without explicit wireless communications etc.
  • Testbeds and experimental evaluations for communications and networking in wireless-connected robot swarms
  • Field demonstrations and applications of aerial, ground and underwater robotic swarms
  • Resource allocation in wireless-connected robot swarms
  • Applications of deep learning techniques in wireless connected robot swarms
  • Transfer learning and reinforcement learning for networking and communications of robot swarms in complex unknown and unexplored environments
  • Maintaining wireless communication-connectivity in wireless-connected robot swarms
  • Underwater robotic swarm communications and networking design
  • Control algorithm and behavior issues in wireless-connected robot swarms
  • Distributed sensing and precise mapping in wireless-connected robot swarms
  • Effect of smart sensing technologies on communications in wireless-connected robot swarms
  • Control, formation and navigation in wireless-connected robot swarms
  • Swarm intelligence in wireless-connected robot swarms
  • Cooperative robotic swarms for Internet-of-Things ecosystems

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

 

Associate Editor:  Jiankang Zhang, University of Southampton, UK

Guest Editors:

  1. Bo Zhang, National Innovation Institute of Defense Technology, China
  2. DaeEun Kim, Yonsei University, Korea
  3. Hui Cheng, Sun Yat-sen University, China
  4. Jinming Wen, University of Toronto, Canada
  5. Luciano Bononi, University of Bologna, Italy
  6. Venanzio Cichella, University of Iowa, USA

 

Relevant IEEE Access Special Sections:

  1. Networks of Unmanned Aerial Vehicles: Wireless Communications, Applications, Control and Modelling
  2. Network Resource Management in Flying Ad Hoc Networks: Challenges, Potentials, Future Applications, and Wayforward
  3. Artificial Intelligence and Cognitive Computing for Communications and Networks


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: jz09v@ecs.soton.ac.uk.

Performance Evaluation of Multi-UAV System in Post-Disaster Application….

Can eyes in the air counter chaos on the ground? Researchers in Japan analyzed performance of unmanned aerial vehicles (UAVs) used in the response to the 2011 Tohoku earthquake-tsunami disaster, and report on their findings in this IEEE Access article of the week.

The paper proposes an evaluation of unmanned aerial vehicles (UAVs) performance in the mapping of disaster-struck areas. Sendai city in Japan, which was struck by the Tohoku earthquake/tsunami disaster in 2011, was mapped using multi-heterogeneous UAV.

Normal mapping and searching missions are challenging as human resources are limited, and rescue teams are always needed to participate in disaster response mission. Mapping data and UAV performance evaluation will help rescuers to access and commence rescue operations in disaster-affected areas more effectively.

Herein, flight-plan designs are based on the information recorded after the disaster and on the mapping capabilities of the UAVs. The numerical and statistical results of the mapping missions were validated by executing the missions on real-time flight experiments in a simulator and analyzing the flight logs of the UAVs.

After considering many factors and elements that affect the outcomes of the mapping mission, the authors provide a significant amount of useful data relevant to real UAV modules in the market. All flight plans were verified both manually and in a hardware-in-the-loop simulator developed by the authors. Most of the existing simulators support only a single UAV feature and have limited functionalities such as the ability to run different models on multiple UAVs.

The simulator demonstrated the mapping and fine-tuned flight plans on an imported map of the disaster. As revealed in the experiments, the presented results and performance evaluations can effectively distribute different UAV models in post-disaster mapping missions.

View this article n IEEE Xplore

Mobile Multimedia: Methodology and Applications

Submission Deadline: 31 December 2019

IEEE Access invites manuscript submissions in the area of Mobile Multimedia: Methodology and Applications.

With the development of mobile computing and high-speed communication technologies, there is an increasing demand for mobile multimedia services and applications. Emerging technologies, such as mobile TV, 3D video, 360-degree video, multi-view video, free-viewpoint video, augmented reality (AR), and virtual reality (VR), have received significant interest and attention from both academia and industry. Those technologies are widely expected to bring exciting services and applications for monitoring, entertaining, training, and operating in the areas of smart home, smart city, public safety, healthcare, education, manufacturing, transportation, etc.

There are many open research issues in developing mobile multimedia systems, which could potentially affect many domains, including mobile computing, context-aware computing, human-computer interaction, cybernetics, cyber-physical human systems (CPHS), and information security and privacy. For example, the two-way communication between user devices and content providers in mobile interactive multimedia systems is highly delay-sensitive. Thus, latency modeling and evaluation is critical to system architecture design and resource allocation. Besides, as many mobile multimedia applications are location-related, research on real-time location-aware computing and context-aware computing becomes important in the development of mobile multimedia systems. Moreover, new networking and computing technologies, such as social networks, software-defined networks, edge and fog computing, and content-centric networking are expected to have great impacts on the design of mobile multimedia systems. For example, to reduce latency for AR/VR applications, software on edge computing servers can provide local object tracking and local AR/VR content caching. In addition, trust and privacy issues are very important concerns to users as malicious applications could deceive users by taking advantage of interactivity and providing false content. This Special Section in IEEE Access focuses on various theoretical and experimental views on the methodology and applications of mobile multimedia.

