Internet of Space: Networking Architectures and Protocols to Support Space-Based Internet Services

Submission Deadline:  31 January 2022

IEEE Access invites manuscript submissions in the area of Internet of Space: Networking Architectures and Protocols to Support Space-Based Internet Services.   

This Special Section is focused on the most recent scientific research and insights on the evolution of communication architectures and protocols for an Internet of Space, able to boost the creation of a truly global Internet by means of the integration of the current Internet with a new Internet of Space. Such evolution is expected to have a significant impact on several markets such as IoT/Industrial IoT, Mobile services, Industry 4.0, Government enterprise, and Connected mobility.

The section shall cover work focused on aspects such as how to support the operation of Tier-1, Tier-2 or even Tier-3 airborne/spaceborne networks; how to address interoperability, within and across different protocol layers in the network architecture, leveraging cross-layer design; and finally how to design a more unified next generation Internet architecture able to transparently include spaceborne and airborne platforms in a way that allows for user-centric services, and a smooth operation of transient networks.

However, an original and competent Internet of Space, calls for the definition of a networking framework able to accommodate specific properties of dynamic systems, including heterogeneous physical layers, frequent changes in network topology, high propagation delays, and intermittent connectivity. The dominant success factor for such a networking framework is low-cost bandwidth, although its capability to support low latency and high-throughput services plays an important role.

Secondly, a global Internet is only possible with a transparent integration of an Internet of Space with the current Internet, while supporting multi-tenants, multi-systems in different orbits and altitudes, as well as multiple markets. Such an integration requires rethinking the Internet architecture in order to extend its operation to all systems above the Earth’s surface, which requires the integration of heterogeneous communication devices and protocols. Such a unifying networking framework will have a truly global reach, allowing the connection between information producers and consumers in any corner of Earth and Space. Last but not least, the seamless integration of an Internet of Space with the current Internet will lead to a global empowerment, providing information access to everyone who may need it to sustain enriched human life, while mitigating some of the major limitations of a network infrastructure that is built on Earth’s surface, which is subjected not only to geographic limits but also to political limits.

From a technical perspective this Special Section is focused on the design and performance evaluation of networking architectures and protocols for the Internet of Space, as well as on a more unified design that best deals with the networking challenges to be faced. 

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

  • Network architectures, able to support multi-tenants, multi-systems in different orbits and altitudes, as well as multiple markets, while being transparently integrated in the current Internet architecture. Such new, unifying, network architecture may require the exploitation of paradigms such as Delay Tolerant Networking (DTN), and Information Centric Networking (ICN).
  • Network virtualization, leveraging well-known technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV), as well as their integration with the emerging concept of Multi-Access Edge Computing (MEC), allowing the virtualization of networking, storage and computing fabrics at the edge, required for the offloading of tasks that have latency constraints from the core to the edge.
  • Decentralized Internet Infrastructure, allowing a scalable Internetworking between computing processes and service hosted at the network edge (including flying platforms and spaceborne platforms, such as smart satellite constellations), leading to an end-to-end latency reduction due to user proximity, as well as a reduction of network traffic through traffic localization and device-to-device communications.
  • Network management, such as support for the global orchestration of network functions on board  spaceborne platforms (e.g., satellites) to best support data processing and aggregation; seamless interoperation of mobile Edge infrastructure and devices; resilience and seamless adaptation based on the capability to anticipate the behavior of services on a global scale.
  • Cognitive networking, in which programmable spaceborne networks allow networked devices to perform customized computation, including the usage of Artificial Intelligence. Such cognitive functions will be exploited to develop more intelligent, adaptive networks, able to perceive network conditions, decide upon those conditions, and learn from the consequences of its actions.
  • Networking protocols, including support for inter-satellite communications, and satellite to ground communications, Quality of Service (QoS) and Quality of Experience (QoE), integrated security, and mobility, and their integration with existing protocols such as IP routing (e.g. segment routing), transport protocols from the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) to Quick UDP Internet Connections (QUIC), and application protocols such as Domain Name Service (DNS).
  • Wireless technologies, including not only the usage of radio frequency systems but also free space optical systems, and a combination of both.
  • Network measurement & performance, to assist in understanding and exposing the performance of spaceborne networking resources, infrastructure, and available communication protocols in a variety of ground-to-space, inter-satellite communication scenarios.
  • Privacy, security and trustworthiness, assuming end-to-end scenarios involving satellites with computational and storage capabilities, and covering aspects such as data security, decentralized trust architectures.
  • Impact on Internet services, such as advanced IoT services (e.g., Augmented Reality/Virtual Reality in manufacturing or farming) served by spaceborne platforms and spaceborne communications; real-time IoT applications (e.g., critical monitoring of public infrastructures); awareness services (e.g., public safety services).
  • Impact on data management aspects, including the support of the next generation of Edge computing in space, as well as a fast cooperation between a large set of Edge-based producers of data.

