A Broad Ensemble Learning System for Drifting Stream Classification

In a data stream environment, classification models must effectively and efficiently handle concept drift. Ensemble methods are widely used for this purpose; however, the ones available in the literature either use a large data chunk to update the model or learn the data one by one. In the former, the model may miss the changes in the data distribution, while in the latter, the model may suffer from inefficiency and instability. To address these issues, we introduce a novel ensemble approach based on the Broad Learning System (BLS), where mini chunks are used at each update. BLS is an effective lightweight neural architecture recently developed for incremental learning. Although it is fast, it requires huge data chunks for effective updates and is unable to handle dynamic changes observed in data streams. Our proposed approach, named Broad Ensemble Learning System (BELS), uses a novel updating method that significantly improves best-in-class model accuracy. It employs an ensemble of output layers to address the limitations of BLS and handle drifts. Our model tracks the changes in the accuracy of the ensemble components and reacts to these changes. We present our mathematical derivation of BELS, perform comprehensive experiments with 35 datasets that demonstrate the adaptability of our model to various drift types, and provide its hyperparameter, ablation, and imbalanced dataset performance analysis. The experimental results show that the proposed approach outperforms 10 state-of-the-art baselines, and supplies an overall improvement of 18.59% in terms of average prequential accuracy.

View this article on IEEE Xplore


AI and IoT Convergence for Smart Health

Submission Deadline:  31 May 2021

IEEE Access invites manuscript submissions in the area of AI and IoT Convergence for Smart Health.   

With the development of smart sensorial media, things, and cloud technologies, “Smart healthcare” is getting remarkable attention from academia, government, industry, and  healthcare communities. Recently, the Internet of Things (IoT) has brought the vision of a smarter world into reality with a massive amount of data and numerous services. With the outbreak of COVID-19, Artificial Intelligence (AI) has gained significant attention by utilizing its machine learning algorithms for quality patient care. However, the convergence of IoT and AI can provide new opportunities for both technologies. AI-driven IoT can play a significant role in smart healthcare by offering better insight of healthcare data to support affordable personalized care. It can also support powerful processing and storage facilities of huge IoT data streams (big data) beyond the capability of individual “things,” as well as to provide automated decision making in real-time. While researchers have been making advances in the study of AI-and IoT for health services individually, very little attention has been given to developing cost-effective and affordable smart healthcare services. The AI-driven IoT (AIIoT) for smart healthcare has the potential to revolutionize many aspects of our healthcare industry; however, many technical challenges need to be addressed before this potential can be realized.

This Special Section is intended to report high-quality research on recent advances toward AI- and IoT convergence for smart healthcare, more specifically to the state-of-the-art approaches, methodologies, and systems for the design, development, deployment and innovative use of those convergence technologies to provide insight into smart healthcare service demands. Authors are solicited to submit complete articles, not previously published elsewhere, in the following topics. 

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

  • AI-empowered innovative classification techniques and testbeds for healthcare in IoT-cloud platform
  • AI- empowered big data analytics and cognitive computing for smart health monitoring
  • Advanced AIIoT convergent services, systems, infrastructure and techniques for healthcare
  • AI-supported IoT data analytics for smart healthcare
  • Machine learning-based smart homecare for mobile-enabled fall detection of disabled or elderly people
  • AIIoT-empowered data analysis for COVID-19
  • AI-enabled contact tracing for preventing the spread of the COVID-19
  • AI and IoT convergence for pandemic management and monitoring
  • Intelligent IoT-driven diagnosis and prognosis mechanisms for infectious diseases
  • IoT cloud-based predictive analysis for personalized healthcare
  • AI- supported healthcare in IoT-cloud platform
  • AIIoT- supported approaches and testbeds for social distance monitoring in pandemic prevention
  • Security, privacy, and trust of AI-IoT convergent smart healthcare system

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

 

Associate Editor:  M. Shamim Hossain, King Saud University, Saudi Arabia

Guest Editors:

    1. Stefan Goebel, Technical University Darmstadt, Germany
    2. Abdulsalam Yassine, Lakehead University, Canada
    3. Diana P. Tobón, Universidad de Medellín, Colombia
    4. Fakhri Karray, University of Waterloo, Canada

 

Relevant IEEE Access Special Sections:

    1. Deep Learning Algorithms for Internet of Medical Things
    2. Behavioral Biometrics for eHealth and Well-Being
    3. Emerging Deep Learning Theories and Methods for Biomedical Engineering

 

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: mshossain@ksu.edu.sa.

