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.

Advanced Artificial Intelligence Technologies for Smart Manufacturing

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Advanced Artificial Intelligence Technologies for Smart Manufacturing.

As the world enters a new phase of industrialization (Industry 4.0, or the fourth industrial revolution), smart manufacturing has become crucial. Industry 4.0 refers to an industrial transformation aided by smart manufacturing and data exchange, such as high-level factory automation and Internet of Things applications. Artificial intelligence and smart machinery have also become integral research areas in manipulation. Researchers from academia and various industries are now working to develop the next generation of intelligent smart manufacturing applications. With the application of advanced artificial intelligence technologies, the revolution of the smart manufacturing industry can beadvanced more quickly.

To build a competitive advantage, keep up with the “Industry 4.0” trend, and to attract and filter high quality academic contributions, we have organized this Special Section in IEEE Access on “Advanced Artificial Intelligence Technologies for Smart Manufacturing.” High quality articles within the field are highly encouraged and considered in this Special Section.

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

  • Artificial Intelligence, Embedded Systems and Cloud Computing in Manufacturing
  • Smart Actuators and Adaptive Control of Machine Tools
  • Man-machine interface and integration
  • Intelligent machinery equipment
  • Intelligent Automation
  • Intelligent manufacturing
  • Advanced signal processing and machine perception of mechanical systems
  • Machine learning techniques for smart manufacturing
  • M2M technology
  • Big Data Analytics in Manufacturing

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

 

Associate Editor: Her-terng Yau, National Chin-Yi University of Technology, Taiwan

Guest Editors:

    1. Stephen D. Prior, University of Southampton, UK
    2. Yang Wang, Georgia Institute of Technology, USA
    3. Yunhua Li, BeiHang University, China

 

Relevant IEEE Access Special Sections:

  1. Artificial Intelligence Technologies for Electric Power Systems
  2. Big Data Technology and Applications in Intelligent Transportation
  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: pan1012@ms52.hinet.net; htyau@ncut.edu.tw.

Emerging Approaches to Mobile Cooperative Sensing and Its Applications in Smart Environments

Submission Deadline: CLOSED

IEEE Access invites manuscript submissions in the area of Emerging Approaches to Mobile Cooperative Sensing and Its Applications in Smart Environments.

Mobile cooperative sensing is becoming a popular paradigm to collect information and outsource tasks to mobile users. Detected events are then reported continuously through the integration and cooperation of sensors, actuators, controllers, and artificial intelligence. Mobile cooperative sensing refers to estimating the event information and simulating the situation of smart environments through the analysis of data from sensor networks deployed in the environment. The systems are able to actively obtain information of occupancy, such as identification, activity, and even gestures with mobile cooperative sensing. For instance, intrusion detection and human identification help to detect intruders automatically. In smart environments, light and temperature can be adjusted according to the occupancy, which conserves the energy. For children and the elderly, activities such as falling can be monitored to prevent potential hazards. Mobile cooperative sensing is becoming a vital part of the fvarious emerging applications in smart environments, such as energy consumption estimation, security surveillance, and human behavior analysis. Generally speaking, mobile cooperative sensing provides an active detected input for smart systems, so  it can make judgment or feedback accordingly.

At present, with the increasing mobile cooperative sensing technology in the field of Mobile Internet of Things (M-IoT) and the continuous improvement and upgrading of the current internet infrastructure, application and business model innovations are constantly emerging. Mobile cooperative sensing is further penetrating traditional fields, such as finance, transportation, medical treatment, education, etc… Mobile cooperative sensing technology can already be found in the fields of intelligent transportation, internet finance, and intelligent medical treatment, for example. In the future, some preliminary application results can be expected to be used in other fields, especially in the application areas of smart environments (e.g., smart homes and cities), which will have a far-reaching impact. Mobile cooperative sensing technology is not just about the detection via sensor networks; it is also about enabling a wide range of new capabilities, architectures, and service paradigms. The development of mobile cooperative sensing is set to have major economic, social, and environmental impacts, the intersection of which forms future sustainable growth.