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Associate Editor: Rute C. Sofia, fortiss GmbH, Germany

Guest Editors:

    1. Paulo Mendes, Airbus, Germany
    2. Vassilis Tsaoussidis, Democritus University of Thrace, Greece
    3. Tomaso de Cola, DLR, Germany
    4. Scott Burleigh, California Institute of Technology, USA
    5. Mianxiong Dong, Muroran Institute of Technology, Japan
    6. Eduardo Cerqueira, University Federal of Pará, Brazil

Relevant IEEE Access Special Sections:

    1. Networks of Unmanned Aerial Vehicles: Wireless Communications, Applications, Control and Modelling
    2. Communications in Harsh Environments
    3. Edge Intelligence 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: sofia@fortiss.org.

Deep Learning for Internet of Things

Submission Deadline:  20 September 2021

IEEE Access invites manuscript submissions in the area of Deep Learning for Internet of Things.   

In recent years, the techniques of Internet of Things (IoT) and mobile communications have been developed to detect and collect human and environment information (e.g. geo-information, weather information, bio-information, human behaviors, etc.) for a variety of intelligent services and applications. The three layers in IoT are the sensor, networking, and application layers; several techniques and standards (e.g. oneM2M, Open Connectivity Foundation, etc.) have been proposed and established for these three layers. For the sensor and networking layers, the rise of mobile technology advancements (e.g. wireless sensor networks, LoRaWAN, Sigfox, narrow band-IoT, etc.) has led to a new wave of machine-to-machine (M2M), machine-to-human (M2H), human-to-human (H2H), and human-to-machine (H2M) communications. For the application layer, the IoT techniques in several applications, including energy, enterprise, healthcare, public services, residency, retail, and transportation, have been designed and implemented to detect environmental changes and send instant updates to a cloud computing server farm via mobile communications and middleware for big data analyses. One of the perfect examples is that the vehicle on-board units can instantly detect and share information about the vehicle geolocation, speed, following distance, as well as gaps with other neighboring vehicles. Big data can be collected by IoT techniques and then analyzed by deep learning techniques for a variety of applications and services.

Deep learning techniques, e.g. neural network (NN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), etc., have been popularly applied into image recognition and time-series inference for IoT applications. Advanced driver assistance systems and autonomous cars, for instance, have been developed based on machine learning and deep learning techniques, which perform forward collision warning, blind spot monitoring, lane departure warning, traffic sign recognition, traffic safety, infrastructure management and congestion, and so on. Autonomous cars can share their detected information, such as traffic signs, collision events, etc., with other cars via vehicular communication systems, e.g., dedicated short range communications (DSRC), vehicular ad hoc networks (VANETs), long term evolution (LTE), and 5th generation mobile networks for cooperation. However, how to enhance the performance and efficiency of these deep learning techniques is one of the big challenges for implementing these real-time applications.

Furthermore, several optimization techniques, such as stochastic gradient descent algorithm (SGD), adaptive moment estimation algorithm (Adam), and Nesterov-accelerated Adaptive Moment Estimation (Nadam), have been proposed to support deep learning algorithms for faster solution searching; for example, the gradient descent method is a popular optimization technique to quickly seek the optimized weight sets and filters of CNN for image recognition. The IoT applications based on these image recognition techniques (autonomous cars, augmented reality navigation systems, etc.) have gained considerable attention, and the hybrid approaches typical of mathematics for engineering and computer science (deep learning and optimization techniques) can be investigated and developed to support a variety of IoT applications.

