Purpose and Scope

The IEEE IoT Journal (IoT-J), launched in 2014 (“Genesis of the IoT-J“), publishes papers on the latest advances, as well as review articles, on the various aspects of IoT. Topics include IoT system architecture, IoT enabling technologies, IoT communication and networking protocols, IoT services and applications, and the social implications of IoT. Examples are IoT demands, impacts, and implications on sensors technologies, big data management, and future internet design for various IoT use cases, such as smart cities, smart environments, smart homes, etc. The fields of interest include:

    IoT architectures such as things-centric, data-centric, service-centric architecture, CPS and SCADA platforms, future Internet design for IoT, cloud-based IoT, and system security and manageability.
    IoT enabling technologies such as sensors, radio frequency identification, low power and energy harvesting, sensor networks, machine-type communication, resource-constrained networks, real-time systems, IoT data analytics, in situ processing, and embedded software.
    IoT services, applications, standards, and test-beds such as streaming data management and mining platforms, service middleware, open service platform, semantic service management, security and privacy-preserving protocols, design examples of smart services and applications, and IoT application support.

Editor-in-Chief

Nei Kato, Tohoku University, Japan (Email: eic-iotj.is@grp.tohoku.ac.jp)

Special Issue on Positioning and Sensing for Near Field (NF)-driven Internet-of-Everything
Submission Date: 2024-12-31

The advent of sixth-generation (6G) enabled positioning and sensing in Internet of everything (IoE) marks a transformative era in telecommunications, poised to improve data rates, latency, and reliability significantly. These advancements are propelled by the integration of ground breaking technologies like ultra-massive multiple-input-multiple-output (UM-MIMO), cell-free massive MIMO, reconfigurable intelligent surface (RIS), cognitive radio (CR) networks and terahertz communications. A pivotal shift in this progression is the transition to near-field (NF) communication, necessitated by adopting large aperture arrays. Unlike conventional far-field (FF) propagation, NF communication employs spherical wavefronts, offering several advantages over planar waves used in FF communications. One of the key benefits of spherical waves in NF communication is their energy efficiency, as they require less energy for data transmission over short distances due to rapid decay with distance. This characteristic is crucial for applications requiring high data rates and low latency, such as virtual reality, augmented reality, and industrial automation. Moreover, the spherical nature of the wavefront allows for precise localization based on the phase and amplitude of the received signal, making NF communication ideal for applications like indoor navigation and tracking. Despite the remarkable technical advancements towards 6G networks, the challenge of efficient positioning and sensing for IoE remains formidable. Note that conventional satellite-based positioning systems, like the global positioning system, face limitations in their application to emerging technologies due to extended transmission distances and unsatisfactory indoor performance resulting from complex propagation environments such as building/wall blockages. Therefore, a multifaceted approach is required that involves innovative designs for energy-efficient and spectrum-efficient transmission, thorough analysis, evaluation, experimentation, testing, and seamless integration of cognitive techniques, especially for efficient positioning and sensing under the NF-driven networks. These techniques include advanced machine learning algorithms for dynamic network optimization, AI-driven adaptive modulation and coding for real-time traffic demands, and context-aware resource management to maximize network efficiency and minimize energy consumption. Furthermore, IoE communications in NF networks must address the unique characteristics of spherical wavefronts, such as their rapid spatial variations and the need for precise beam alignment. This requirement opens up new research avenues in beamforming strategies, antenna design, and signal processing techniques specifically tailored for NF environments. The integration of IoE strategies with NF communication not only optimizes network performance but also paves the way for innovative applications such as immersive telepresence, ultra-precise industrial control systems, and next-generation IoT ecosystems. These applications demand not only high throughput and low latency but also intelligent network behavior to adapt to varying user requirements and environmental conditions for efficient positioning/sensing. Consequently, the transition to the radiating NF region necessitates a re-evaluation of existing wireless sensing, localization; beam focusing, communication and signal processing techniques towards developing an efficient positioning and sensing in IoE communication networks. In addition to this, the study of NF for IoE is still in its early stages, and there is a need for further research to fully understand its potential and challenges to support IoE communication. This special issue aims to bring together researchers from academia, industry, and government to explore these challenges and discuss future research directions in designing and shaping the next generation positioning/sensing techniques following NF assumptions. The topics of interest include, but are not limited to:

 mm-Wave and THz systems for positioning and sensing in IoE networks
 Reconfigurable intelligent surface (RIS)-assisted positioning and sensing in NF-driven IoE networks
 Resource allocation and network designs for positioning and sensing in under NF communications
 Hybrid FF and NF designs for efficient positioning and sensing in IoE
 Integrated sensing and communications (ISAC) in NF-driven IoE networks
 Physics- and electromagnetic-compliant modelling for NF-driven IoE networks
 Channel state information (CSI) in advanced NF-driven IoE networks
 CR based spectrum sharing in NF-driven IoE networks
 Semantic communications in NF-driven IoE networks
 NF-supported symbiotic communication for IoE
 Low-overhead beam training scheme for positioning and sensing in IoE
 Low cost and energy efficient hardware architecture design for positioning and sensing in IoE
 Internet of vehicles (IoV)/vehicle to vehicle (V2V)/ vehicle to infrastructure (V2I)/vehicle to everything (V2X) in NF-driven networks
 Machine learning driven techniques/designs for efficient positioning and sensing in NF-driven IoE networks
 Low-complexity beamforming design for efficient positioning and sensing in NF-driven IoE networks
 Resource management towards efficient positioning and sensing in NF-driven IoE networks
 Secure wireless communication in NF-driven IoE networks
 Definition of uses cases, application scenarios, and techno-economic analysis
 Real testbed and validation

