Most Cited Article of 2017: A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures, which bring network functions and contents to the network edge, are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks, including definition, architecture, and advantages. Next, a comprehensive survey of issues on computing, caching, and communication techniques at the network edge is presented. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks, such as cloud technology, SDN/NFV, and smart devices are discussed. Finally, open research challenges and future directions are presented as well.

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Most Popular Article of 2017: Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges

Voluminous amounts of data have been produced, since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This paper investigates the state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this paper adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Finally, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions.

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Most Popular Article of 2017: Disease Prediction by Machine Learning Over Big Data From Healthcare Communities

With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. We experiment the modified prediction models over real-life hospital data collected from central China in 2013-2015. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We experiment on a regional chronic disease of cerebral infarction. We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital. To the best of our knowledge, none of the existing work focused on both data types in the area of medical big data analytics. Compared with several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94.8% with a convergence speed, which is faster than that of the CNN-based unimodal disease risk prediction algorithm.

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Emerging Approaches to Cyber Security

Submission Deadline: 30 April 2020

IEEE Access invites manuscript submissions in the area of Emerging Approaches to Cyber Security.

The Internet has become a key feature for any business activity. Criminal activity is no exception. Some crimes prior to the Internet, such as theft and scams, have now found the perfect tool for developing their activities- the Internet. The internet allows criminals to hide their real identity and to execute several kinds of offenses (e.g. to sell drugs, to sell private information, child pornography, etc.) and all this is thanks to the possibility of purchasing, in different black markets, specific and advanced tools to facilitate these activities for a low risk and a low economic investment.

In recent years, Internet Crime (e-Crime) has changed its business model, becoming more professional. The most skilled criminals offer their services to other criminals with less IT skills. This is known as CaaS (Crime-as-a-Service). Criminals often offer their skills in forums and markets of the Deep Web and the Dark Net, where advanced anonymization techniques are used to allow users to communicate freely without being traced. In these sites, potential clients can find many types of solutions for illegal activities. For instance, they find software kits, which allow less skilled criminals to infect thousands of computers to steal sensitive information, such as online bank credentials and credit card details.

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

  • Computational and artificial intelligence
  • Internet of Things (IoT)
  • Big Data
  • Social implications of technology
  • Information Security
  • Advances in Traditional System Forensic Methods
  • Multimedia and Artifact Analysis
  • Emerging Approaches to Cyber security
  • Incident Response and Malware Analysis
  • SCADA Forensics and Critical Infrastructure Protection
  • Digital Forensic Science
  • Cyber Crime Law, Psychology and Economics

 

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

 

Associate Editor: Luis Javier García Villalba, Universidad Complutense de Madrid, Spain

Guest Editors:

  1. Ana Lucila Sandoval Orozco, University of Brasilia, Brazil
  2. Mario Blaum, IBM Almaden Research Center, CA, USA
  3. Tai-Hoon Kim, Sungshin Women’s University, South Korea

 

Relevant IEEE Access Special Sections:

  1. Advanced Software and Data Engineering for Secure Societies
  2. Challenges and Opportunities of Big Data Against Cyber Crime
  3. Security Analytics and Intelligence for Cyber Physical Systems


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: javiergv@fdi.ucm.es

Security and Privacy for Cloud and IoT

Submission Deadline: 28 February 2019

IEEE Access invites manuscript submissions in the area of Security and Privacy for Cloud and IoT.

Internet of Things (IoT), which enables a wide variety of embedded devices, sensors and actuators (known as smart things) to interconnect and exchange data, is a promising network scenario for bridging the physical devices and virtual objects in the cyber world. By considering the limited capacity of smart things, cloud computing is naturally introduced to store and process the huge amount of data collected by the IoT. The appropriate integration of cloud computing and IoT can be regarded as the best of two worlds by simultaneously providing omnipresent sensing services and powerful processing capabilities. Undoubtedly, cloud-assisted IoT will boost the advancement of innovative applications and services including smart cities, industrial IoT, intelligent transportation and electronic health systems. In spite of the benefits brought from cloud-aided IoT, it is impossible to overlook the significance of security and privacy in this kind of highly heterogeneous and inter-connected system. To deal with security threats to smart devices and sensitive data, hundreds of security solutions have recently been put forward separately for the cloud or IoT environment. However, a few important characteristics such as heterogeneity and scalability haven’t been properly considered in these solutions.