As a result, the development of mobile cooperative sensing in smart environments is fundamentally important; however, many problems still remain.. For example, how to construct and improve the framework and platform for mobile cooperative sensing; how to collect, measure, process, analysis, and optimize mobile cooperative sensing data; and how to improve the effectiveness and decrease the energy in mobile cooperative sensing. These are all vital but unavoidable problems for mobile cooperative sensing in smart environments.

This Special Section is intended to provide a specific opportunity to argue the state-of-the-art technologies around mobile cooperative sensing in smart environments and invite researchers in the relevant fields to share the latest progress, novel methodologies, and potential research topics.

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

  • Construction and improvement of framework and platform for mobile cooperative sensing
  • Collection, measurement, processing, analysis, and optimization of mobile cooperative sensing data
  • Improvement of effectiveness and decrease of energy in mobile cooperative sensing
  • Evaluation of performance relevant to mobile cooperative sensing systems
  • Adaptability of mobile cooperative sensing systems in complex and variable environments
  • Emerging technologies in smart environments
  • Artificial intelligence in smart environments
  • Smart environment-based machine learning for mobile cooperative sensing
  • Security and privacy of mobile cooperative sensing in smart environments
  • Quality of Service (QoS) and Mobile Internet of Things (M-IoT) services in mobile cooperative sensing systems

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

 

Associate Editor: Mu Zhou, Chongqing University of Posts and Telecommunications, China

Guest Editors:

    1. Hongying Meng, Brunel University London, UK
    2. Haiying Wang, Ulster University, UK
    3. Lei Chen, Georgia Southern University, USA
    4. Kunjie Xu, Intel Corporation, USA

 

Relevant IEEE Access Special Sections:

  1. Innovation and Application of Internet of Things and Emerging Technologies in Smart Sensing
  2. Intelligent Information Services
  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: zhoumu@cqupt.edu.cn.

Multi-Energy Computed Tomography and its Applications

Submission Deadline:  01 May 2021

IEEE Access invites manuscript submissions in the area of Multi-Energy Computed Tomography and its Applications.

X-ray Computed Tomography (CT) can reconstruct the internal image of an object by passing x-rays through it and measuring the information. However, the conventional CT not only has poor performance in tissue contrast and spatial resolution, but also fails to provide quantitative analysis results and specific material components. To avoid these limitations, as a natural extension of the well-known dual-energy CT, the multi-energy CT (MECT) has emerged and is attracting increasing attention. A typical MECT system has great potential in reducing x-ray radiation doses, improving spatial resolution, enhancing material discrimination ability and providing quantitative results by collecting several projections from different energy windows (e.g. photon-counting detector technique) or spectra (e.g. fast kV-switching technique) either sequentially or simultaneously. It is a great achievement in terms of tissue characterization, lesion detection and material decomposition, etc. This can enhance the capabilities of imaging internal structures for accurate diagnosis and optimized treatments.

On the one hand, the limited photons within the narrow energy windows can result in energy response inconsistency. On the other hand, due to spectral distortions (e.g., charge sharing, K-escape, fluorescence x-ray emission and pulse pileups), the projections of MECT are tarnished by complicated noise. In this case, it is a challenge to find meaningful insights by utilizing these projections for practical applications. Therefore, there are new research opportunities to overcome this issue for higher levels of MECT imaging and applications.

This Special Section on IEEE Access aims to capture the state-of-the-art advances in imaging techniques for MECT and other related research.

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

  • MECT image reconstruction
  • MECT image denoising
  • MECT material decomposition
  • MECT hardware development
  • MECT system design
  • MECT image analysis
  • MECT image quality assessment
  • Applications of machine learning in MECT
  • X-ray spectrum estimation for MECT
  • Clinical diagnosis using MECT technique
  • Multi-contrast contrast agent imaging
  • K-edge imaging technique
  • Simulation software package for MECT imaging
  • Scattering correction for MECT
  • Artifacts removal of MECT image
  • Noise estimation models for MECT imaging

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

 

Associate Editor:  Hengyong Yu, University of Massachusetts Lowell, USA

Guest Editors:

    1. Yuemin Zhu, CNRS, University of Lyon, France
    2. Raja Aamir Younis, Khalifa University of Science and Technology UAE

 

Relevant IEEE Access Special Sections:

  1. Deep Learning Algorithms for Internet of Medical Things
  2. Millimeter-Wave and Terahertz Propagation, Channel Modeling and Applications
  3. Trends and Advances in Bio-Inspired Image-Based Deep Learning Methodologies 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: Hengyong-yu@ieee.org.