Intelligent Big Data Analytics for Internet of Things, Services and People

Submission Deadline:  30 June 2021

IEEE Access invites manuscript submissions in the area of Intelligent Big Data Analytics for Internet of Things, Services and People.   

In the envisaged future internet, which consists of billions of digital devices, people, services and other physical objects, people will utilize these digital devices and physical objects to exchange data about themselves and their perceived surrounding environments over a web-based service infrastructure, in what we refer to as the Internet of Things. Because of its openness, multi-source heterogeneity, and ubiquity, interconnecting things, services and people via the internet improves data analysis, boosts productivity, enhances reliability, saves energy and costs, and generates new revenue opportunities through innovative business models. However, the increasing number of IoT users and services leads to fast-growing IoT data, while the quality of service of IoT should also be maintained regardless of the number of IoT users and services. Therefore, the data transmission and processing in IoT should be performed in a more intelligent manner. A large number of computational intelligent technologies such as artificial neural networks, machine learning and data mining can be applied in IoT to improve the IoT data transmission and processing. The adoption of intelligence technologies and big data in handling IoT could offer a number of advantages as big data technology could handle various data effectively, while artificial intelligence technology could further facilitate capturing and structuring the big data.

This Special Section in IEEE Access will focus on intelligent big data analytics for advancing IoT. Novel applications by the integration of big data and artificial intelligence for IoT are particularly welcome.

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

  • Big-data analytics in IoT
  • Machine learning algorithms in IoT
  • Scalable/parallel/distributed algorithms in IoT
  • Privacy preserving and security approaches for large scale analytics in IoT
  • Big data technology for intelligent system
  • Artificial intelligence technology for data integration in IoT
  • Artificial intelligence technology for data mining in IoT
  • Artificial intelligence technology for data prediction in IoT
  • Artificial intelligence technology for data storage in IoT
  • Artificial intelligence technology for multimedia data processing
  • Intelligent optimization algorithms in IoT
  • Advances in artificial learning and their applications for information security
  • Intelligent big data analytics for prediction and applications in IoT
  • Novel applications of intelligent big data analytics for IoT
  • Big data technology for intelligent monitoring in IoT

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

 

  Associate Editor: Zhaoqing Pan, Nanjing University of Information Science and Technology, China

  Guest Editors:

    1. Yang Xiao, University of Alabama, USA
    2. Muhammad Khurram Khan, King Saud University, Saudi Arabia
    3. Markku Oivo, University of Oulu, Finland
    4. Vidyasagar Potdar, Curtin University, Australia
    5. Yuan Tian, Nanjing Institute of Technology, China

 

Relevant IEEE Access Special Sections:

    1. Scalable Deep Learning for Big Data
    2. Intelligent Systems for the Internet of Things
    3. Human-Centered Smart Systems and Technologies

 

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: zhaoqingpan@nuist.edu.cn.

Towards Smart Cities with IoT Based on Crowdsensing

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Towards Smart Cities with IoT Based on Crowdsensing.

The proliferation of Internet of Things (IoT) has paved the way for the future of smart cities. The large volume data over IoT can enable decision-making for various applications, such as smart transportation, smart parking and smart lighting. The key to the success of smart cities is data collection and aggregation over IoT. Recently, crowdsensing has become a new data collection paradigm over IoT, which can realize large-scale and fine-grained data collection with low cost for various applications. For example, we can leverage the power of the crowd to build a real-time noise map with the microphones on smartphones. Despite the advantages of crowdsensing and IoT, there are many challenges to utilize crowdsensing over IoT for smart cities, such as how to allocate tasks to appropriate users to provide high-quality sensing data, how to incentivize users to participate in crowdsourcing, how to detect the reliability of the crowdsourced data, and how to protect the privacy of users. This Special Section aims to solicit original research works that address the challenging problems in utilizing crowdsensing over IoT for smart cities.