The objective of the Special Section in IEEE Access is to compile recent research efforts dedicated to study the security and privacy of rapidly increasing cloud and IoT applications. The Special Section solicits high quality and unpublished work on recent advances in new methodologies empowering traditional security solutions for cloud and IoT, and theories and technologies proposed to defend cloud and IoT-oriented applications against adversarial or malicious attacks.

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

  • Security and protection architectures for cloud and IoT
  • Threat models and attack methodologies for cloud and IoT
  • Security applications and management of cloud and IoT
  • Challenges related to security and privacy for cloud and IoT
  • Data analysis of IoT and cloud for security and privacy
  • Cryptography protocols and algorithms for cloud and IoT
  • State-of-the-art reviews on security and privacy for cloud and IoT
  • Energy efficient solutions for cloud and IoT security and privacy

 

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

 

Associate Editor: Kuo-Hui Yeh, National Dong Hwa University, Taiwan

Guest Editors:

  1. Kuan Zhang, University of Nebraska-Lincoln, USA
  2. SK Hafizul Islam, Indian Institute of Information Technology Kalyani, India
  3. Weizhi Meng, Technical University of Denmark, Denmark
  4. Ennan Zhai, Alibaba Damo Academy, USA

 

Relevant IEEE Access Special Sections:

  1. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
  2. Internet-of-Things (IoT) Big Data Trust Management
  3. Towards Service-Centric Internet of Things (IoT): From Modeling to Practice


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: khyeh@gms.ndhu.edu.tw

Data-Enabled Intelligence for Digital Health

Submission Deadline: 15 September 2019

IEEE Access invites manuscript submissions in the area of Data-Enabled Intelligence for Digital Health.

The worldwide increase in the aging population presents an urgent need for new technologies to improve the quality of life for the elderly. In recent years we have seen rapid development of healthcare technologies along with the widespread use of Internet, mobile technologies, data analytics and artificial intelligence in healthcare. These developments have resulted in highly multi-disciplinary research in digital health and smart health, and have also driven the move towards more personalized care.

Digital health aims to apply data sciences, machine learning, artificial intelligence and the internet of things to tackle the health problems and challenges faced by patients and the care professionals. For example, tracking personalized health indicators regularly such as blood pressure, heart rate and others can help with the management of the health and well-being of patients with heart issues.

New technologies developed in the digital industry, particularly in the emerging interfacing area between big data and artificial intelligence, are changing the way healthcare is delivered and can have an enormous economic impact on healthcare provision. We are experiencing extensive research in health care including the development of new smart sensing, new algorithms, and new systems or devices for personalized healthcare. One of the fundamentals of these developments is to ensure that healthcare data can be accessed and analyzed effectively in order to support accurate decision-making. Most digital health system design has been focused on the functionalities defined by the domain expertise. For these types of systems, user experience and effectiveness of the systems will very much depend on the users’ knowledge of the system. This can be a challenging issue for personalized healthcare, particularly for users with disabilities and in an aging society.

Extensive research is currently taking place worldwide in the related areas, which in return raises new scientific questions as well as practical issues; for example (1) what will the next generation of Artificial Intelligence (AI) provide for us to achieve a better quality of life, particularly for our aging society? (2) How can healthcare systems be data-enabled to exploit a learning capability and fit in personal needs? (3) How can data-enabled technologies support effective human-machine cooperation and adapt to each other, and ultimately support humans and machines to work together? and (4) How can human-machine cooperation drive new intelligence to improve the quality of life for people in the healthcare systems?

This Special Section in IEEE Access aims to attract original research articles that advance the state of the art in digital health as well as data science and artificial intelligence. The goal is that it provides an opportunity for us to gain a significantly better understanding of the current developments and the future direction of artificial intelligence and data science in relation to healthcare.