Beyond 5G Communications

Submission Deadline: 30 September 2020

IEEE Access invites manuscript submissions in the area of Beyond 5G Communications.

As the commercial deployment of the fifth generation of cellular networks (5G) is well underway in many countries of the world, academia as well as industrial research organizations turn their attention to what comes next. As it typically takes ten years to develop a new cellular communication standard, it is now the perfect time to identify promising topics and research directions for the next decade, which will lay the foundations for a possible 6G system. Moving from 4G to 5G, no disruptive changes to the physical layer were made. The main novelty was to simultaneously support a set of diverse applications with different throughput, latency, and reliability requirements, thanks to a flexible OFDM numerology and the concept of network slicing. Also, the spectral efficiency could be dramatically increased by supporting larger bandwidths and antenna arrays at the base station, i.e., massive MIMO. Although machine learning is currently one of the hottest topics in the field of communications, it did not play any role in the design of 5G and will mainly be used to implement, optimize, and operate such systems efficiently. 6G will likely be driven by a mix of past trends (e.g., more cells, larger and distributed antenna arrays, higher spectrum) as well as new technologies, services, applications, and devices.

The aim of this Special Section is to gather forward-looking contributions on radio access technologies beyond 5G. Topics of interest comprise new frequency bands, new multiple-antenna technologies (passive and/or active), new network deployments, new waveforms, and new applications of RF signals beyond mere communications, as well as the fusion of wireless and sensor information. A tool of central importance is machine learning, to either learn entirely new communication protocols or simply enhance traditional algorithms. Since the development of a new standard is largely driven by use cases, e.g., mobile broadband, mission critical applications, massive machine-type traffic, we explicitly solicit opinion and vision articles concerning the potential requirements and key enablers of 6G.

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

  • New wireless communication systems, network deployments, and spectrum sharing
  • Machine learning-based wireless systems and services
  • Terahertz communications and networks
  • Radar enhanced wireless systems
  • New multiple antenna technologies and deployments
  • Massive connectivity in communication systems
  • Edge intelligence for beyond 5G networks
  • Wireless big data enabled technologies
  • Photonics and wireless integration
  • Autonomous networks

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

 

Associate Editor:  Jakob Hoydis, Nokia Bell Labs, France

Guest Editors:

    1. Ulf Gustavsson, Ericsson AB, Sweden
    2. Urbashi Mitra, University of Southern California, USA
    3. Luca Sanguinetti, University of Pisa, Italy
    4. Christoph Studer, Cornell University, USA
    5. Meixia Tao, Shanghai Jiao Tong University, China

 

Relevant IEEE Access Special Sections:

  1. Antenna and Propagation for 5G and Beyond
  2. 5G and Beyond Mobile Wireless Communications Enabling Intelligent Mobility 
  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: jakob.hoydis@nokia-bell-labs.com.

Reliability in Sensor-Cloud Systems and Applications (SCSA)

Submission Deadline: 28 February 2021

IEEE Access invites manuscript submissions in the area of Reliability in Sensor-Cloud Systems and Applications (SCSA).

The sensor-cloud system (SCS) integrates sensors, sensor networks, and cloud for managing sensors, collecting data, and decision-making. The integration provides flexible, low-cost, and reconfigurable platforms for monitoring and controlling the applications, including emerging applications of the Internet of things (IoT), machine to machine, and cyber-physical systems. Though the SCS has received tremendous attention from both academia and industry because of its numerous exciting applications, it suffers from issues with reliability.