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

  • Deep learning for massive IoT
  • Deep learning for critical IoT
  • Deep learning for enhancing IoT security
  • Deep learning for enhancing IoT privacy
  • Preprocessing of IoT data for AI modeling
  • Deep learning for IoT applications (smart home, smart agriculture, interactive art, etc.)

 

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Associate Editor: Chi-Hua Chen, Fuzhou University, China

Guest Editors:

    1. Yi-Bing Lin, National Yang Ming Chiao Tung University, Taiwan
    2. Kuo-Ming Chao, Coventry University, UK

Relevant IEEE Access Special Sections:

    1. Intelligent Logistics Based on Big Data
    2. Real-Time Machine Learning Applications In Mobile Robotics
    3. Advances in Machine Learning and Cognitive Computing for Industry Applications

 

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

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

 For inquiries regarding this Special Section, please contact: chihua0826@gmail.com.

Metal Additive Manufacturing

Submission Deadline:  15 December 2021

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

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

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

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

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

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

 

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

 

Associate Editor:  Pedro Neto, University of Coimbra, Portugal

Guest Editors:

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

 

Relevant IEEE Access Special Sections:

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

 

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

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

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

Collaborative Intelligence for Internet of Vehicles

Submission Deadline:  01 September 2021

IEEE Access invites manuscript submissions in the area of Collaborative Intelligence for Internet of Vehicles.   

Internet of vehicles (IoV) technology is one of the most important breakthroughs that can significantly support mobility systems toward achieving smart and sustainable societies. For example, cooperative driving features enabled by IoV can significantly decrease the risk of traffic accidents and reduce CO2 emissions, thus facilitating smarter transportation. Aerial vehicles, also known as drones, are useful for many applications, including environment and traffic monitoring, crowd mobility and gathering surveillance in pandemics, disaster recovery, and so on. Internet of underwater vehicles could enable many innovative maritime applications such as autonomous shipping, target detection, navigation, localization, and environmental pollution control. However, the development of IoV systems is dependent on overcoming the following two main challenges.

First, due to the heterogeneity of networking entities, strict application and data processing requirements, and limited resources in IoV environments, more advanced networking and computing technologies are required. Future IoV systems feature a larger number of devices and multi-access environments where different types of wireless spectrums should be efficiently utilized. At the same time, novel services, such as cooperative autonomous driving, IoV-based safety and traffic efficiency applications are emerging, and demand unprecedented high accuracy and reliability, ultra-low latency, and large bandwidth. This poses crucial challenges to the efficient use of the limited networking and computing resources.

Recently, to further explore the value of big data from IoV systems, artificial intelligence (AI)-based approaches have been attracting great interest in empowering computer systems. Some collaborative learning approaches, such as federated learning and multi-agent systems, have been used to reduce network traffic and improve the learning efficiency of some smartphone applications. For IoV systems, collaborative intelligence can be achieved via an efficient collaboration among heterogeneous entities, including vehicles, edge servers, and the cloud.

Second, in order to enable a smarter society, more research should be conducted on developing collaborative IoV frameworks and systems to expedite the applications of emerging IoV technologies. An efficient use of cross-domain big data should be discussed, and academic-industrial collaborations should be promoted to solve the existing problems toward a smarter society.

This Special Section focuses on the technical challenges for enabling collaborative IoV systems, and the applications of IoV technologies for a smarter society.

