Machine Learning Designs, Implementations and Techniques

Submission Deadline: 15 February 2020

IEEE Access invites manuscript submissions in the area of Machine Learning Designs, Implementations and Techniques.

Most modern machine learning research is devoted to improving the accuracy of prediction. However, less attention is paid to deployment of machine and deep learning systems, supervised /unsupervised techniques for mining healthcare data, and time series similarity and irregular temporal data analysis. Most deployments are in the cloud, with abundant and scalable resources, and a free choice of computation platform. However, with the advent of intelligent physical devices—such as intelligent robots or self-driven cars—the resources are more limited, and the latency may be strictly bounded.

To address these questions, the focus of this Special Section in IEEE Access is on machine and deep learning designs, implementations and techniques, including both system level topics and other research questions related to the general use and framework of machine learning algorithms.

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

  • Real time implementation of machine and deep learning,
  • System level implementation, considering full pipeline from raw data until the decision layer
  • Novel and innovative applications with strong emphasis on design and implementation
  • Novel approaches for Temporal / Spatial/Spatio-Temporal Association analysis
  • Pattern discovery from Time stamped Temporal and Interval databases
  • High performance data mining in cloud
  • Novel approaches for handling Uncertain and Imbalanced data
  • Supervised/Unsupervised techniques for mining healthcare data
  • Deep learning for translational bio-informatics
  • Periodic/Sequential pattern mining
  • Evolutionary algorithms
  • Privacy-Preserving Data mining
  • Time series similarity and Irregular temporal data analysis
  • Mining Text Web and Social network data
  • Imputation techniques for Temporal data
  • Causality and Event Processing
  • Applications of Data Mining in Anomaly and Intrusion detection
  • Applications to medical 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:  Shadi A. Aljawarneh, Jordan University of Science and Technology, Jordan

Guest Editors:

    1. Oguz Bayat, Altinbas University, Turkey
    2. Juan A. Lara, Madrid Open University, Udima, Spain
    3. Robert P. Schumaker, University of Texas at Tyler, USA

 

Relevant IEEE Access Special Sections:

  1. Visual Analysis for CPS Data
  2. Emerging Approaches to Cyber Security
  3. Data-Enabled Intelligence for Digital Health


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:  saaljawarneh@just.edu.jo, shadi.jawarneh@yahoo.com.

Utility Pattern Mining: Theoretical Analytics and Applications

Submission Deadline: 30 April 2020

IEEE Access invites manuscript submissions in the area of Utility Pattern Mining: Theoretical Analytics and Applications.

Utility pattern mining from data has received a lot of attention from the Knowledge Discovery in Data Mining (KDD) community due to the high potential impact in many applications such as finance, biomedicine, manufacturing, e-commerce and social media. Current research in utility mining primarily focuses on discovering patterns of high value (e.g. high profit) in large databases, and analyzing/learning important factors (e.g. economic factors) in a data mining process. One of the popular applications of utility mining is the analysis of large transactional databases to discover high-utility itemsets, which consist of sets of items that generate a high profit when purchased together.

This Special Section in IEEE Access aims at bringing together academic and industrial researchers and practitioners from data mining, machine learning and other interdisciplinary communities, in a collaborative effort to identify and discuss major technical challenges, recent results and potential topics on the emerging fields of Utility-Pattern Mining, by focusing especially on theoretical analytics and applications. Studies about real-world experiences, inherent challenges, and new research methods/applications are also welcome.

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

  • Theory, applications, and core methods for utility mining and computing
  • Utility patterns mining in large datasets, e.g., high-utility itemset mining, high-utility sequential pattern/rule mining, high-utility episode mining, and other novel patterns
  • Analysing and learning utility factors of mining and learning processes
  • Predictive modeling/learning, clustering and link analysis that incorporate utility factors
  • Incremental utility mining and computing
  • Utility mining and learning in streams
  • Utility mining and learning in uncertain systems
  • Utility mining and learning in big data
  • Knowledge representations for utility patterns
  • Privacy preserving utility mining/learning
  • Visualization techniques for utility mining/computing
  • Open-source software/libraries/platform
  • Innovative applications in interdisciplinary domains like finance, biomedicine, healthcare, manufacturing, e-commerce, social media, education, etc.
  • New, open, or unsolved problems in utility-pattern mining and computation

 

This Special Section is also published in cooperation with the second “Utility-Driven Mining and Learning (UDML)” workshop held at IEEE ICDM 2019. Important articles from the UDML workshop will be invited for this Special Section, provided that each article has less than 35% similarity to the author’s previous work.