The reliability of the SCS is the ability to perform required functions at or below a given failure rate for a given period of time. The required functions are identified as correctly providing measurement results. Sensor data can be faulty due to system faults or security attacks during the sensor data acquisition and collection, and data processing/decision-making on the cloud. There is also the possibility of not receiving the data at all or received data is compromised. Sensor data is also susceptible to errors and interference such as noise. Furthermore, to save sensor resources, end-users are becoming more dependent on cloud processing and decision-making. This enables significant interactions between sensors and between sensors and the cloud. As a result, the required functions can be extended to cover more concerns, including system design and integration, system continuity, real-time responsiveness, data security, and prevention of privacy intrusions, etc. This is why practitioners and engineers must also be able to measure imprecision and true reliability for both the data and the system that deals with the data.  When designing and developing the SCS for real applications, the reliability verification at any level is required.

This Special Section aims to collect and publish state-of-art research results to solve the reliability concerns of sensor-cloud systems.

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

  • Reliability theory, standardization, modeling, and architecture for SCSA
  • Reliability in sensor design and integration in SCSA
  • Reliability in communication and networking in SCSA
  • Reliability in sensor data privacy processing, computing, and control in SCSA
  • Reliability in applying machine learning models and methods for SCSA
  • Reliability through data security, privacy, and trust for SCSA
  • Reliability concerns in monitoring diverse applications of SCSA
  • Reliability through QoS in application monitoring in SCSA
  • Reliability issues (e.g., cloud storage, computing, decision-making) in SCSA
  • Reliability in fog and edge computing in SCSA
  • Reliability in mobile sensing, offloading and tracking in SCSA
  • Reliability assessment, measures, and testbeds for SCSA
  • Reliability levels and relations, metrics, and measures for SCSA
  • Data quality assurance and quality control in SCSA
  • Effective network design and development of SCSA
  • Smartphone-based and heterogeneous design of SCSA
  • Reliability through fault-tolerance and reliability in SCSA

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

 

Associate Editor:  Md Zakirul Alam Bhuiyan, Fordham University, USA

Guest Editors:

    1. Arafatur Rahman, University Malaysia Pahang, Malaysia
    2. Liu Qin, Hunan University, China
    3. Xuyun Zhang, Macquarie University, Australia
    4. Taufiq Asyhari, Coventry University, UK

 

Relevant IEEE Access Special Sections:

  1. Additive Manufacturing Security
  2. Advanced Sensor Technologies on Water Monitoring and Modeling
  3. The convergence of Sensor Networks, Cloud Computing, and Big Data in 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: zakirulalam@gmail.com.

Emerging Trends of Energy and Spectrum Harvesting Technologies

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Emerging Trends of Energy and Spectrum Harvesting Technologies.

Billions of low-end wireless devices, such as sensors, are permeating into almost every aspect of personal life, such as in vehicles, washing machines, air conditioners, etc.  These  miniaturized and low-end devices are a promising solution to collect information and assist users for interaction with real-world objects. Frost & Sullivan reported that the global market of miniaturized devices is forecast to increase from 1.4 billion to 3.26 billion from 2014 to 2024. Unfortunately, the performance of miniaturized devices, which generally operate with limited battery  power and transmit data over an unlicensed spectrum, is highly deteriorated due to the resource scarcity issues in terms of energy and spectrum. The energy scarcity issue limits the longevity of devices, and requires the operator to manually replace the depleted battery, which results in considerable maintenance costs. Even with sufficient energy supply, data transmission of devices conflicts with other networks that coexist in the unlicensed spectrum band, which creates the spectrum scarcity issue. To alleviate these energy and spectrum scarcity issues, numerous energy and spectrum harvesting technologies emerge, such as mini solar panel, piezoelectric transducers, and cognitive radio. By embedding these modules, the devices can harvest energy from the ambient energy sources and explore the idle licensed spectrum for data transmission, leading to energy and spectrum harvesting-enabled devices.

