Advances on High Performance Wireless Networks for Automation and IIoT

Submission Deadline:  30 November 2022

IEEE Access invites manuscript submissions in the area of Advances on High Performance Wireless Networks for Automation and IIoT.   

High dependability and bounded transmission times are historically the main requirements of any communication networks conceived for automation. The recent pervasive introduction of wireless extensions to the wired backbones has opened new complex challenges, the most critical one being the ability to satisfy such requirements also over intrinsically unreliable communication supports like the radio spectrum.

Technologies for making devices communicate seamlessly over the air are expected to be adopted more and more in future digital ecosystems, including cyber-physical systems. The primary enabler is probably constituted by the Industrial Internet of Things (IIoT), which can be profitably applied to smart industry, smart environment, and smart agriculture, to cite a few. Thanks to IIoT, applications are hidden details about the underlying physical networks, as long as constraints on reliability and timeliness of end-to-end data transfers are overall met. Additional requirements often have to be considered, which impact on feasibility (technical, economical, and ecological); for example, power consumption may affect maintenance costs and battery waste, whereas communication range is a critical aspect in brownfield scenarios.

Because of the inherent complexity of wirelessly interconnected distributed systems, the relevant key performance indicators (KPI) to be used for design and optimization are application-driven, and usually the work of designers involves finding a compromise between a plurality of aspects, e.g., dependability, latency and jitter, power consumption, covered area, and node density.

It is worth stressing that, when dealing with IIoT, the term “high performance” does not refer simply to raw throughput, but rather to the ability of the network to satisfy in the best way and at the same time all the increasingly demanding requirements and constraints, both functional (mobility through wireless communication, ability to operate self-powered for very long times, support for safety and security, clock synchronization, etc.) and about performance (as expressed by above KPIs), dictated by modern distributed control applications for specific classes of (cyber-)physical systems. As an example, time, and consequently bounded latencies and synchronization, are essential for control applications: if the density of nodes is high, coordinated access to the channel is needed. If nodes are not fixed, low energy consumption and seamless mobility are other main requirements that need to be optimized to achieve high performance in this kind of network.

While a single winning wireless IIoT technology cannot be clearly identified, several competing solutions are currently available off-the-shelf. In the context of unlicensed bands, which are particularly appealing to users because they do not imply any fees, some of the most important ones are IEEE 802.11 (Wi-Fi), wireless sensor and actuator networks (WSN/WSAN) based on IEEE 802.15.4, including DSME and TSCH (Zigbee, WIA-PA, WirelessHART, ISA100.11a, 6TiSCH, etc.), Bluetooth Low Energy (IO-Link Wireless), and LoRaWAN. Concerning solutions operating in licensed bands, recent additions to 5G/6G, like URLLC and mMTC, are deemed particularly relevant in view of their use in the context of automation and sensing.

Current research on high-speed, highly dependable, and low-power wireless networks opens a promising door for the evolution of communications in automated systems, which will be heterogeneous in nature but, at the same time, capable of meeting very demanding constraints.

This Special Section aims to provide a forum for the academic and industrial communities to present the latest advances on wireless communication, with a specific focus on automation.

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

  • Dependable and timely wireless networking: protocols, algorithms, and architectures
  • Ultra-Reliable, Low-Latency, and Quasi-Deterministic wireless networks
  • Ultra-Low Power and Green wireless networks
  • Mesh, Long-Range, and Ultra-Dense wireless networks
  • Cross-Layer optimization of wireless protocol stacks
  • Software-Defined Radios (SDR) and Networks (SDN for wireless) to enhance communication KPIs
  • Coexistence and compatibility among wireless networks with performance optimization
  • High performance Mobile Ad Hoc Networks and opportunistic networking
  • Analysis, simulation, and modeling techniques in time-critical wireless systems
  • Extension of TSN features to wireless including IEEE 802.11 and 5G/6G cellular networks
  • Performance optimized integration and adaptation of 5G/6G systems with legacy industrial protocols
  • Standardization efforts on next generation wireless networks and convergence toward TSN
  • Precise time synchronization and localization over wireless networks
  • Reliable roaming and fast handover in wireless networks
  • Data compression techniques for high performance wireless networks
  • Machine learning to improve the quality of wireless communication
  • Wireless design for high performance applications in smart factories, smart agriculture, and smart environment
  • Non-5G high performance wireless networks for rural areas
  • PHY layer security mechanisms for URLLC wireless communication links
  • Fault mitigation for reliable wireless networks
  • Future demanding industrial applications that require high performance wireless networks

 

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Associate Editor:  Stefano Scanzio, CNR-IEIIT, Italy

Guest Editors:

    1. Hans-Peter Bernhard, Silicon Austria Labs and Johannes Kepler University, Austria
    2. Dave Cavalcanti, Intel Corporation, USA
    3. Gianluca Cena, CNR-IEIIT, Italy
    4. Lei Shu, Nanjing Agricultural University, China
    5. Iñaki Val, IKERLAN, Spain
    6. Lukasz Wisniewski, Institute Industrial IT – inIT of Technische Hochschule OWL, Germany

 

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

Article submission: Submit manuscripts to: http://ieee.atyponrex.com/journal/ieee-access

For information regarding IEEE Access, including its peer review policies and APC information, please visit the website http://ieeeaccess.ieee.org

For inquiries regarding this Special Section, please contact: stefano.scanzio@ieiit.cnr.it.