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

 

Associate Editor:  Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway

Guest Editors:

    1. Philippe Fournier-Viger, Harbin Institute of Technology (Shenzhen), China
    2. Vincent S. Tseng, National Chiao Tung University, Taiwan
    3. Philip Yu, University of Illinois at Chicago, USA

 

Relevant IEEE Access Special Sections:

  1. Innovation and Application of Intelligent Processing of Data, Information and Knowledge as Resources in Edge Computing
  2. Data-Enabled Intelligence for Digital Health
  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:  jerrylin@ieee.org.

Feature Representation and Learning Methods With Applications in Large-Scale Biological Sequence Analysis

Submission Deadline: 01 August 2020

IEEE Access invites manuscript submissions in the area of Feature Representation and Learning Methods With Applications in Large-Scale Biological Sequence Analysis.

In recent years, with the rapid growth of biological data (especially biological sequences in the bioinformatics field) data-driven computational methods are increasingly needed to quickly and accurately analyze large-scale biological data.  Machine learning has recently emerged as an “intelligent” method in many specific bioinformatics areas, such as identification of DNA mutations, and protein post-transcriptional modifications, etc. Methods to find discriminative feature representation for biological sequences is quite important, as it greatly impacts the performance of machine learning methods. A good representation is often the one that captures the discriminative information from the data and supports effective machine learning. As a result, it is fundamentally important for the development of new feature representation approaches in machine learning. Over the last few decades, feature representation engineering in different fields has been well studied; however, most are labor intensive and heavily dependent on the professional knowledge of researchers, highlighting the weakness of current learning algorithms. To expand the scope and ease of the applicability of machine learning, it is highly desirable to make learning algorithms less dependent on handcrafted feature engineering so that novel applications could be constructed faster, and more importantly, to make progress toward artificial intelligence (AI).

This Special Section in IEEE Access will target recent feature representation and learning techniques in bioinformatics applications. Applications on large-scale biological data are strongly encouraged. We also encourage authors to contribute their codes and experimental data so they are available to the public, which would make our Special Section more infusive and attractive.

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

  • Supervised learning algorithms with applications in large-scale sequence analysis
  • Unsupervised learning algorithms with applications in large-scale sequence analysis
  • Imbalanced learning algorithms for biological sequence data
  • Multi-view feature learning for DNA or Protein classification
  • Deep learning-based feature learning strategies for biological data
  • Feature representation optimization algorithms with applications on bioinformatics.
  • Handcrafted feature representation algorithms for bioinformatic application

 

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

 

Associate Editor:  Quan Zou, University of Electronic Science and Technology of China, China

Guest Editors:

    1. Leyi Wei, University of Tokyo, Japan
    2. Qin Ma, Ohio State University, USA
    3. Jijun Tang, University of South Carolina, USA
    4. Dariusz Mrozek, Silesian University of Technology, Poland
    5. Guobao Xiao, Minjiang University, China

 

Relevant IEEE Access Special Sections:

  1. Scalable Deep Learning for Big Data
  2. Deep Learning Algorithms for Internet of Medical Things
  3. Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts


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: zouquan@tju.edu.cn.

Big Data Technology and Applications in Intelligent Transportation

Submission Deadline: 31 March 2020

IEEE Access invites manuscript submissions in the area of Big Data Technology and Applications in Intelligent Transportation.

Intelligent transportation is an emerging trending topic in the frontier of world transportation development. It relies on the integration of transport infrastructure and vehicle technology, and aims at building a safe, convenient, efficient and green transportation system through the integration of modern information, communication and control. It includes four main parts: intelligent transport, intelligent vehicle, intelligent traffic management, intelligent plan and applications.

With the improvement of the Internet of Vehicles, road network monitoring, navigation systems, logistics supervision and other platforms, the sources and categories of traffic big data have increased in recent years. Massive multi-source traffic big data provides valuable data resources for intelligent traffic management. Based on big data, emerging technologies such as cloud computing and artificial intelligence make traffic more intelligent, green and safe.

This Special Section in IEEE Access aims to provide researchers and practitioners a platform to present innovative solutions based on Big Data Technology and Applications. The focus of this Special Section is to address the current research challenges by encouraging submissions related to the advanced Big Data Technology and Applications in Intelligent Transportation System.