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

  • Collaborative sensing and computing
  • Sparse mobile crowdsensing over IoT
  • Task allocation/selection in crowdsensing
  • Fair/long-term/quality-oriented incentive in crowdsensing
  • Data collection/aggregation in crowdsensing
  • Truth discovery in crowdsensing
  • Security, trust and privacy protection for IoT devices
  • Privacy-preserving data collection for crowdsensing
  • Fog computing and edge computing
  • IoT device monitoring and scheduling in smart city
  • Analysis for IoT-based Big Data
  • Vehicular crowdsensing networks in smart city
  • Resource management of IoT devices for smart city
  • Quality measurement of crowdsourced data for smart city
  • Crowdsourced data enabled applications in smart city

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

 

Associate Editor:   

Kun Wang, University of California, Los Angeles, USA
Zhibo Wang, Wuhan University, China

 

Guest Editors:

    1. Ye-Qiong Song, University of Lorraine, France
    2. Dejun Yang, Colorado School of Mines, USA
    3. Shibo He, Zhejiang University, China
    4. Wei Wang, Amazon Inc, USA

 

Relevant IEEE Access Special Sections:

  1. Urban Computing & Well-being in Smart Cities: Services, Applications, Policymaking Considerations
  2. Data Mining for Internet of Things
  3. Security, Privacy, and Trust Management in Smart Cities

 

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: zbwang@whu.edu.cn.

Key Enabling Technologies for Prosumer Energy Management

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Key Enabling Technologies for Prosumer Energy Management.

Distributed energy resources (DERs), such as photovoltaics, electric vehicles, energy storage and heat pump devices, play a central role in the energy transition from fossil fuels to renewables. The growing penetration of DERs has made it possible for traditional passive consumers to evolve into active prosumers. Compared with traditional consumers, prosumers are capable of managing their energy generation, storage and consumption simultaneously. Prosumers can not only participate in electricity market transactions, e.g., minimizing the cost of energy procurement, but also facilitate smart grid operations, e.g., providing ancillary service to power grids. With the booming development of prosumers, a prosumer energy management system is urgently needed to take full advantage of prosumers’ flexibility while taking the interests of other parties into account. In recent years, several prosumer energy management strategies have been proposed in literature, such as the peer-to-peer approach, coordinated scheduling-based scheme and centralized control method. However, these strategies have the following deficiencies: (1) they lack comprehensive analytics and intelligent control tools compatible with the existing energy management systems to reduce energy costs; (2) they do not address how to increase the prosumer profitability through improved customer segmentation; (3) they do not analyze the intrinsic revenue streams among prosumers.

To handle these deficiencies, the current energy management system needs to be rigorously re-engineered into an integrated and intelligent system that manages not only the smart grid but also the multi-energy system with couplings of electricity, thermal and natural gas networks. To this end, a large number of prosumers will actively participate in system-wide and local coordination tasks. Therefore, the modeling methods and related key enabling technologies are still hot topics that require substantial scientific research.

Research into prosumer energy management involves a wide range of disciplines, including power engineering, computer science, (micro) economics, thermal and control engineering. This Special Section will bring together researchers and practitioners to introduce and discuss key enabling technologies covering monitoring, operation, planning, marketing and control architectures related to the prosumer energy management.

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

  • Electricity market design for prosumer energy management
  • Prosumer-oriented home energy management system
  • Data management and ICT technologies to promote energy trading between prosumers
  • IoTs/Cloud based solutions for prosumer monitoring, management and control
  • Aggregation and disaggregation technologies for integrating and managing prosumers’ DERs
  • New coordinated control methodologies to integrate prosumers’ flexibility into smart grid operations
  • Automated technologies based on market behavior analysis to improve the robustness of prosumer energy management system
  • Market modeling methods based on peer to peer (P2P) energy trading and blockchain
  • Cyber physical modeling and cyber security of prosumer energy management system
  • Interactive energy management system that facilitates the prosumers’ operation
  • Transactive energy system for enabling the operation of prosumer energy management
  • Experiences and lessons learned from the field implementations
  • Renewable energy policies that can promote the development of prosumers in future smart grid
  • Standardization and new technologies that facilitate the application of prosumer energy management

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

 

Associate Editor:  Bin Zhou, Hunan University, China

Guest Editors:

  1. Nian Liu, North China Electric Power University, China
  2. Junjie Hu, North China Electric Power University, China
  3. Guangya Yang, Technical University of Denmark, Denmark
  4. Ahmad F. Taha, University of Texas, USA
  5. Huaizhi Wang, Shenzhen University, China
  6. Hugo Morais, EDF R&D Department, France
  7. Siqi Bu, The Hong Kong Polytechnic University, Hong Kong
  8. Jiayong Li, Hunan University, China

Relevant IEEE Access Special Sections:

  1. Artificial Intelligence Technologies for Electric Power Systems
  2. Emerging Technologies for Energy Internet
  3. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric 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: binzhou@hnu.edu.cn.