Submission Guidelines:
All original manuscripts or revisions to the IEEE IoT Journal must be submitted electronically through IEEE Manuscript Central, http://mc.manuscriptcentral.com/iot. When the authors reach the “Article Type” step in the submission process, they should select “SI: Positioning and Sensing for Near Filed (NF)-driven Internet-of-Everything.” Solicited original submissions must not be currently under consideration for publication in other venues. Author guidelines and submission information can be found at http://ieee-iotj.org/guidelines-for-authors/.

Important Dates
Manuscript Submission Deadline: December 31st, 2024 
Acceptance Notification: May 1st 2025
First Decision Notification: February 28th 2025 
Final Manuscript Due: May 15th, 2025
Revised Manuscript Due: April 1st 2025 
Publication Date: July 2025

Guest Editors:

Keshav Singh (Lead Guest Editor) 
National Sun Yat-sen University, Kaohsiung, Taiwan
keshav.singh@mail.nsysu.edu.tw

Cunhua Pan (Lead Guest Editor) 
Southeast University, Nanjing, China 
cpan@seu.edu.cn

Linglong Dai
Tsinghua University, Beijing, China
daill@tsinghua.edu.cn

Yuanwei Liu
Queen Mary University of London, UK
yuanwei.liu@qmul.ac.uk

Chongwen Huang
Zhejiang University, China
chongwenhuang@zju.edu.cn

Octavia A. Dobre
Memorial University, Canada
odobre@mun.ca

Robert Schober
Friedrich-Alexander-University Erlangen-Nuremberg, Germany
robert.schober@fau.de

Sandeep Kumar Singh
Motilal Nehru National Institute of Technology Allahabad, India
sksingh@mnnit.ac.in

Special Issue on Digital Twin for 6G Internet of Everything
Submission Date: 2025-01-15

The exponential growth of intelligent devices has given rise to the emergence of the intelligent Internet of Everything (IoE) as a significant scenario within 6G networks. In this context, Digital Twin (DT) plays a vital role in integrating 6G with IoE by providing a virtual replica of physical entities, facilitating seamless communication and integration between the physical and virtual realms. By leveraging advanced machine learning (ML) techniques to analyze digital networks, DT empowers optimization and decision-making processes within the physical networks of IoE.

This creates transformative opportunities across domains such as smart transportation, smart healthcare, smart energy, and smart cities. However, achieving a high-precision DT network entails higher requirements for the 6G wireless communication system. These requirements include higher data rates, lower latency, enhanced reliability, and other crucial performance metrics. The seamless communication between the physical entities and their digital replicas necessitates robust and efficient wireless connectivity, capable of supporting the demanding needs of DT-based applications within the IoE.

This special issue aims to gather the latest advancements in DT for 6G IoE, exploring theoretical foundations, practical implementations, and real-world applications. Topics of interest include, but are not limited to:

• Architecture for DT 6G IoE networks
• DT enabled 6G networking/6G enabled DT networks
• DT IoE application areas, including manufacturing, healthcare, smart transportation, and smart cities
• Cloud, edge, and hybrid deployment of DT in 6G networks
• MEC-assisted DT deployment/ DT-assisted MEC networking
• AI for DT in 6G IoE Networks
• Security and privacy in DT IoE networks
• Dynamic DT networks in IoE
• Prototype and testbeds for DT IoE
• DT interactive with other emerging 6G technologies

Important Dates:
Submission Deadline: January 15th, 2025
First Review Due: March 1st, 2025
Revision Due: April 15th, 2025
Second Review Due/Notification: May 15th, 2025
Final Manuscript Due: May 31st, 2025
Publication Date: August 2025

Submission Guidelines:
All original manuscripts or revisions to the IEEE IoT Journal must be submitted electronically through IEEE Manuscript Central, http://mc.manuscriptcentral.com/iot. When the authors reach the “Article Type” step in the submission process, they should select SI: Digital Twin for 6G Internet of Everything. Solicited original submissions must not be currently under consideration for publication in other venues. Author guidelines and submission information can be found at http://ieeeiotj.org/guidelines-for-authors/.