The Internet of Federated Things (IoFT)

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.

View this article on IEEE Xplore

 

Security and Privacy in Smart Farming: Challenges and Opportunities

Internet of Things (IoT) and smart computing technologies have revolutionized every sphere of 21 st century humans. IoT technologies and the data driven services they offer were beyond imagination just a decade ago. Now, they surround us and influence a variety of domains such as automobile, smart home, healthcare, etc. In particular, the Agriculture and Farming industries have also embraced this technological intervention. Smart devices are widely used by a range of people from farmers to entrepreneurs. These technologies are used in a variety of ways, from finding real-time status of crops and soil moisture content to deploying drones to assist with tasks such as applying pesticide spray. However, the use of IoT and smart communication technologies introduce a vast exposure to cybersecurity threats and vulnerabilities in smart farming environments. Such cyber attacks have the potential to disrupt the economies of countries that are widely dependent on agriculture. In this paper, we present a holistic study on security and privacy in a smart farming ecosystem. The paper outlines a multi layered architecture relevant to the precision agriculture domain and discusses the security and privacy issues in this dynamic and distributed cyber physical environment. Further more, the paper elaborates on potential cyber attack scenarios and highlights open research challenges and future directions.

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Positioning and Navigation in Challenging Environments

Submission Deadline:  31 July 2022

IEEE Access invites manuscript submissions in the area of Positioning and Navigation in Challenging Environments.   

In recent years, positioning and navigation has become a vital part of modern life especially with the continuous performance enhancement and modernization of the four global navigation satellite systems. Positioning and navigation industry has been growing quickly and has played a significant role in the industrial chain. Although great progress and many achievements have been made over the past few decades, there are a range of significant issues to be dealt with, especially in challenging environments.

In complex (e.g. large, multi-floor) indoor environments, it is a challenge to generate a valid positioning and navigation solution by a remote cloud platform with both offline and online data (e.g. WiFi and magnetic fingerprint data) recorded with smartphones The problem may become more complex if a pedestrian goes through different scenarios, such as from one floor to another floor of the same building, or from one building to another connected or neighboring building. There is a preference to avoid any interruption in the provision of valid position information. Thus, in the design of next-generation (beyond 5G) communication systems, the positioning functions need to be enabled and standardization of positioning technology such as in 3GPP should be taken into account.

As natural resources of the earth’s surface and shallow sea are becoming scarce, it is inevitable to acquire resources from deep underground, deep underwater and outer space. Regarding deep underground mining, there are currently a good number of deep mines in the world, including Mponeng Gold Mine and Tau Tona Mine, both located in South Africa with a depth of about 3.9km, and Kidd Creek Copper and Zinc Mine located in Ontario, Canada with a depth of about 2.9km. Deep underground mining is a challenging scenario which usually has high humidity and irregular space distribution, requiring stricter restrictions on the design and building of positioning and navigation systems.

Deep sea mining is promising because of abundant minerals on and under the deep seabed. For instance, a large amount of polymetallic nodules, containing rich concentrations of manganese, nickel, copper, and cobalt, are found in the Clarion-Clipperton Zone, a great abyssal plain as wide as the continental United States that lies 4 to 6 km below the surface of the eastern Pacific Ocean. Abundant naturel gas and oil also exist deep under the sea. Positioning and navigation is important for vehicles and robots to pick up the seabed surface minerals and to perform drilling and extraction of minerals under the seabed.

Space mining is currently a hot topic and should become a reality in the next few decades. , It is crucial to provide accurate and reliable positioning and navigation information for spacecraft and/or space robots which will approach and then usually land on the target planet (e.g. moon) or asteroid, followed by exploration, mining and so on; or simply catch and hold a rather small asteroid and move it back to Earth. For instance, a small Japanese space capsule carrying pristine pieces of the near-Earth asteroid Ryugu touched down on 5 December 2020, northwest of the South Australian capital of Adelaide. This was a successful initial step towards space mining on asteroids.

Positioning and navigation is vital for safe, reliable and effective operations in the scenarios of the frontier applications mentioned above. This Special Section aims to report the recent advances on positioning and navigation in such challenging scenarios. Researchers and engineers are also encouraged to perform more research and development to make advances in this area.