Visual Perception Modeling in Consumer and Industrial Applications

Submission Deadline: 31 May 2020

IEEE Access invites manuscript submissions in the area of Visual Perception Modeling in Consumer and Industrial Applications.

In recent literature, various visual perception mechanisms have been modeled to facilitate the relevant consumer and industrial applications, from low-level visual attention to higher-level quality of experience, object detection, and recognition. Specifically, visual attention can help us handle massive amounts of visual information efficiently, and visual attention modeling can help us simulate such visual attention mechanisms and focus on more salient information. Since the ultimate receiver of the processed signal is often human, the receiver’s perception of the overall quality is also very important, and quality perception modeling can help control the whole processing chain and guarantee a good perceptual quality of experience. Due to the rapid advancement of machine learning techniques, higher-level perception modeling related to semantics also becomes possible. How to utilize the most recent big data and learning techniques to interpret and model visual perception also becomes a problem. What’s more, a growing number of advanced multimedia technologies have become available over the last decade, such as High dynamic range (HDR) imaging, virtual reality (VR), augmented reality (AR), mixed reality (MR), and light field imaging. Visual perception modeling for such advanced multimedia technologies also needs further research. This Special Section solicits novel and high-quality articles to present reliable solutions and technologies of the above-mentioned problems.

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

  • Visual perception modeling for various consumer and industrial applications
  • Visual attention modeling, including the mechanism of visual attention, visual saliency prediction and the utilization of visual attention models in relevant applications
  • Visual quality of experience modeling, including visual quality assessment, control, and optimization for consumer electronics and industrial applications
  • Advanced learning technologies, such as deep learning, random forests (RF), multiple kernel learning (MKL), and their applications in visual perception modeling
  • Statistical analytics and modeling based on big data (cloud) for images, videos or other formats of industrial data
  • Emerging multimedia technologies, such as virtual reality (VR), augmented reality (AR), 4-dimensional (4-D) light fields, and high dynamic range (HDR), including visual perception modeling for these emerging technologies and their use in industry

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

 

Associate Editor: Guangtao Zhai, Shanghai Jiao Tong University, China

Guest Editors:

    1. Xiongkuo Min, The University of Texas at Austin, USA
    2. Vinit Jakhetiya, Indian Institute of Technology (IIT), Jammu, India
    3. Hamed Rezazadegan Tavakoli, Aalto University, Finland
    4. Menghan Hu, East China Normal University, China
    5. Ke Gu, Beijing University of Technology, China

 

Relevant IEEE Access Special Sections:

  1. Recent Advances in Video Coding and Security
  2. Biologically inspired image processing challenges and future directions
  3. Integrative Computer Vision and Multimedia Analytics


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: zhaiguangtao@sjtu.edu.cn.

Intelligent Logistics Based on Big Data

Submission Deadline: 20 May 2020

IEEE Access invites manuscript submissions in the area of Intelligent Logistics Based on Big Data.

The advent of the era of big data and the rapid development of e-commerce have provided a new development direction for the modern logistics industry, prompting the logistics industry to think more about data. In addition, the operation mode has gradually changed from the traditional extensive mode to the intelligent logistics one, characterized by information, data, sharing and intelligence.

Intelligent logistics based on big data has significantly improved the intelligence level of warehousing, transportation and distribution, including the intelligent location of logistics outlets, the optimal configuration of transportation routes, the highest loading rate of transportation vehicles, and the optimal distribution of the last mile, which can be used to explore greater potential business value through massive logistics data analysis.

The goal of this Special Section in IEEE Access is to provide a specific opportunity to review the state-of-the-art of intelligent logistics in big data, and bring together researchers in the relevant areas to share the latest progress, novel methodologies and potential research topics.

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

  • Design and development of intelligent logistics system
  • Data collection and knowledge management for intelligent logistics based on Big Data
  • Analysis of intelligent logistics mode based on Big Data
  • Development of smart logistics systems using Big Data
  • Emergency logistics modeling and optimization based on Big Data
  • Optimal design of manufacturing/remanufacturing logistics network
  • Data-driven-based intelligent logistics management methods & technologies
  • Internet-of-things-based intelligent logistics design and optimization
  • Environment analysis of reverse logistics based on Big Data
  • Modeling of network design for intelligent logistics using Big Data

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

 

Associate Editor:  Zhiwu Li, Macau University of Science and Technology, Macau

Guest Editors:

    1. Guangdong Tian, Shandong University, China
    2. Di Wu, Hunan University, China
    3. MengChu Zhou, New Jersey Institute of Technology, Newark, USA
    4. Feng Chu, Univeristy of Paris-Saclay and University of Evry, France

 

Relevant IEEE Access Special Sections:

  1. Applications of Big Data in Social Sciences
  2. AI-Driven Big Data Processing: Theory, Methodology, and Applications
  3. Urban Computing and Intelligence


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: systemscontrol@gmail.com.