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

  • Collaboration among Space, Air, Ground, and Sea mobile networks
  • Collaborative intelligence based on cross-domain big data for IoV
  • Collaborative networking for IoV
  • Collaborative computing for IoV
  • Collaborative IoV for smart cities
  • Collaborative IoV for intelligent transportation systems
  • Collaborative IoV for energy-efficient sustainable cities
  • Collaborative electric vehicles
  • Collaborative unmanned aerial vehicles
  • Collaborative heterogeneous unmanned ground and aerial vehicles
  • Collaborative underwater vehicle technologies for smart ocean
  • Collaborative IoV for smarter society
  • Collaborative learning for IoV
  • Data driven collaborative intelligence for IoV
  • End-edge-cloud collaboration for IoV
  • New networking and computing architectures for Collaborative IoV
  • Security & privacy for IoV

 

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

 

Associate Editor:  Celimuge Wu, The University of Electro-Communications, Japan

Guest Editors:

    1. Soufiene Djahel, Manchester Metropolitan University, UK
    2. Damla Turgut, University of Central Florida, USA
    3. Sidi-Mohammed Senouci, University of Bourgogne, France
    4. Lei Zhong, Toyota Motor Corporation, Japan

 

Relevant IEEE Access Special Sections:

    1. Artificial Intelligence (AI)-Empowered Intelligent Transportation SystemsBeyond 5G Communications
    2. Edge Intelligence for Internet of ThingsMillimeter-Wave Communications: New Research Trends and Challenges
    3. Information Centric Wireless Networking with Edge Computing for 5G and IoT

 

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: celimuge@uec.ac.jp.

Reconfigurable Intelligent Surface Aided Communications for 6G and Beyond

Submission Deadline:  31 August 2021

IEEE Access invites manuscript submissions in the area of Reconfigurable Intelligent Surface Aided Communications for 6G and Beyond.   

Reconfigurable Intelligent Surface (RIS) aided wireless communications is a hot research topic in academic and industry communities since it can enhance both the spectrum and energy efficiency of wireless systems by artificially reconfiguring the wireless propagation environment. RIS can configure tiny antenna elements or scatterers, which can be judiciously tuned to enhance signal power at desired users, such as primary users in cognitive radio networks, or suppress signal power at undesired users, such as eavesdroppers in physical layer security networks. The RIS also finds promising applications in dense urban areas or indoor scenarios, where electromagnetic waves are prone to be blocked by obstacles such as buildings and walls. There are numerous advantages associated with RIS. For instance, since RIS needs no analog-to-digital converters or radio frequency chains, it saves energy consumption to improve its sustainability, and reduces system cost. RIS can be fabricated in small size and light weight, which can be easily deployed on a building’s facade, walls, ceilings, street lamps, etc. Furthermore, since RIS is a complementary device, it can be readily integrated into current wireless networks (both cellular network and WIFI) without many standardization modifications. Due to these appealing advantages, RIS-aided wireless communications is envisioned to be a revolutionary technique, and one of the key technologies for the sixth-generation (6G) wireless networks.

To reap the full potential offered by RIS, a number of emerging challenges for the transceiver design of RIS-aided wireless communications needs to be tackled. The transceiver beamforming design requires advanced low complexity signal processing algorithms, the incorporation of RIS in wireless communications will consume more pilot resources for the RIS-related channel estimation, and the time slots left for data transmission will be reduced. It is imperative to justify the benefits of introducing RIS when taking into account additional pilot overhead. Furthermore, most of the existing contributions on transceiver design are based on perfect channel state information (CSI), which is challenging to achieve in RIS-aided communications. Hence, robust transmission design needs to be investigated. Finally, in practice, the RIS elements are designed with discrete shifts, which further pose new challenges for evaluating its performance.

This Special Section aims to summarize recent advancements in RIS-aided wireless communications and spur more efforts in this area to make it a reality. The scope of this Special Section covers a wide range of disciplines such as wireless communications, metamaterials, signal processing, and artificial intelligence. In this Special Section, we invite high-quality, original, technical and survey articles, which have not been published previously on RIS-related techniques and their applications in wireless communications.