Guest Editors:
Yaru Fu (yfu@hkmu.edu.hk), Hong Kong Metropolitan University, Hong Kong SAR, China
Chung-Shue Calvin Chen (chung_shue.chen@nokia-bell-labs.com), Nokia Bell Labs, France
Wen Sun (sunwen@nwpu.edu.cn), Northwestern Polytechnical University, China
Tony Q. S. Quek (tonyquek@sutd.edu.sg), Singapore University of Technology and Design, Singapore
Yan Zhang (yanzhang@ieee.org), University of Oslo, Norway

Special Issue on Human Machine Interaction in Industrial IoT Flexible Manufacturing with Advanced AI Tools: Emerging Application and Challenges
Submission Date: 2025-01-31

With the explosive growth in Industrial Internet-of-things (IIoT) devices, applications and services have also substantially expanded in recent years. Due to the various market demands and device potentials, flexible manufacturing that can quickly adjust the production scale and product structure according to specified requirements is the new trend. Besides, artificial intelligence (AI) technologies, such as digital twins, machine learning, and natural language processing (NLP), have laid a solid foundation for the advancement of industrial flexible manufacturing. Distributed and heterogeneous features of the IIoT systems require multiple channels to work in a collaborative way to bridge the service/application and device domains, especially human-machine- interaction (HMI). Compared with the traditional IIoT HMI methods for displaying and relaying information, such as machine conditions, output rates, and error signals, the advanced AI tools (ML, NLP, CV) can enhance the interaction capabilities and make it more user-centered, context-aware, adaptive, and intelligent. A variety of questions related to designing and applying new HMI approaches in IIoT flexible manufacturing with advanced AI tools need to be solved, including the control and design of hybrid systems with new interaction methods, design HMI that can meet the diverse and dynamic needs of different users, tasks, and situations in the IIoT flexible manufacturing environments, the security and privacy of the IIoT devices data and applications, error correction and explanation mechanisms for the AI-assisted solutions, and so on.

This special issue intends to encourage high-quality research in IIoT flexible manufacturing, and push the theoretical and practical bound forward for a deeper understanding of architectures, techniques, services/toolkits, and business value. The submitted work should be unpublished technical articles with a substantial novel contribution towards the scope. The topics of interest include, but are not limited to the following:

⚫ Edge/Fog/Cloud Computing in IIoT flexible manufacturing
⚫ Multi-Agent and workflow for the IIoT flexible manufacturing
⚫ Data Analysis with AI in IIoT flexible manufacturing
⚫ Digital twins technologies in IIoT flexible manufacturing
⚫ Service Computing and Recommendation in IIoT flexible manufacturing
⚫ Novel human-machine-interaction approaches in IIoT flexible manufacturing
⚫ Novel QoS Management in IIoT flexible manufacturing
⚫ Security and data privacy in IIoT flexible manufacturing
⚫ Reliability Evaluation and On-line Monitoring in IIoT flexible manufacturing
⚫ Novel Industrial Application Design Systems/Tools in IIoT flexible manufacturing
⚫ Emerging Tools and benchmarks for HMI in IIoT flexible manufacturing

Important Dates
• Manuscript Submission Due: January 31st, 2025
• First Round of Reviews Completed: March 31st, 2025
• Revision Due: April 25th, 2025
• Second Review Due/Notification: May 25st, 2025
• Final Manuscript Due: June 15th, 2025
• Publication Date: August 2025

Submission
The original manuscripts to be submitted need to follow the guidelines described at: http://ieeeiotj.org/guidelinesfor-authors/, which should not be concurrently submitted for publication in other venues. Authors should submit their manuscripts through the IEEE Manuscript Central at: https://mc.manuscriptcentral.com/iot. The authors must select as "SI: Human Machine Interaction in Industrial IoT Flexible Manufacturing with Advanced AI Tools: Emerging Application and Challenges" when they reach the "Article Type" step in the submission process.

Guest Editors:
Prof. Honghao Gao, Shanghai University, China (gaohonghao@shu.edu.cn)
Prof. Hussain Walayat, Australian Catholic University, Australia (Walayat.Hussain@acu.edu.au)
Prof. Ramón J. Durán Barroso, Universidad de Valladolid, Spain (rduran@tel.uva.es)
Prof. Anwer Al-Dulaimi, Veltros and ZU University, Canada(anwer@veltris.com)

Special Issue on Carbon Intelligent Industrial Internet of Things
Submission Date: 2025-02-15

Industry 5.0 is empowered by a wide range of modern-day technologies like artificial intelligence (AI), the Internet of Things (IoT), communication systems (like 5G/6G), network architectures (e.g. software-defined networking), and robotics. IoT acts as one of the most important enablers for Industry 5.0 or smart city applications or autonomous (self-driven) transportation. It connects a wide array of data generators (devices, sensors, people) and empowers them to communicate and seamlessly interact with each other. IoT collects data (or rather big data) and processes it (in bulk or real-time) at massive data centers (cloud) or local compute servers (edge) to improve the underlying processes, making them a measurable and more quantifiable, paving the way for Industrial IoT (IIoT). IIoT engulfs AI-based technologies to provide enhanced automation and embedded intelligence to realize decision-making in real time and achieve optimized decisions at a lower cost. The key driving force behind industrial applications (like smart factories, intelligent aerodynamic systems, and smart defence ecosystems) is to improve the quality of everything (QoE) within the stipulated industrial environment.