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

  • Urban Traffic Big Data
  • Road Network Travel Big Data Analysis
  • Rail Traffic Big Data Analysis
  • Civil Aviation Big Data Application
  • Big Data and Traffic Safety
  • Big Data and Traffic Optimization
  • Big Data in Ports, Waterways, Inland Navigation, and Vessel Traffic Management
  • Big Data in Detection of Vulnerable Road Users and Animals
  • Big Data and Air, Road, and Rail Traffic Management
  • Big Data in ITS User Services
  • Big Data in Emergency Management
  • Big Data in Transportation Electrification
  • Big Data in Emissions, Noise, Environment
  • Big Data in Management of Exceptional Events
  • Big Data in Intelligent Logistics
  • Big Data in Sensing, Detectors and Actuators
  • Big Data in Intelligent Vehicles
  • Big Data in Vision, and Environment Perception
  • Smart Mobility
  • Shared Mobility
  • Big Data in Safety Systems
  • Testing for Big Data Application in ITS

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

 

Associate Editor:  Sabah Mohammed, Lakehead University, Canada

Guest Editors:

    1. Xiaobo QU, Chalmers University of Technology, Sweden
    2. Hamid Arabnia, The University of Georgia, USA
    3. Jiandong Zhao, Beijing Jiaotong University, China
    4. Dalin Zhang, Beijing Jiaotong University, China
    5. Tai-Hoon Kim, University of Tasmania, Australia, Beijing Jiaotong University, China

 

Relevant IEEE Access Special Sections:

  1. Data Mining for Internet of Things
  2. Urban Computing and Intelligence
  3. Distributed Computing Infrastructure for Cyber-Physical Systems


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: sabah.mohammed@lakeheadu.ca.

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 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.

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Artificial Intelligence Technologies for Electric Power Systems

Submission Deadline: 31 December 2019

IEEE Access invites manuscript submissions in the area of Artificial Intelligence Technologies for Electric Power Systems.

As the main energy supply system and the most complicated artificial system, the electric power system is undergoing revolutionary changes, including high-penetration renewable energy resources, complicated networks with tremendous data communications, and numerous power devices with the feature of bi-directional energy flow. Developing an intelligent power and energy system is becoming more and more urgent to promote the power production and consumption revolution and construct a clean, low-carbon, safe, and efficient energy system. Currently, artificial intelligence, as a newly developed scientific technology used to imitate, stretch, and extend the theory, method, technology, and application of human intelligence, is providing a great support for promoting the intelligence revolution of power and energy system. Artificial intelligence technology with attractive features such as deep learning, cross-border integration, man-machine cooperation, open group intelligence, and autonomous control shows the strong handling capacity in perceptual intelligence, computational intelligence, and cognitive intelligence, which shows great potential in reshaping the way of producing and utilizing the electrical energy. In particular, the combination of artificial intelligence with cloud computing, big data, internet of things (IoT), and mobile interconnection can endow the power system with features of intelligent interaction, safety, and controllability. Thus, the security, reliability, and flexibility of the power grid can be significantly improved. The revolution of the power and energy system can be highly sped up.

The goal of this Special Section in IEEE Access is to welcome the latest research in the area of Artificial Intelligence Technologies for Electric Power Systems. Reviews, surveys and traditional research articles are welcome to submit to this Special Section.

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

  • Image recognition technology and its application in power system security management
  • Intelligent optimization and its application in power system planning, market trade, and dispatch
  • Intelligent electric power equipment
  • Big-data-based intelligent prediction and assistant decision-making
  • Intelligence integration of renewable energy resources
  • Artificial-intelligence-based power management and consumption
  • Application of artificial intelligence in power system security and stability
  • Artificial-intelligence-based power equipment maintenance plan

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

 

Associate Editor:  Canbing Li, Hunan University, China

Guest Editors:

  1. Hui Liu, Guangxi University, China
  2. Long Zhou, Guangdong Power Grid Co., Ltd, China
  3. Sheng Huang, Technical University of Denmark, Denmark
  4. Mingjian Cui, Southern Methodist University, USA
  5. Wuhui Chen, Jiangsu University, China
  6. Cong Zhang, Hunan University, China
  7. Jin Ma, The University of Sydney, Australia

 

Relevant IEEE Access Special Sections:

  1. Software Defined Networks for Energy Internet and Smart Grid Communications
  2. Artificial Intelligence and Cognitive Computing for Communications and Networks
  3. AI-Driven Big Data Processing: Theory, Methodology, and Applications


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: lcb@hnu.edu.cn.