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

  • Integration of RIS in emerging wireless applications (e.g., RIS-aided wireless power transfer, RIS-aided mobile edge computing, RIS-aided physical layer security, IRS-aided UAV communications, etc)
  • Pilot overhead reduction schemes for channel estimation in RIS-aided wireless communications (e.g. compressed-sensing method by exploiting the sparsity of the channels)
  • Robust transceiver design based on imperfect channel state information or/and imperfect phase shift models
  • Transceiver design based on statistical channel state information
  • Joint active and beamforming for RIS-aided wireless communications
  • Information theoretical results of the capacity of RIS
  • The impact and design of using practical hardware, e.g. discrete phase shifts
  • Energy supply of RIS
  • Mobility and handover management for RIS-aided wireless communications
  • Association and coordination among RIS, base stations and users
  • Resource allocation and interference management in RIS-aided wireless communications
  • Fundamental limits, scaling laws analysis, performance analysis, and information-theoretic analysis
  • Channel and propagation models
  • Control information exchange protocols design
  • Energy efficient system design
  • Machine learning based design
  • RIS-aided mmWave/Terahertz communications
  • Measurement studies and real-world prototypes and test-beds
  • Integration of RIS-enabled networks into the standard

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

 

Associate Editor:  Cunhua Pan, Queen Mary University of London, UK

Guest Editors:

    1. Ying-Chang Liang, University of Electronic Science and Technology of China (UESTC), China
    2. Marco Di Renzo, Paris-Saclay University, France
    3. Lee Swindlehurst, University of California Irvine, USA
    4. Vincenzo Sciancalepore, NEC Laboratories Europe GmbH, Germany

 

Relevant IEEE Access Special Sections:

    1. Beyond 5G Communications
    2. Millimeter-Wave Communications: New Research Trends and Challenges
    3. Millimeter-wave and Terahertz Propagation, Channel Modeling and Applications

 

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

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

 For inquiries regarding this Special Section, please contact: c.pan@qmul.ac.uk.

Optimal Operation of Active Buildings as an Energy System

Submission Deadline:  31 August 2021

IEEE Access invites manuscript submissions in the area of Optimal Operation of Active Buildings as an Energy System.   

The increasing share of buildings in the consumption of energy and carbon emission indicates that any solutions provided in this regard would have to consider the energy efficiency of the buildings, to obtain promising results. Active Buildings are viable solutions to this issue, in which intelligent integration of renewable-based energy technologies, heating, cooling, and transport systems would be able to make a multi-vector energy system.

Active Buildings can work in an isolated way as a self-sufficient energy system, or can interact with the other ABs in a district area and trade energy via the network. They have the potential of interacting with local as well as national level energy grids and, by behaving as zero or positive energy buildings, they are able to deliver various energy services to reduce the pressure on the upstream energy networks, and defer new investment requirements. As a result, the operation of Active Buildings is being developed as fundamental research and part of the future smart energy systems that call for a (re)thinking on the definition of the control, operation and optimization of the Active Buildings as an energy system.

This Special Section in IEEE Access will target numerous prospects in the operation of active buildings as an energy system. Both review and research articles are welcome. Real-world use cases discussing new application areas and resulting new developments are especially welcome.

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

  • A holistic approach in modeling of the energy systems of Active Buildings (ABs)
  • The role of ABs in energy systems
  • Building physics-based modeling of ABs
  • Zero energy and net-zero energy buildings
  • Coordinated operation and control of ABs at the district/city level
  • Application of model predictive control in ABs operation
  • IoT-based operation and control of ABs
  • Energy management systems of ABs
  • AC, DC, or Hybrid model of ABs
  • AB as a service provider in the electricity networks
  • Resilience-based operation of ABs
  • Reliability-based modeling of ABs
  • Uncertainty aware energy management of ABs
  • Artificial intelligence for the operation of ABs
  • Market-based operation of ABs including Building-to-Building (B2B), Building-to-Grid (B2G), Building-to-Vehicle (B2V), and Vehicle-to-Building (V2B) energy transactions as well as peer-to-peer (P2P) energy transactions

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

 

Associate Editor:  Behnam Mohammadi-Ivatloo, University of Tabriz, Iran

Guest Editors:

    1. Vahid Vahidinasab, Newcastle University, UK
    2. Somayeh Asadi, Pennsylvania State University, USA
    3. Fei Wang, North China Electric Power University, China

 

Relevant IEEE Access Special Sections:

    1. Key Enabling Technologies for Prosumer Energy Management
    2. Evolving Technologies in Energy Storage Systems for Energy Systems Applications
    3. Advanced Internet of Things for Smart Cyber-Physical Infrastructure 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: mohammadi@ieee.org.