Although the potential benefits of the IIoT paradigm overweight its challenges, one key challenge that has been a concern globally is related to the increasing energy consumption. In the conventional ecosystem, the reliance on data centers to process the massive amount of data generated by IoT devices has ended up in the expansion of cloud data centers (involving millions of servers at one or more locations). This expansion directly impacts energy consumption and carbon emissions globally. Thus, in the past several years, the research spectrum has been concerned about this issue, and many solutions have emerged to tackle this issue in partial. The concerns around climate change have led to energy efficiency being a key vertical in the United Nations Sustainable Development Agenda as well. A recent report1 suggested that although IoT technologies will burden the global energy consumption by 34 TWh by 2030, the use of intelligent IoT solutions will help to reduce energy consumption by 1.6 PWh (i.e., enough energy to support 136.5 million homes every year). Addressing the key challenge of climate change and ever-increasing carbon footprints, carbon-intelligent computing can be utilized to manage the ever-increasing energy demands of computing paradigms. For instance, a carbon intelligent solution may leverage AI technology to understand the energy forecasts, predict hourly carbon intensity, predict internal hourly power requirements, and finally align the tasks with low-carbon energy supply in an intelligent manner. This way, the
future can witness minimized carbon emissions by shifting IIoT loads based on time and location. Here, it is important to understand the difference between carbon-aware computing (based on time when one can use more energy from sources like solar and wind) from carbon-intelligent computing which considers both time and location where carbon-free energy is available.

This special issue will act as a platforms to put forward novel solutions and techniques to support the carbon intelligent IIoT ecosystem. This special issue will cover various aspects related to carbon intelligence for IIoT or vice-versa. We call upon the researchers (working in academia or industry) to share their latest unpublished high-quality research work on carbon-intelligent computing for IIoT. The relevant topics include, but are not limited to, the following:

• Protocol design for embedding carbon intelligence in IIoT
• Carbon intelligent resource allocation and management
• Modelling and performance analysis for Carbon intelligent IIoT
• Machine learning, deep learning for intelligent IIoT
• QoS and QoE provisioning for carbon intelligent IIoT
• Carbon vs Reliability for low-latency IIoT communications
• Energy and scalability issues in IoT, edge and cloud
• Security and privacy issues for carbon intelligent IIoT
• Implementation and testbeds for carbon intelligent IIoT
• Deep computing and learning methodologies carbon intelligent IIoT
• Edge-enabled carbon intelligent systems
• Sustainable computing for for IIoT, edge and cloud
• Green computing for IIoT, edge and cloud
• Carbon intelligent osmotic computing
• Energy-efficient solutions for IIoT, edge and cloud
• Electric vehicles as a service for IIoT, edge and cloud
• Smart Grid and distributed energy resources for IIoT, edge and cloud
• IIoT and AI for improvising smart grid operations

Guest Editors

• Gagangeet Singh Aujla, Durham University, UK 
Email: gagangeet.s.aujla@durham.ac.uk

• Amir H. Gandomi, University of Technology Sydney, Australia; Obuda University, Hungary
Email: gandomi@uts.edu.au, amirhossein.gandomi@uts.edu.au

• Danda B. Rawat, Howard University, USA
Email: danda.rawat@howard.edu

• Chunxiao Jiang, Tsinghua University, China
Email: jchx@tsinghua.edu.cn

Important Dates
• Manuscript Submission Deadline: February 15th, 2025
• Initial Decision Date: April 15, 2024
• Revised Manuscript Due: May 15, 2025
• Second Reviews Due/Final Decision Date: June 15th, 2025
• Final Manuscript Due: June 30th, 2025
• Publication Date: September 2025

Submission:
All original manuscripts and revisions must be submitted electronically through IEEE Manuscript Central http://mc.manuscriptcentral.com/iot, where, at the “Article Type” step, authors are must select the correct special issue name. Solicited original submissions must not be currently under consideration for publication in other venues. Author guidelines and submission information can be found at https://ieee-iotj.org/.

Special Issue on Edge AI Models for Social Internet of Things
Submission Date: 2025-02-28

With the Internet of Things (IoT) providing the umbrella under which many heterogeneous technologies and objects are interacting and cooperating, the need to enhance its performance with characteristics from other more mature technologies is rising. Especially, the Social Internet of Things (SIoT) that refers to the convergence of the Social Networks with the IoT paradigm, creates a “social network of intelligent objects”, where smart objects are capable of establishing social relationships between themselves. The SIoT paradigm achieves efficient and scalable network navigability for service and object discovery, by exploiting the analytics and statistical models used in social networks.

Even though the preliminary results on the aforementioned issues are encouraging, more examples and applications need to be studied to provide for a comprehensive study of the area. Issues related to the design of an efficient SIoT architecture, the cognition of objects especially to implement various and different social behavior characteristics, along with increasing social-based trustworthiness are areas where further study can provide results that could promote SIoT as one of the currently most pervasive and ubiquitous paradigms. Utilizing the edge computing capabilities to facilitate the fast development SIoT is considered as a promising solution. Artificial intelligence (AI) technologies can be deployed at the network edge to complement the analysis of social network and physical characteristics of the IoT. In this context,
Foundation models, large language models (LLMs), generative adversarial networks (GAN) and diffusion models, and many others, which are pretrained on massive amounts of data, can be implemented via edge computing, and network edge can
obtain the ability to learn complex relationships and patterns in data. Drawing inspiration from these successes, this proposal seeks to unite researchers in the development of AI models, such as foundation models and LLM models, at
network edge tailored for SIoT development. The objective is to enhance the efficiency, accuracy, and understanding of edge AI-enhanced SIoT systems, enabling the unified analysis of social network and IoT. Anticipated outcomes include improved accuracy in network analysis, network architecture development, efficient extraction of meaningful patterns, and enhanced adaptability for SIoT scenarios. Contributions are invited on diverse aspects of edge AI for SIoT, encompassing but not limited to:

⚫ Performance evaluation for Edge AI models in SIoT
⚫ Novel datasets and benchmarks for building domain-specific Edge AI models
⚫ Edge AI models for social network analysis in SIoT
⚫ Multimodal data analysis in the social context at network edge
⚫ Architecture and framework for accommodating AI models in edge SIoT systems
⚫ Edge AI-based real-time decision making in SIoT systems
⚫ Edge AI energy-aware approaches in SIoT systems
⚫ Edge AI-based Resource management for SIoT systems
⚫ Edge AI-based Security and privacy-preserving approaches for SIoT systems
⚫ Edge AI for semantic communications, integrated sensing and communications, and mobile offloading in the context of SIoT
⚫ Use cases/applications highlighting the potential of edge AI in SIoT systems

We encourage researchers and practitioners to contribute their latest advancements and insights, collectively advancing the field of SIoT through the development and application of edge AI models.

Guest Editor:

Zheng Chang, University of Electronic Science and Technology of China, China, (zheng.chang@uestc.edu.cn)
Ryosuke Shibasaki, University of Tokyo, Japan,(shiba@csis.u-tokyo.ac.jp)
Li Wang, Beijing University of Posts and Telecommunications, China, (liwang@bupt.edu.cn)
Zhu Han, University of Houston, U.S. (zhan2@uh.edu)
Giuseppe Araniti, University Mediterranea of Reggio Calabria, Italy. (araniti@unirc.it)

Important Data
Submission Deadline: February 28th, 2025
Date first review round completed: April 30th, 2025
Date revised manuscripts due: May 31st, 2025
Final notification: July 1st, 2025
Final Manuscript Due: July 15th, 2025
Publication Date: September 2025

Special Issue on Responsible and Federated Foundation Models for IIoT
Submission Date: 2025-03-15

By incorporating cutting-edge IoT sensing, big data analysis, and artificial intelligence into industrial production process, Industrial Internet of Things (IIoT) plays important roles in enhancing production efficiency, reducing resource and power consumption, improving product quality, and so on. The current large parametric foundation models, represented by ChatGPT and Sora, have made significant progress, particularly in domains such as natural language processing and
computer vision. The foundation model can collaborate with or optimize smaller scale models, and combining the two can provide more efficient solutions for the IIoT. At the same time, the emergence of credible federated learning introduces a paradigm that supports collaborative model training across distributed devices, thereby safeguarding data privacy and maximizing data utilization efficiency. This development aligns seamlessly with the stringent data privacy requirements of IIoT applications. Consequently, credible/federated foundation models for IIoT are very promising by the integration of credible federated learning and foundation models. While some advancements have been achieved in this field, there remains a compelling need for further exploration of credible and federated foundation models, also the framework for credible collaboration between foundational and small models.

The IEEE Internet of Things Special Issue on Credible and Federated Foundation Models for IIoT strives to comprehensively address various facets intertwined with credible federated learning and foundation models within the domain of IIoT. We invite authors to submit their latest original research results on the following topics, but are not limited to:

⚫ Cross-modal and multimodal foundation models for IIoT
⚫ Refinement of foundation models through fine-tuning for IIoT
⚫ Learning paradigm for training foundation models via federated learning for IIoT
⚫ Collaboration mechanisms between foundation models and small models
⚫ Federated unlearning approach for foundation models after organizational changes in the IIoT
⚫ Federated retrieval augmented generation (RAG)
⚫ Framework for fusion or collaboration between foundation models
⚫ Reasoning optimization of foundation models and federated learning for IIoT
⚫ Ensuring responsibility of IIoT using Federated Learning and Foundation Models
⚫ Optimization strategies for Federated Learning and/or pre-training foundation models targeted at IIoT applications
⚫ Establishing large foundation model repositories for IIoT applications
⚫ Federated life long learning and AI governance control in IIoT
⚫ Responsible AI engineering for foundation model-based industrial applications

Submission Information
The original manuscripts to be submitted need to follow the guidelines described at: http://ieeeiotj.org/guidelinesfor-authors/, which should not be concurrently submitted for publication in other venues. Authors should submit their manuscripts through the IEEE Manuscript Central at: https://mc.manuscriptcentral.com/iot. The authors must select as "SI: Responsible and Federated Foundation Models for IIoT" when they reach the Article Type" step in the submission
process.