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

  • Positioning and navigation in complex indoor environments
  • Deep underground positioning and navigation
  • Positioning and navigation for deep ocean operations and mining
  • Positioning and navigation for space exploration and mining
  • Cloud computing for positioning and navigation
  • High sensitivity GNSS receivers
  • Suppression of GNSS jamming and spoofing
  • Positioning for communication systems beyond 5G
  • Standardization of positioning technology

 

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

 

Associate Editor:  Kegen Yu, China University of Mining and Technology, Mainland of China

Guest Editors:

    1. Andrew Dempster, University of New South Wales, Australia
    2. Pau Closas, Northeastern University, USA
    3. Shih-Hau Fang, Yuan Ze University, Taiwan
    4. Guenther Retscher, Vienna University of Technology, Austria
    5. Ali Broumandan, Hexagon Autonomy and Positioning, Canada

 

Relevant IEEE Access Special Sections:

    1. GNSS, Localization, and Navigation Technologies
    2. Intelligent Systems for the Internet of Things
    3. 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: kegenyu@foxmail.com.

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.

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

 

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.

Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk

Despite the perception people may have regarding the agricultural process, the reality is that today’s agriculture industry is data-centered, precise, and smarter than ever. The rapid emergence of the Internet-of-Things (IoT) based technologies redesigned almost every industry including “smart agriculture” which moved the industry from statistical to quantitative approaches. Such revolutionary changes are shaking the existing agriculture methods and creating new opportunities along a range of challenges. This article highlights the potential of wireless sensors and IoT in agriculture, as well as the challenges expected to be faced when integrating this technology with the traditional farming practices. IoT devices and communication techniques associated with wireless sensors encountered in agriculture applications are analyzed in detail. What sensors are available for specific agriculture application, like soil preparation, crop status, irrigation, insect and pest detection are listed. How this technology helping the growers throughout the crop stages, from sowing until harvesting, packing and transportation is explained. Furthermore, the use of unmanned aerial vehicles for crop surveillance and other favorable applications such as optimizing crop yield is considered in this article. State-of-the-art IoT-based architectures and platforms used in agriculture are also highlighted wherever suitable. Finally, based on this thorough review, we identify current and future trends of IoT in agriculture and highlight potential research challenges.

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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.)

 

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

 

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.

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.

Green Elevator Scheduling Based on IoT Communications

In this paper, we propose an energy-saving elevator scheduling algorithm to reduce the car moving steps to achieve motor energy saving and green wireless communications. The proposed algorithm consisting of six procedures can attain fewer Internet of Things (IoT) message exchanges (i.e. communication transmissions) between the Scheduler subsystem and the Car subsystem via the core function AssignCar(r). The function AssignCar(r) is capable of assigning a request to the nearest car through car search globally. From the emulation results for four cars, this work shows that the proposed algorithm outperforms the previous work named as aggressive car scheduling with initial car distribution (ACSICD) algorithm with energy consumption reductions by 49.43%, 47.68%, 37.89%, and 47.65% for up-peak, inter-floor, downpeak, and all-day request patterns, respectively.

View this article on IEEE Xplore

Wearable Sleepcare Kit: Analysis and Prevention of Sleep Apnea Symptoms in Real-Time

Obstructive sleep apnea (OSA), although it is a common symptom for ordinary people, is a serious issue in that it can lead to chronic and degenerative brain disease. However, these sleep disorders and apnea symptoms are difficult to diagnose at home or to recognize and cope with severe apnea situations. In response to this, we developed a Sleepcare Kit, an integrated wearable device. The Sleepcare Kit is a wearable distributed system in which the PAAR band and the bio-cradle are combined in the form of a hot plug-in without pre-setting. The PAAR band serves as a gateway for wireless communication with external devices and adjusts initial setting values for various sensors of the bio-cradle. Bio-cradle continuously measures/stores multiple bio-signals (PPG/SPO2, respiration, 3axis-acc, and body temperature) and analyzes the signal data to determine sleep quality and emergency situation in real-time. Although it is a set of small wearable devices, the kit itself diagnoses sleep quality on a real-time base without any external computing assistance while he/she is asleep. Simultaneously, it analyzes the gathered hypopnea and apnea data in real time and calculates the apnea risk phase. Moreover, according to the apnea risk phase, it can inform the wearer or guardian about the danger through the smartphone or smart-speaker. In this paper, we will discuss the algorithm that is used for the detection of sleep apnea in Sleepcare Kit, as well as the software platform for continuous measurement and synchronization of various bio-signals in real time. Moreover, we evaluated the accuracy of the system by comparing the obtained results with the polysomnography equipment used in hospitals.

View this article on IEEE Xplore