Most Cited Article of 2017: Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study

Fog computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, to date, have been considered standing-alone. However, because of their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the IoE, Smart City, Industry 4.0, and Big Data Streaming, while introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE and then we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterward, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only device-to-device links to establish inter-thing “ad hoc” communication. Last, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects.

View this article on IEEE Xplore

Visual Analysis for CPS Data

Submission Deadline: 31 March 2020

IEEE Access invites manuscript submissions in the area of Visual Analysis for CPS Data.

Ubiquitous sensing technologies, social media and large-scale computing infrastructures have produced a variety of CPS (cyber-physical-social) data, e.g., Twitter/WeChat posts, human mobility, car trajectories, phone calls, WeChat connections, and geographical data. Analyzing CPS data can provide solutions for green, high-efficient and intelligent production and lifestyles. However, CPS data is usually massive, heterogeneous and distributed, and consequently cannot be analyzed effectively by analysts with traditional data processing techniques. The goal of Visual Analysis for CPS Data is to develop methods and tools that can help analysts understand and utilize CPS data to gain insight and make decisions in an interactive and iterative way. To facilitate management of CPS-relevant future applications, visual encodings, visual interfaces and visual interactions are essential components that integrate (or combine) human intelligence with machine intelligence.

The Special Section in IEEE Access on “Visual Analysis for CPS Data” of IEEE Access aims to address issues related to the representation, visual design, visual mapping, interaction, analysis and applications of multi-variate and time-varying data collected in a CPS system (e.g., smart city, MOOCs, smart factory, ITS). We solicit articles describing frameworks, theories, approaches, and techniques from visualization, visual data mining and visual analysis for designing, building and managing CPS systems.

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

  • Visual representation, visual design, visual interaction, visual reasoning, visual decision-making for CPS data
  • Visualization theories and visual analysis models for CPS systems and applications
  • Novel visual data mining, visual machine learning pipelines for CPS data, and applications, surveys, and evaluation approaches of visual-assisted CPS systems
  • Visualization and visual analysis theories for CPS data analysis
  • Visual representations and interaction techniques for CPS data analysis
  • Novel visual data mining and visual machine learning pipelines for CPS data analysis
  • Visual-assisted CPS data management and knowledge representation
  • Visual-supported modeling, planning and decision-making for CPS systems and applications
  • Collections, benchmarking and evaluations for visual analysis of CPS data
  • Surveys of visual-assisted CPS systems and application

 

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

 

Associate Editor:  Shuiguang Deng, Zhejiang University, China

Guest Editors:

  1. Wei Chen, Zhejiang University, China
  2. Ye Zhao, the Kent State University, USA
  3. Xinheng (Henry) Wang, University of West London, UK
  4. Panpan Xu, Bosch Research North America, USA

 

Relevant IEEE Access Special Sections:

  1. Applications of Big Data in Social Sciences
  2. Advanced Software and Data Engineering for Secure Societies
  3. AI-Driven Big Data Processing: Theory, Methodology, and Applications


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: dengsg@zju.edu.cn.

Most Cited Article of 2017: A Survey on Software-Defined Wireless Sensor Networks: Challenges and Design Requirements

Software defined networking (SDN) brings about innovation, simplicity in network management, and configuration in network computing. Traditional networks often lack the flexibility to bring into effect instant changes because of the rigidity of the network and also the over dependence on proprietary services. SDN decouples the control plane from the data plane, thus moving the control logic from the node to a central controller. A wireless sensor network (WSN) is a great platform for low-rate wireless personal area networks with little resources and short communication ranges. However, as the scale of WSN expands, it faces several challenges, such as network management and heterogeneous-node networks. The SDN approach to WSNs seeks to alleviate most of the challenges and ultimately foster efficiency and sustainability in WSNs. The fusion of these two models gives rise to a new paradigm: Software defined wireless sensor networks (SDWSN). The SDWSN model is also envisioned to play a critical role in the looming Internet of Things paradigm. This paper presents a comprehensive review of the SDWSN literature. Moreover, it delves into some of the challenges facing this paradigm, as well as the major SDWSN design requirements that need to be considered to address these challenges.

View this article on IEEE Xplore