Important Dates
Submission Deadline: March 15th, 2025
First Reviews Due: May 1, 2025
Revision Due: June 1, 2025
Second Reviews Due/Notification: July 1, 2025
Final Manuscript Due: July 31st, 2025
Publication Date: October 2025

Guest Editors
Weishan Zhang (zhangws@upc.edu.cn), China University of Petroleum (East China), China
Paolo Bellavista (paolo.bellavista@unibo.it), University of Bologna, Italy
Lu (Qinghua.Lu@data61.csiro.au), Data61, CSIRO, Australia
Xiaokang Zhou (zhou@kansai-u.ac.jp), Kansai University, Japan
Chonggang Wang (CGWANG@IEEE.org), InterDigital, USA

Special Issue on AIoT-enabled Secured and Green Supply Chain Systems: Challenges & Opportunities
Submission Date: 2025-03-31

Supply Chain Systems (SCS) range from the procurement of raw materials to the production and delivery of items. The main stakeholders in SCS are vendors, suppliers, and retailers responsible for delivering the right product to the right customer on time. This can be achieved through intelligent technologies such as Artificial Intelligence (AI)-based Internet of Things (IoT). The AI-based IoT (AIoT) performs real-time tracking of products, sustaining a green environment by reducing carbon emissions as the entire system is digitally connected. The AIoT-based systems provide numerous opportunities, however, they have certain challenges that need to be overcome.

For example, digital systems are prone to adversarial attacks, and their security is of utmost importance for the smooth operations of green SCS. The secured and green SCS are efficient and cost-effective for sourcing, manufacturing, delivering, and returning products. In this system, the IoT devices monitor the external world and securely track the products. These devices can also be deployed to enhance the storage conditions of the products for quality assurance while sustaining a green environment.

Although the AIoT-enabled SCS is at the initial stage it will use next-generation technologies such as big data, cloud computing, 5/6G, and cybersecurity for efficient operations. However, a big challenge is making a global AIoT-enabled secured and green SCS by joining multiple sites. Furthermore, designing such a system for small-scale industries is another challenge as they have limited resources. Hence, we need an intelligent, secure, green, economical, global, and fast SCS
to accelerate business opportunities, serve customers better, save money, and improve efficiency.

This special issue aims to collect recent advancements, innovations, and industry-oriented practices in AIoT-enabled secured and green SCS to provide excellent customer satisfaction and business growth at a low cost. The topics of interest include, but are not limited to:

• IoT for Supply Chain Resilience and Agility
• Sustainable and green practices through AIoT in SCS
• Blockchain technology for transparency in AIoT-enabled SCS
• AIoT innovations in logistics and smart warehousing
• IoT-driven innovations in logistics in autonomous vehicles and drone delivery
• Energy efficiency and carbon footprint reduction in SCS via IoT
• 5/6G and edge computing integration for real-time operations in SCS
• Predictive analytics and intelligent decision-making in SCS
• IoT-enabled circular economy models for green and secured SCS
• Cross-industry impact of AIoT on global SCS
• Smart contract applications in IoT for privacy and security in supply chain transactions
• AIoT in enhancing product lifecycle management and traceability
• AIoT-driven solutions for supply chain risk management and mitigation
• Interoperability and standards for AIoT in supply chain ecosystems

Guest Editors:

1. Dr. Fazlullah Khan
Business Technology and Management Group, Chicago IL 60504, USA
fazlullah.mcs@gmail.com

2. Prof. Saru Kumari
Chaudry Charan Singh University, Uttar Pradesh, Meerut, India
saryusiirohi@gmail.com

3. Dr. Muhammet Deveci
National Defence University, Turkey
muhammetdeveci@gmail.com

4. Prof. Joel J. P. C. Rodrigue
Lusófona University, Lisbon, Portugal
joeljr@ieee.org

5. Prof. Gautam Srivastava
Brandon University, Manitoba, Canada
srivastavag@brandonu.ca

Submission Deadlines:
Submission Deadline: March 31st, 2025
First Reviews Due: April 30th, 2025
Revision Due: May 31st, 2025
Second Reviews Due: June 30th, 2025
Final Manuscript Due: August 15th, 2025
Publication Date: October 2025

Special Issue on Security and Privacy in Large Language Models for Internet of Things (IoT)
Submission Date: 2025-04-15

Large Language Models (LLMs) such as ChatGPT have revolutionized many aspects of modern life with their powerful generation, contextual understanding, and multi-modal processing. Meanwhile, the rapid advances of Internet of Things (IoT) devices have led to the proliferation of smart devices from the home to the industrial. These devices are equipped with advanced sensors and communication technologies that can collect and transmit large amounts of data. Combining these IoT devices with LLMs not only enhances communication capabilities, but also creates a new interaction paradigm that
efficiently integrates human needs with machine intelligence, significantly improving efficiency and quality of intelligent services.