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

  • New technologies and frameworks that support human-machine interaction and human-machine collaborative intelligence
  • Brain-Computer modeling for human-machine cooperation
  • Cognitive computing for healthcare and data intelligence
  • Brain-Computer modeling for cognitive intelligence
  • The design and implementation of personalized healthcare systems
  • The value and challenges of human-machine collaboration in healthcare
  • Data Science and artificial intelligence in digital health, and health management
  • Data science and artificial intelligence in public health
  • Machine learning to understand human behavior and well-being
  • New algorithms for medical and healthcare data analytics
  • Predictive analysis in personalized healthcare
  • Intelligent and predictive analytics for early warning, feedback and in-time intervention for personalized healthcare
  • The cutting edge development of digital health
  • New digital technologies to assist mental healthcare
  • New technology to enable personal data security and effective use in healthcare

 

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

 

Associate Editor:  Yonghong Peng, University of Sunderland, UK

Guest Editors:

  1. Wenbing Zhao, Cleveland State University, United States
  2. Yongtao Hao, Tongji University, China
  3. Yongqiang Cheng, University of Hull, United Kingdom
  4. Linbo Qing, Sichuan University, China
  5. Weihong Huang, Xiangya Hospital, China
  6. Ying Song, West China Hospital, China

 

Relevant IEEE Access Special Sections:

  1. Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications
  2. Mobile Multimedia for Healthcare
  3. Healthcare Big Data


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: Yonghong.Peng@Sunderland.ac.uk

Most Popular Article of 2017: Machine Learning With Big Data: Challenges and Approaches

The Big Data revolution promises to transform how we live, work, and think by enabling process optimization, empowering insight discovery and improving decision making. The realization of this grand potential relies on the ability to extract value from such massive data through data analytics; machine learning is at its core because of its ability to learn from data and provide data driven insights, decisions, and predictions. However, traditional machine learning approaches were developed in a different era, and thus are based upon multiple assumptions, such as the data set fitting entirely into memory, what unfortunately no longer holds true in this new context. These broken assumptions, together with the Big Data characteristics, are creating obstacles for the traditional techniques. Consequently, this paper compiles, summarizes, and organizes machine learning challenges with Big Data. In contrast to other research that discusses challenges, this work highlights the cause-effect relationship by organizing challenges according to Big Data Vs or dimensions that instigated the issue: volume, velocity, variety, or veracity. Moreover, emerging machine learning approaches and techniques are discussed in terms of how they are capable of handling the various challenges with the ultimate objective of helping practitioners select appropriate solutions for their use cases. Finally, a matrix relating the challenges and approaches is presented. Through this process, this paper provides a perspective on the domain, identifies research gaps and opportunities, and provides a strong foundation and encouragement for further research in the field of machine learning with Big Data.

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Proximity Service (ProSe) Challenges and Applications

Submission Deadline: 31 December 2018

IEEE Access invites manuscript submissions in the area of Proximity Service (ProSe) Challenges and Applications.

The mobile revolution is changing the way we interact with people and things around us. Proximity awareness, the ability to actively/passively and continuously search for relevant value in one’s physical proximity, is at the core of this phenomenon.

Generally, Proximity Service (ProSe) can be composed of two main groups of use cases: public safety communications and discovery mode (commercial applications). On one hand, the ability to support direct communication is a core requirement for public safety use cases, when the devices are in proximity, and the network is down or when the device is out of coverage (e.g., in the situations of disaster rescue), as it may take too much time to install new communication equipment and restore damaged infrastructure. On the other hand, much more than just a “friend finder”, commercial discovery mode could establish a paradigm shift from the Personal Computer (PC) “search-to-discover” mindset, to “always-on” discovery services that are fundamental to defining the next generation of mobile service.

Existing technologies used to serve the proximity awareness can be broadly divided into over-the-top (OTT), and Device-to-Device (D2D) (peer-to-peer (P2P)) solutions. In the OTT model, a server located in the cloud receives periodic location updates from user mobile devices. Besides the underlying enabling novel technologies, the latest application and research results of ProSe in academic, industrial fields and standardization should be analyzed and designed. In this Special Section in IEEE Access, we solicit articles from researchers in the field to comprehensively present architecture, fundamental issues and applications in ProSe networking environment from interdisciplinary viewpoints.