The ESH technologies have received much attention from the industry. Numerous commercial products have emerged on the global market, such as self-powered miniaturized devices produced by EnOcean, battery-free Bluetooth tags by Wiliot, and PolarFusion digital radio architecture for extreme low power by Innophase.

However, issues remain in the application of the ESH-enabled devices. First, empowering devices with ESH capabilities increases the manufacturing cost. How can one design cost-efficient, ESH-enabled, embedded architecture with promising performance? Second, considering that both the spectrum sensing and data transmission consume energy, the management of energy and spectrum resource both impact the system performance, which makes resource allocation challenging in ESH-enabled devices. Third, the energy harvesting process and activities of primary users, who own the licensed spectrum, exhibit high dynamics over time. How can one customize the communication protocol to adapt to such high dynamics and guarantee the delivery ratio?

To address these issues, this Special Section solicits original research and practical contributions in ESH technologies. We would like to provide a chance to share ideas and solutions for the enabling techniques which empower the devices with ESH capability. We also highly welcome submissions related to the joint management of energy and spectrum resources.

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

  • Architecture design for ESH-enabled systems
  • Data-driven modeling of energy and spectrum harvesting processes
  • Communication protocol design for ESH-enabled networks
  • Hardware design and prototyping of ESH-enabled systems
  • Key scenarios/applications for ESH-enabled IoT devices
  • Convergence of energy and spectrum harvesting
  • Security, privacy, and trust in ESH-enabled systems
  • Reliable data delivery in ESH-enabled systems
  • Simulation platforms for ESH-enabled   systems

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

 

Associate Editor:  Guangjie Han, Hohai University, China

Guest Editors:

    1. Deyu Zhang, Central South University, China
    2. Ning Zhang, Texas A&M University at Corpus Christi, USA
    3. Song Guo, The Hong Kong Polytechnic University, Hong Kong, China
    4. Geyong Min, University of Exeter, United Kingdom
    5. Shengrong Bu, University of Glasgow, United Kingdom
    6. Kanke Gao, Magna International Inc, USA

 

Relevant IEEE Access Special Sections:

  1. Energy Harvesting and Scavenging: Technologies, Algorithms, and Communication Protocols
  2. Intelligent and Cognitive Techniques for Internet of Things
  3. Energy Efficient Wireless Communications with Energy Harvesting and Wireless Power Transfer


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: hanguangjie@gmail.com.

Edge Intelligence for Internet of Things

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Edge Intelligence for Internet of Things.

Internet of Things (IoT) is a key enabler for many modernized applications, from marine monitoring to outer space exploration. In recent years, we have witnessed the proliferation of edge/mobile computing and IoT, where billions of mobile and IoT devices are connected to the Internet, generating a huge volume of data at the network edge. Driven by this trend, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, analysis and control closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability and energy efficiency, as well as alleviate the network traffic.

However, with the emergence of diverse IoT applications (e.g., smart home, smart city, industrial automation, connected vehicles), it becomes challenging for edge computing to deal with these heterogeneous IoT environments with edge big data. Motivated by the success of artificial intelligence (AI) in a wide spectrum of fields, it is envisaged that AI-powered edge computing can overcome the emerging challenges by fully unleashing the potential of the edge big data. The resulting new inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of interest. However, research on edge intelligence for IoT is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. There are still many fundamental challenges faced in how to improve the architecture of IoT while fully utilizing these techniques (communication, big data processing, and computing etc.,) Some open issues require immediate review, such as:

  • How can we utilize advanced capabilities of IoT, such as in-network storage and caching, to offload the IoT data in order to release the traffic scale in the cellular networks and provide low-latency IoT services via edge intelligence?
  • Can we leverage recent advances in computing and machine learning to develop IoT?
  • How can we design novel security schemes such as lightweight encryption and blockchain, to reduce the energy consumption of a secure IoT network?
  • How can we support good system observability and controllability with the employment of edge intelligence in the IoT platform?
  • How can we realize big data analysis at the edge of IoT to promote traditional IoT to content-centric IoT integrated with 5G/6G?