The combination of LLMs and IoT devices brings intelligence to the devices, but also data security and privacy issues. However, it is challenging to safeguard the security and privacy of these devices due to the IoT devices variety, the complex structure of LLMs, and the diversity of data. To address these challenges, this special issue collects the latest research outcomes and developments on the security, privacy and trust in combination of LLMs and IoT devices. Topics of interest include but are not limited to:

 LLMs-Enhanced Authentication for IoT Devices
 LLMs-Based Access Control for IoT
 LLMs for Truth Verification in IoT Sensing
 LLMs for Data Privacy in IoT Cloud Services
 Federated LLMs Learning in IoT Security
 LLMs-Driven Trust Management for IoT
 Blockchain and LLMs Convergence for IoT Security
 Edge-Deployed LLMs Security in IoT
 Incentive Strategies for LLMs Interaction in IoT
 LLMs Applications for Smart City IoT Security

Important Dates:
Submission Deadline: April 15th, 2025 Sec. Reviews Due/Notification: August 15th, 2025
First Review Due: May 30th, 2025 Final Manuscript Due: October 30th, 2025
Revision Due: July 15th, 2025 Publication Date: January 2026

Submission:
The original manuscripts to be submitted need to follow the guidelines at: http://ieeeiotj.org/guidelinesfor-authors/, which should not be concurrently submitted for publication in other venues. Authors should submit their manuscripts through the IEEE Manuscript Central at: https://mc.manuscriptcentral.com/iot. The authors must select as "SI: Security and Privacy in Large Language Models for Internet of Things (IoT)" when they reach the "Article Type" step in the submission process.

Guest Editors:
 Haomiao Yang, University of Electronic Science and Technology of China, China (haomyang@uestc.edu.cn)
 Tianwei Zhang, Nanyang Technological University, Singapore (tianwei.zhang@ntu.edu.sg)
 Rongxing Lu, University of New Brunswick, Canada (RLU1@unb.ca)
 Hyunsung Kim, Kyungil University, Kyungbuk, Korea (kim@kiu.ac.kr)
 Kuan Zhang, University of Nebraska–Lincoln, United States (kuan.zhang@unl.edu)

Special Issue on Symbiotic Internet-of-Things for Giant AI in the 6G Era
Submission Date: 2025-04-15

Incepted by the popularity of ChatGPT, Giant AI models have demonstrated unprecedented capability in handling various language and multimodal tasks. Since giant AI models incurs significant computing and memory costs, it is a common practice to deploy them, for both training and inference, in cloud data centers with vast computing resources. However, such a cloud-centric deployment of giant models often fails to meet the efficiency and security requisites of IoT devices.

Symbiotic Internet-of-Things (IoT) is envisioned as a promising computing paradigm where IoT devices and systems are designed to work in close cooperation with advanced AI models, particularly at the network edge, rather than relying solely on centralized cloud computing resources. This approach seeks to create a mutually beneficial relationship - a symbiosis - between IoT devices, as well as larger computational systems to which they connect. In the context of "symbiotic" computing, IoT devices engage in a tightly-knit interaction that extends beyond mere connectivity. They share resources and collaboratively manage computing tasks through the high-speed capabilities of 6G networks, enabling them to jointly perform inference and training of large-scale AI models. This is not a simple cooperative effort, but an interdependent relationship where the performance and efficiency of one participant are significantly enhanced by the others.

Building symbiotic IoT for giant AI over the 6G network needs foundational innovations of the whole communication and computing infrastructure, across edge to the cloud. We also rely on machine learning, blockchain, and other inter-disciplinary techniques to address the unique challenges posed by integrating giant AI models with IoT infrastructures, ensuring that systems are secure, adaptable, and resilient. This special issue looks for original and high-quality research works related to the following topics, but not limited to:

• Design and Optimization of Distributed Architectures for Symbiotic IoT
• Novel Giant AI Model Structures for Symbiotic IoT
• 6G Network Innovations for Enhanced IoT-AI Integration
• Security and Privacy for Symbiotic IoT Ecosystems
• Resource Management and Scalability in Symbiotic IoT Systems
• Robustness and Reliability of AI Services in Symbiotic IoT
• Federated Learning for giant AI models in symbiotic IoT
• Blockchain for symbiotic IoT empowered by giant AI

Important Dates:
• Submission Deadline: April 15, 2025
• First Review Due: May 31, 2025
• Revision Due: June 30, 2025
• Second Review Due/Notification: July 31, 2024
• Final Manuscript Due: August 31, 2025
• Publication Date: November 2025

Submission Guidelines:
All manuscripts to the IEEE IoT Journal must be submitted through IEEE Manuscript Central, http://mc.manuscriptcentral.com/iot. Solicited original submissions must not be currently under consideration for publication in other venues. Author guidelines and submission information can be found at http://ieee-iotj.org/guidelines-for-authors/.

Guest Editors:
Peng Li, The University of Aizu, Japan, pengli@u-aizu.ac.jp
Francesco Flammini, University of Applied Sciences and Arts of Southern Switzerland, francesco.flammini@supsi.ch
Jing Deng, University of North Carolina at Greensboro, USA, jing.deng@uncg.edu
Song Guo, Hong Kong University of Science and Technology, Hong Kong, songguo@cse.ust.hk
Giancarlo Fortino, University of Calabria, giancarlo.fortino@unical.it

Special Issue on Data and Knowledge-Empowered Distributed Learning for Internet of Unmanned Agents
Submission Date: 2025-04-30

With the rapid development of ubiquitous networks and unmanned devices, numerous unmanned devices are interconnected via wireless networks to form a powerful distributed unmanned system, i.e., Internet of Unmanned Agents (IUA). By leveraging network communication technology for data transmission and sharing across the unmanned agents in the IUA, individual agents within the IUA can collect environmental data to identify targets or perform other important tasks. The learning information is aggregated and analyzed to support IUA decision-making and operations, and IUA can effectively monitor and comprehend their environment, thereby enabling smarter and more flexible behaviors.