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

  • Architecture, algorithms, and applications of next-generation mobile multimedia systems
  • Metrics and evaluation of mobile multimedia quality
  • 3D mobile multimedia
  • Mobile interactive multimedia and AR/VR
  • Mobile multimedia networking, streaming, and computing
  • Mobile multimedia for internet of things (IoT)
  • Mobile multimedia for human-centered cyber-physical systems (CPS)
  • Standardization and prototypes
  • Security and privacy
  • Mobile multimedia data analytics
  • Artificial intelligence for mobile multimedia

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

 

Associate Editor:  Honggang Wang, University of Massachusetts Dartmouth, USA

Guest Editors:

  1. Dalei Wu, University of Tennessee at Chattanooga, USA
  2. Qing Yang, University of North Texas, USA
  3. Dapeng Wu, Chongqing University of Posts and Telecommunications, China
  4. Danda B. Rawat, Howard University, USA
  5. Enzo Mingozzi, University of Pisa, Italy

 

Relevant IEEE Access Special Sections:

  1. Recent Advances on Video Coding and Security
  2. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
  3. Sustainable Infrastructures, Protocols, and Research Challenges for Fog 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: dalei-wu@utc.edu.

Advanced Data Mining Methods for Social Computing

Submission Deadline: 31 December 2019

IEEE Access invites manuscript submissions in the area of Advanced Data Mining Methods for Social Computing.

Social networks have become an important way for individuals to communicate with each other. Various kinds of social networks develop explosively, such as online social networks, scientific cooperation networks, athlete networks, airport passage networks, etc. Social networks have increasingly demonstrated their strength due to their large number of participants and real-time information dissemination capability. Social computing has become a promising research area and attracts much attention. Analyzing and mining human behavior, topological structure and information diffusion in social networks can help to understand the essential mechanism of macroscopic phenomena, discover potential public interest, and provide early warnings of collective emergencies.

In the past, to study the characteristics of social networks, questionnaires were designed, and volunteers in the network were invited to complete questionnaires. However, the amount of data collected from questionnaires was not enough to understand the whole perspective and essential mechanism of social events. With the development of mobile sensing, computer networks and artificial intelligence in recent years, it is possible to collect an abundance of data from various social multimedia. Big data in social networks also bring challenges in how to process social data and investigate human behavior. In addition, there are new and complex features in social networks, such as heterogeneous human properties, dynamic network structures and random interpersonal interactions. Therefore, advanced multidisciplinary data collection and data mining methods should be proposed for social computing and developed to study social networks.

This Special Section in IEEE Access welcomes contributions in the quickly growing field of social computing. We encourage articles with multidisciplinary methods for social data mining. The related disciplines include machine learning, information theory, mathematics, computational and statistical physics.

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

  • Network representation learning
  • Streaming social data processing
  • Heterogeneous social network mining
  • Behavior analysis of social networks
  • Social multimedia and image processing
  • Social text analysis
  • Deep learning in social computing
  • Behavior analysis on social networks
  • Pattern recognition of behaviors
  • Human sentiment mining and analysis
  • Individual interest modeling
  • Personalized recommender systems
  • Knowledge graph and its applications
  • Essential mechanism of information diffusion and control
  • Modeling the formation and phase transition of collective phenomena
  • Trend prediction of information propagation
  • Modeling and analysis of interpersonal interactions
  • Multimedia data analysis

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

 

Associate Editor:  Yongqiang Zhao, Northwestern Polytechnical University, China

Guest Editors:

  1. Shirui Pan, Monash University, Australia
  2. Jia Wu, Macquarie University, Australia
  3. Huaiyu Wan, Beijing Jiaotong University, China
  4. Huizhi Liang, University of Reading, United Kingdom
  5. Haishuai Wang, Fairfield University, USA
  6. Huawei Shen, Chinese Academy of Sciences, China

 

Relevant IEEE Access Special Sections:

  1. Applications of Big Data in Social Sciences
  2. AI-Driven Big Data Processing: Theory, Methodology, and Applications
  3. Privacy Preservation for Large-Scale User Data in Social Networks


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: zhaoyq@nwpu.edu.cn.

.