The aim of this Special Section is to solicit original submissions related to edge intelligence for IoT including but not limited to:

  • Advanced system modeling, including computation modeling, content modeling, threat/security modeling, and energy consumption modeling
  • Novel transmission technologies for learning-based applications at the network edge
  • Scheduling schemes for efficient training, inference for edge learning/edge intelligence
  • Timely data acquisition mechanisms to support delay sensitive edge processing
  • Big data analytics for edge intelligence
  • Coded computing for edge intelligence
  • Enabling technologies, e.g., SDN, NFV, CRAN, D2D, cloud/fog computing and networking
  • Emerging applications via edge intelligence: vehicular networking, massive IoT, smart grid, healthcare, intelligent manufacturing
  • Novel network architecture: convergence of computing, communications and caching, content/information-centric network, cognitive computing and networking, big data analytics
  • Context-aware schemes: incentive mechanism for computing and caching, pricing, game-theoretic approach, network economics, caching placement and delivery
  • Mobility management for mobile edge computing and proactive caching, i.e., exploiting mobility for more computing and caching opportunities
  • Energy efficiency aspects: energy harvesting, energy storage, energy transfer
  • Novel pricing schemes for edge computing, communications, energy, AI model, data resources
  • AR/VR applications
  • Tactile Internet
  • Novel security technologies such as adapted blockchain, differential privacy, intrusion detection, traceability, etc.
  • Novel applications for edge intelligence such as autonomous driving, Industry 4.0, networked robots, networked UAV, smart grid, energy Internet, etc.
  • Prototyping, test-beds and field trials

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

 

Associate Editor:  Zhenyu Zhou, North China Electric Power University, China

Guest Editors:

    1. Takuro Sato, Professor, Waseda University, Japan
    2. Zheng Chang, Associate Professor, University of Jyvaskyla, Finland
    3. Sherali Zeadally, Associate Professor, University of Kentucky, USA
    4. Haris Gacanin, Department Head, Nokia Bell Labs, Belgium

 

Relevant IEEE Access Special Sections:

  1. Edge Computing and Networking for Ubiquitous AI
  2. Communication and Fog/Edge Computing Towards Intelligent Connected Vehicles (ICVs)
  3. Cloud-Fog-Edge Computing in Cyber-Physical-Social Systems (CPSS)


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: zhenyu_zhou@ncepu.edu.cn.

Recent Advances on Hybrid Complex Networks: Analysis and Control

Submission Deadline: 31 October 2020

IEEE Access invites manuscript submissions in the area of Recent Advances on Hybrid Complex Networks: Analysis and Control.

Due to varied complexities such as network dynamics complexity, statistical complexity and so on, some complex networks involve more than one discipline. Among network dynamics, both  impulsive effects and logical dynamics have attracted increasing attention recently. It is of interest and importance to study the complex networks with impulsive effects and logical dynamics. Note that these networks are called hybrid complex networks, which widely exist in cells, ecology, social systems and communication engineering.

In hybrid complex networks, many nodes are coupled together through networks, and their properties lead to very complex dynamic behaviors, including discrete and continuous dynamic behaviors, with both the time and state space taking finite values. The continuous parts of systems are often described by differential equations, while the discrete parts can be described by difference equations. The logical networks are usually used to model the systems where time and state space take finite values. Although interesting work has been reported on hybrid complex networks, there is some conservativeness on both the analysis method and relevant results. To be specific, conservative impulsive delay inequalities were used in some literatures and corresponding stability or synchronization criteria seem hard to check. Therefore, it is necessary to find effective approaches to break some conservativeness on both the analysis method and relevant results of hybrid complex networks.

Our proposed Special Section will provide a valuable and timely platform for the exchange of the latest advances in hybrid complex networks.