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

  • D2D Communications technologies
  • Peer and Service Discovery in ProSe
  • Forwarding mechanism in ProSe
  • Profile matching in ProSe
  • ProSe development framework
  • Energy-efficient technologies in ProSe
  • Security, trust, privacy in ProSe
  • Smart sensors and sensor systems for ProSe
  • Wireless networks with improved responsiveness for ProSe
  • Human-computer Interface (HCI) for ProSe
  • Human robot interaction for ProSe
  • Activity recognition
  • Indoor localization and tracking systems
  • Incentive mechanism (mobile participatory sensing)
  • Business models of proximity service
  • Mobile Social Networks in proximity (MSNP)
  • Vehicular social Networks (VSN)
  • Mobile crowdsourcing applications
  • People-centric computing

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

 

Associate Editor: Yufeng Wang, Nanjing University of Posts and Telecommunications, China

Guest Editors:

  1. Qun Jin, Waseda University, Japan
  2. Jianhua Ma, Hosei University, Japan
  3. Klimis Ntalianis, Athens University of Applied Sciences, Greece
  4. Md Zakirul Alam Bhuiyan, Fordham University, USA
  5. Michele Luvisotto, ABB Corporate Research, Sweden

 

Relevant IEEE Access Special Sections:

  1. Human-Centered Smart Systems and Technologies
  2. Advanced Big Data Analysis for Vehicular Social Networks
  3. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things


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:  wfwang@njupt.edu.cn

Urban Computing & Well-being in Smart Cities: Services, Applications, Policymaking Considerations

Submission Deadline: 31 July 2019

IEEE Access invites manuscript submissions in the area of Urban Computing & Well-being in Smart Cities: Services, Applications, Policymaking Considerations.

Research on smart cities is maturing and the question of securing the well-being of cities’ inhabitants is attracting increased attention of researchers, practitioners, and policymakers. Urban computing occupies an important position, as advances in this domain will define the scope of possible developments and policymaking strategies in any city. Considering the challenges and opportunities cities/urban spaces generate, today the imperative is to examine how targeted research and cutting-edge innovation can be effectively communicated to all stakeholders. Thus, synergies emerging at the research-innovation-policymaking nexus can be exploited and thus city dwellers’ well-being can be enhanced. Urban computing serves as a framework that integrates increasingly sophisticated technologies pertinent to the Internet of Things (IoT). Therefore pervasive computing, big data analytics, crowdsourcing, and volunteered geographic information, including user behavior, brand popularity, recommender systems, and social media analytics bears the promise and potential that viable solutions to key problems and challenges specific to cities/urban spaces will be solved. The objective of this Special Section in IEEE Access is to examine this promise and potential from a variety of complementary interdisciplinary perspectives, including (but not limited to) computing/ICT, political economy, public policy, innovation, and entrepreneurship.

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

  • ICTs and their role in integrative knowledge management systems for smart cities.
  • Application of public policies aimed at boosting research and innovation for smart cities.
  • Smart generation of volunteered information to finance research and innovation promotion in smart cities.
  • Pervasive computing applied in the transformation from cities to smart cities.
  • Smart and collaborative mobile applications to analyze the human dynamics in big cities.
  • The role of cryptocurrency technology for Social Economic Growth in smart cities.
  • Case studies based on IoT and Big Data Analytics technologies applied to smart cities.
  • Enhancement and strategic development of skills and competencies for the required digital transformation to develop public policy making.
  • Advanced computing approaches and systems for international business leadership in the context of smart cities.
  • Building of international innovation networks enabled by sound technological innovative applications for the sustainability of smart cities.
  • Blended (concept- and practice-driven) approaches to smart cities research
  • Smart and open data acquisition and processing
  • Pervasive and mobile computing to analyze the social impact in smart cities
  • Cloud computing for smart services inside smart cities
  • Smart healthcare applications in the development of public safety policies
  • Big Data Analytics to smart data from smart cities
  • Virtual and Augmented Reality applied to smart cities applications
  • Crowd-sensing with 5G sensors to smart cities
  • Nanotechnology applied to successful cases in smart cities
  • Cognitive computing to describe behavior in the knowledge society
  • Regulatory and policymaking considerations, including the role of international organizations in context of smart cities and their evolution