Blockchain-Enabled Trustworthy Systems

Submission Deadline: 01 April 2020

IEEE Access invites manuscript submissions in the area of Blockchain-Enabled Trustworthy Systems.

We are enjoying the benefits brought about by the accelerated development of computing systems and Internet. However, we are also suffering from a number of security and privacy vulnerabilities caused by the increasing system complexity, heterogeneity, dynamicity and decentralized nature. These security and privacy vulnerabilities may prevent the wide adoption of Information and communications technology (ICT) technologies. Therefore, trust management has become a crucial aspect in developing trustworthy systems with the preservation of security and privacy.

From the Oxford dictionary, the term blockchain is defined as “A system in which a record of transactions made in bitcoin or another cryptocurrency are maintained across several computers that are linked in a peer-to-peer network.” The recent advances in blockchain technologies bring opportunities to fully realize trustworthy systems. In particular, blockchain technologies can enable anonymous and trustful transactions in decentralized and trustless environments. As a result, blockchain-enabled trust management can help to reduce system risks, mitigate financial fraud and cut down operational cost of computing systems. Blockchain-enabled trustworthy systems can apply to diverse areas, such as financial services, social management, internet of things and supply chain management. Therefore, blockchains can potentially enable trustworthy systems, though there are a number of research issues to be solved before the formal adoption of blockchains to trustworthy systems.

This Special Section of IEEE Access will solicit high-quality, original contributions.  The topics of interest include, but are not limited to:

  • Theories and algorithms for blockchain-enabled trust management
  • Scalability and fault tolerance mechanisms for trustworthy systems
  • Platform development for blockchain-enabled trustworthy systems
  • Smart contracts for blockchain-enabled trust management and trustworthy systems
  • Security, privacy, safety, and risk management for trustworthy systems
  • Blockchain-based trustworthy applications
  • Security, privacy and trust for blockchain
  • Blockchain for trusted social management
  • Blockchain for big data in trustworthy systems
  • Blockchain for trusted service computing
  • Blockchain for trusted industrial systems
  • Blockchain for trusted cloud computing
  • Blockchain for trusted Internet of Things
  • Algorithms, architecture, framework, design patterns and techniques for trustworthy systems
  • Metrics and measurement for trustworthy systems
  • Quality assurance, maintenance and reverse engineering for trustworthy systems
  • Verification, validation, testing, and analysis for trustworthy systems
  • Communication, networking, optimization, and performance for trustworthy systems
  • Empirical studies, benchmarking, and industrial best practices for trustworthy systems
  • Service-based trustworthy systems
  • Other emerging ideas and solutions for blockchain and trustworthy systems.

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

 

Associate Editor: Hong-Ning Dai, Macau University of Science and Technology, Macau

Guest Editors:

    1. Sabita Maharjan, Simula Metropolitan Center for Digital Engineering, Norway
    2. Zibin Zheng, Sun Yat-sen University, China
    3. Patrick C. K. Hung, Ontario Tech University, Canada
    4. Quanqing Xu, Ant Financial Services Group and Blockchain lab, DAMO Academy, China
    5. Wen Sun, Northwestern Polytechnical University, China

 

Relevant IEEE Access Special Sections:

  1. Research Challenges and Opportunities in Security and Privacy of Blockchain Technologies
  2. Internet-of-Things (IoT) Big Data Trust Management
  3. Security and Trusted Computing for Industrial 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:  hndai@ieee.org.

Cloud – Fog – Edge Computing in Cyber-Physical-Social Systems (CPSS)

Submission Deadline: 01 February 2020

IEEE Access invites manuscript submissions in the area of Cloud – Fog – Edge Computing in Cyber-Physical-Social Systems (CPSS).