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

  • Analysis of hybrid complex networks: stability/ synchronization/ consensus/ robustness/ complexity analysis/ controllability/ observability/ nonsingularity
  • Synthesis of hybrid complex networks: stabilization/ disturbances decoupling problem/ functions perturbations/ attacks/ optimization

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

 

Associate Editor:  Jianquan Lu, Southeast University, China

Guest Editors:

    1. Daniel W. C. Ho, City University of Hong Kong, Hong Kong, China
    2. Tingwen Huang, Texas A&M University, Qatar
    3. Jürgen Kurths, Potsdam Institute for Climate Impact Research, Germany
    4. Ljiljana Trajkovic, Simon Fraser University, Canada

 

Relevant IEEE Access Special Sections:

  1. Complex Networks Analysis and Engineering in 5G and beyond towards 6G
  2. Body Area Networks
  3. Internet-of-Things Attacks and Defenses: Recent Advances and Challenges


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

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

For inquiries regarding this Special Section, please contact: jqluma@seu.edu.cn.

Lightweight Security and Provenance for Internet of Health Things

Submission Deadline: 31 October 2020

IEEE Access invites manuscript submissions in the area of Lightweight Security and Provenance for Internet of Health Things.

As an extension of the Internet of Things (IoT), the Internet of Health Things (IoHT), play an important role in the remote exchange of data of different physical processes such as patient monitoring, treatment progress, observation and consultation. In IoHT, the connectivity, integration, computation and interoperability are enabled through various sensors, actuators, and controllers, thereby providing seamless connectivity with efficient utilization of resources. In emergency situations, when a patient is being shifted to a hospital, seamless connectivity between Ambulance to Hospital (A2H), Hospital to Hospital (H2H) and Hospital to Ambulance (H2A), is very critical. With advances in tele-medicine, telesurgery, and other health-care applications, streaming has become an essential part of IoHT. The data traffic in IoHT applications, such as interactive multimedia streaming, traffic generated from faulty sensors, and vital signs, can tolerate packet loss but have stringent delay requirements. On the other hand, video streaming applications cannot tolerate jitter. Similarly, the low-power devices are sensitive to packet loss, and the periodic physiological traffic of medical traffic can tolerate delay, or jitter, but not packet loss.  Routing data in different IoHT applications has varying quality of service (QoS) requirements in terms of delay, packet loss, jitter, and throughput. Most of the algorithms used today to secure the data and cryptography techniques in IoHT contain high computational complexities with high energy consumption. However, due to the energy limitations of low-power embedded devices, traditional cryptographic solutions are not viable for most of the IoHT applications. Less computational complexity, less space acquisition and energy-efficient security primitives are key building blocks for end-to-end content protection, user authentication, and consumer confidentiality in the IoHT.  Once the data is gathered from different applications, it must be accurate and information about its origin should also be known. Due to scalability, tiny devices installed in IoHT are not usually physically protected. Data security and provenance therefore serve as the backbone for implementing IoHT applications.

This Special Section targets original technical articles with novel contributions on the improvement of security of IoHT, in particular by finding the correct lightweight solution. Review articles of high quality that provide thorough overview of the subject will also be considered. The topics of interest include, but are not limited to:

  • Lightweight security for IoHT
  • Low energy IoHT systems
  • Low energy security algorithms for A2H, H2H, and H2A in IoHT
  • Lightweight solutions for data forensics in IoHT
  • Lightweight routing algorithms for data provenance in IoHT
  • Secure lightweight protocols for A2H, H2H, and H2A in IoHT
  • Security framework and architecture for IoHT
  • Lightweight video streaming mechanism for IoHT

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

 

Associate Editor:  Muhammad Tariq, FAST National University of computer & Emerging Sciences, Pakistan and Princeton University, USA

 

Guest Editors:

    1. Takuro Sato, Waseda University, Waseda University, Tokyo, Japan
    2. Gautam Srivastava, Brandon University, Canada
    3. Vuk Marojevic, Mississippi State University, USA
    4. Mario Goldenbaum, Bremen University, Bremen, Germany

 

Relevant IEEE Access Special Sections:

  1. Secure Communication for the Next Generation 5G and IoT Networks
  2. Deep Learning: Security and Forensics Research Advances and Challenges
  3. Emerging Approaches to Cyber Security


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: mtariq@princeton.edu.