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.

Deep Learning Technologies for Internet of Video Things

Submission Deadline:  31 March 2021

IEEE Access invites manuscript submissions in the area of Deep Learning Technologies for Internet of Video Things.   

The past decade has witnessed tremendous advances in Internet of Things (IoT) technologies, protocols, and applications. The goal of IoT is to connect every physical device/sensor (with video, audio, texting capabilities) in a seamless network to allow communication and perform intelligent decisions. Among these, video data devices (things) in IoT are increasingly becoming important since video communications are an essential part of everyone’s experiences and everything that is happening around the world. The technologies of Internet of Video Things (IoVT) are widely expected to bring exciting services and applications for security, healthcare, education, transportation, and so on.

Due to the huge amount of video data that is being generated and consumed nowadays, there are many challenges and problems that are yet to be solved to have practical real-world applications of IoVT, from sensing/capturing to displaying the data. First, efficient video sensing technologies are required to capture high quality videos with low-power. Second, video coding and communication technologies are essential for compressing and transmitting enormous volumes of data. Third, since not all transmitted data are useful for (or targets) human consumption, there are huge challenges in the areas of data learning/understanding to filter and extract high-level information. Finally, video quality enhancement and assessment algorithms are indispensable for improving and evaluating the video quality, respectively.

Recently, data-driven algorithms such as deep neural networks have attracted a lot of attention and have become a popular area of research and development. This interest is driven by several factors, such as recent advances in processing power (cheap and powerful hardware), the availability of large data sets (big data) and several small but important algorithmic advances (e.g., convolutional layers). Nowadays, deep neural networks are the state-of-the-art for several computer vision tasks, such as the ones that require high level understanding of image semantics, e.g. image classification, object segmentation, saliency detection, but also in low level image processing tasks, such as image denoising, inpainting, super-resolution, among others. Deep learning can handle large volumes of video data by making use of its powerful non-linear mapping, and extracting high-level features with very deep networks. Incorporating deep learning into IoVT can provide radical innovations in video sensing, coding, enhancing, understanding, and evaluation areas, to better handle the enormous growth in video data compared to traditional methods. On the other hand, incorporating deep learning into IoVT also brings a lot of challenges such as the long training latency of data training and huge computational cost.

This Special Section in IEEE Access provides a perfect platform for researchers from academia and industry to discuss the prospective developments and innovative ideas in applying deep learning technologies for IoVT.

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

  • Deep learning technologies in video sensing/capturing systems
  • Deep learning technologies in visual communications
  • Deep learning algorithms, architectures, and databases for image and video compression/coding
  • Deep learning for enhancement and quality assessment of visual data
  • Deep learning-based visual attention and saliency detection
  • Deep learning-based real-time and low-power video coding technologies
  • Deep learning-based algorithms, architectures, and databases for video analysis and understanding
  • Deep learning-based 3D visual coding and processing (from 360º degrees to light-fields)
  • Technologies for reducing the complexity of deep learning-based IoVT
  • Technologies for reducing training latency of deep learning-based IoVT

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

 

  Associate Editor:  Jinjia Zhou, Hosei University, Japan

  Guest Editors:

    1. Joao Ascenso, University of Lisbon, Portugal
    2. Victor Sanchez, University of Warwick, UK
    3. Lu Zhang, INSA Rennes, France
    4. Jianquan Liu, NEC Corporation, Japan
    5. Jiu Xu, Apple, USA

 

Relevant IEEE Access Special Sections:

    1. Mobile Multimedia: Methodology and Applications
    2. Innovation and Application of Internet of Things and Emerging Technologies in Smart Sensing
    3. Deep Learning Algorithms for Internet of Medical 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: jinjia.zhou.35@hosei.ac.jp.

Advanced Energy Conversion Systems Based on Multi-Port Electrical Machines

Submission Deadline:  31 July 2021

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

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

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

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

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

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

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

  Guest Editors:

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

Relevant IEEE Access Special Sections:

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

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

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

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