Cyber-Physical-Social Systems (CPSS) integrates the cyber, physical and social spaces together. One of the ultimate goals of cyber-physical-social systems (CPSS) is to make our lives more convenient and intelligent by providing prospective and personalized services for users. To achieve this goal, a wide range of data in CPSS are employed as the starting point for research, since the data contains the user’s historical behavior trajectory and the user’s demand preference. Generated and collected from social and physical spaces and integrated in the cyber space, CPSS data are complex and heterogeneous, recording all aspects of users’ lives in the forms of image, audio, video and text. Generally, the collected or generated data in CPSS satisfies 4Vs (volume, variety, velocity, and veracity) of big data. Thus, how to deal with CPSS big data efficiently is the key to provision services for users.

From another perspective, CPSS big data are specified as the global historical data and the local real-time data. Cloud computing, as a powerful paradigm for implementing the data-intensive applications, has an irreplaceable role in processing global historical data. On the other hand, with the increasing computing capacity and communication capabilities of mobile terminal devices, fog-edge computing, as an important and effective supplement of cloud computing, has been widely used to process the local real-time data. Therefore, how to systematically and efficiently process the CPSS big data (including both the global historical data and the local real-time data) in CPSS has become the key for providing services in CPSS.

This Special Section in IEEE Access aims to share and discuss recent advances and future trends of Cloud-Fog-Edge Computing in CPSS, and to bring academic researchers and industry developers together. Articles on practical as well as on theoretical topics and problems about proximity services are invited.

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

  • Cloud computing for big data processing in CPSS
  • Big data management framework for CPSS in cloud-fog-edge environment
  • Security and privacy issues for CPSS in fog/edge computing
  • Computation offloading for edge computing in CPSS
  • Dynamic resource provisioning for CPSS in cloud-fog-edge computing
  • CPSS big data mining in cloud-fog-edge computing
  • Service composition for CPSS in cloud-fog-edge computing
  • Big data applications in CPSS

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

 

Associate Editor:  Md Arafatur Rahman, University Malaysia Pahang, Malaysia

Guest Editors:

    1. Zahir Tari, Royal Melbourne Institute of Technology University, Australia
    2. Dakai Zhu, University of Texas at San Antonio, USA
    3. Francesco Piccialli, University of Naples FEDERICO II, Italy
    4. Xiaokang Wang, St. Francis Xavier University, Canada

 

Relevant IEEE Access Special Sections:

  1. Advanced Data Mining Methods for Social Computing
  2. Distributed Computing Infrastructure for Cyber-Physical Systems
  3. Innovation and Application of Intelligent Processing of Data, Information and Knowledge as Resources in Edge 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:  arafatur@ump.edu.my.

Future Generation Smart Cities Research: Services, Applications, Case Studies and Policymaking Considerations for Well-Being [Part II]

Submission Deadline: 20 February 2020

IEEE Access invites manuscript submissions in the area of Future Generation Smart Cities Research: Services, Applications, Case Studies and Policymaking Considerations for Well-Being [Part II].

Research on smart cities is maturing and the question of securing the well-being of cities’ inhabitants is attracting increasing attention of researchers, practitioners, and policymakers. Urban computing occupies a central position in this context 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 so that, ultimately, synergies emerging at the research-innovation-policymaking nexus can be exploited and thus city dwellers’ well-being enhanced. Since urban computing serves as a framework that integrates increasingly sophisticated technologies pertinent to the Internet of Things (IoT), pervasive computing, big data analytics, crowdsourcing, and volunteered geographic information, including user behavior, brand popularity, recommender systems, and social media analytics, it bears the promise and potential that viable solutions to key problems and challenges specific to cities/urban spaces will be worked out. The objective of this Special Section 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 geared toward 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 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
  • Volunteered geographic information
  • 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
  • Cryptocurrency technology to impact the social economic growth in smart cities
  • 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, Deree – The American College of Greece, Greece, and  King Abdulaziz University, Saudi Arabia

Guest Editors:

    1. Anna Visvizi, Deree – The American College of Greece, Greece and Effat University, Saudi Arabia
    2. Akila Sarirete, Effat University, Saudi Arabia
    3. Miguel Tores Ruiz, Polytechnic Institue of Mexico, Mexico City, Mexico
    4. Tugrul U. Daim, Portland State University, USA

 

Relevant IEEE Access Special Sections:

  1. Urban Computing & Well-being in Smart Cities: Services, Applications, Policymaking Considerations
  2. Advanced Data Mining Methods for Social Computing
  3. Artificial Intelligence (AI)-Empowered Intelligent Transportation 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:  mlytras@acg.edu.