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

 

Associate Editor: Miltiadis D. Lytras, King Abdulaziz University, Saudi Arabia

Guest Editors:

  1. Ernesto Damiani, Khalifa University, Abu Dhabi, UAE
  2. Anna Visvizi, Effat University, Saudi Arabia
  3. Miguel Torres-Ruiz, Instituto Politécnico Nacional, Mexico
  4. Peiquan Jin, University of Science and Technology China, China

 

Relevant IEEE Access Special Sections:

  1. Social Computing Applications for Smart Cities
  2. Human-Centered Smart Systems and Technologies
  3. Key technologies for Smart Factory of Industry 4.0


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: mlytras@acg.edu

AI-Driven Big Data Processing: Theory, Methodology, and Applications

Submission Deadline: 31 March 2019

IEEE Access invites manuscript submissions in the area of AI-Driven Big Data Processing: Theory, Methodology, and Applications.

With the rapid development of network infrastructures and personal electronic devices, big data generated from Internet, sensing networks, and other equipment are rapidly growing, and have received increased attention in recent years. Big data consists of multisource content, for example, images, videos, audio, text, spatio-temporal data, and wireless communication data. Moreover, big data processing includes computer vision, natural language processing (NLP), social computing, speech recognition, data analysis in Internet of Vehicle (IoV), real-time data analysis in Internet of Things (IoT), and wireless big data processing.

Recently, artificial intelligence (AI)-driven big data processing technologies based on pattern recognition, machine learning, and deep learning, are intensively applied to deal with the large-scale heterogeneous data. However, challenges still exist in the development of AI-driven big data processing.

In computer vision and image processing, increasingly more databases and data streams have been transmitted and collected. One of the biggest challenges in the massive image/video data analysis is to develop energy efficient and real-time methods to extract useful information out of the colossal amount of data being generated every second. In speech signal processing, benefitting from the help of ‘Big Data’ and new AI technology, a lot of progress has also been made in speech processing area. How to build a condition robustness speech processing system using limited labeled data is still a direction to emphasize studying in the future.

In NLP, knowledge is an essential part of artificial intelligence. Many NLP tasks, such as opinion mining, question answering system, and dialog system need big data to get more knowledge to improve the system performance. How to effectively use the existing huge knowledge in NLP systems is still a hot research topic. In wireless communications, facing 5G and beyond systems with the increased antenna number, huge bandwidth and versatile application scenarios, the channel characteristics become more complex and hidden in big volume of data. Simultaneously, there will be a continuous increase in the wireless channel dimension which is already considerably large. In this Special Section in IEEE Access, we invite researchers to discuss the aforementioned challenges; analyzing and processing big data in a more effective and cost reducing way, discovering and understanding knowledge from the data, and generalizing and transferring the discovery into other application fields, are challenging problems to solve.

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

  • Foundations of machine learning and pattern recognition
  • Neural networks and deep learning
  • Image big data and computer vision
  • Natural language processing
  • Wireless big data processing
  • Speech big data analysis and speaker recognition
  • Platforms and systems, e.g., architecture design, hardware implementation

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

 

Associate Editor:  Zhanyu Ma, Beijing University of Posts and Telecommunications, China

Guest Editors:

  1. Sunwoo Kim, Hanyang University, Korea
  2. Pascual Martínez-Gómez, Amazon, US
  3. Jalil Taghia, Uppsala University, Sweden
  4. Yi-Zhe Song, Queen Mary University of London, UK
  5. Huiji Gao, LinkedIn, US

 

Relevant IEEE Access Special Sections:

  1. Big Data Learning and Discovery
  2. Multimedia Analysis for Internet-of-Things
  3. Advanced Data Analytics for Large-scale Complex Data Environments


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: mazhanyu@bupt.edu.cn