The learning results of the IUA by data-driven approaches heavily depend on the quality of the data. If the data contains issues such as missing, erroneous, duplicated, or biased information, it may lead to inaccurate or distorted learning results. Knowledge-driven learning approaches rely heavily on existing knowledge bases. If the available knowledge is limited, outdated, or incorrect, it may impact the effectiveness and accuracy of the learning approaches. Much valuable knowledge exists in tacit form, which can be challenging to capture and represent formally. This may result in gaps in knowledge-driven systems, limiting their ability to address complex and nuanced problems. The data-knowledge dual-driven approach combines the strengths of both learning methods, enabling the comprehensive utilization of data and domain knowledge to fully leverage their roles in problem-solving and decision support. By integrating data analysis with domain expertise, it enhances the comprehensiveness and accuracy of problem-solving solutions. The objective of this special issue is to solicit high-quality original research papers, which address open issues in data and knowledge-empowered distributed learning for IUA from both academia and industry.

Topics include, but are not limited to the following:
 Data and knowledge-empowered distributed learning architectures for IUA
 Data and knowledge-empowered distributed learning algorithms for IUA
 Design of lightweight data and knowledge-empowered distributed learning algorithm for IUA
 Resource management based on data and knowledge-empowered distributed learning for IUA
 Practical application based on data and knowledge-empowered distributed learning for IUA
 Test and evaluation tools for data and knowledge-empowered distributed learning for IUA
 Data and knowledge-empowered distributed learning platforms in IUA
 Future of data and knowledge-empowered distributed learning for IUA

Submission:
All original manuscripts or revisions to the IEEE IoT Journal must be submitted electronically through IEEE Manuscript Central, http://mc.manuscriptcentral.com/iot. Author guidelines and submission information can be found at http://ieee-iotj.org/.

Important dates:
Submission Deadline: April 30th, 2025 
First Round Review Due: June 30th, 2025
Final Manuscript Due: September 15th, 2025 
Publication Date: November 2025

Guest Editors:
Liangtian Wan, Dalian University of Technology, China, Email: wanliangtian@dlut.edu.cn.
Wei Zhang, The University of New South Wales, Australia, Email: w.zhang@unsw.edu.au.
Feifei Gao, Tsinghua University, China, Email: feifeigao@tsinghua.edu.cn.
Wei Liu, Queen Mary University of London, UK, Email: w.liu@qmul.ac.uk.
Wali Ullah Khan, University of Luxembourg, Luxembourg, Email: waliullah.khan@uni.lu

Special Issue on Ultra-Reliable and Low-Latency Satellite Communications for Ubiquitous Internet of Everything (IoE)
Submission Date: 2025-04-30

Driven by the proliferating Internet of Everything (IoE) applications, satellite communication emerges as a key enabling
technology to establish an omnidirectional network architecture for global IoE service provision. IoE yields a significant
paradigm shift from enhanced mobile broadband services to ultra-reliable low-latency communications. In this context,
a tradeoff between low latency and ultra-high reliability necessitates in-depth investigations since the latency is
typically negative with reliability. However, satellite communications suffer from prohibitive service latency due to
long transmission distance, serious blockage effect, and severe propagation attenuation. Even worse, the intrinsic high-
dynamic network typologies and the serious Doppler effects in satellite networks pose significant reliability challenges
for service delivery. To support ubiquitous IoE services, innovative research efforts are required for efficient satellite
communications, thereby jointly enhancing the response latency and service reliability.

The research of ultra-reliable and low-latency satellite communications for ubiquitous IoE is still in its infancy and
calls for more extensive and in-depth research efforts. Towards that end, this special issue aims to provide a venue to
exchange recent advances in this topic. In this special issue, we look for original and high-quality research works in
the novel area of ultra-reliable and low-latency satellite communications for ubiquitous IoE. Theoretical, experimental
studies, and also case studies are highly encouraged. Relevant topics include, but are not limited to:

• Architectural enhancement for service provisioning in satellite networks
• Satellite network resource management and optimization
• Advanced antenna-enabled satellite communications
• Optical and terahertz communications for satellite networks
• Prototypes and testbeds for satellite networks
• Physical-layer security and covert communication in satellite networks
• Grant-free/grant-based multi-access technology for satellite communications
• AI-empowered satellite network orchestration
• Age of information in satellite communications
• Multi-access edge computing-enabled satellite communications
• Satellite-enabled integrated sensing and communication
• Synchronous and asynchronous transmission in satellite networks
• Heterogeneous multi-tier satellite networks
• New modulation and multiple access schemes for integrated ground-space networks
• Mobility and handover management for satellite communications
• Robust and low-complexity physical-layer authentication for satellite networks

Submission Guidelines:
All original manuscripts or revisions to the IEEE IoT Journal must be submitted electronically through IEEE Manuscript
Central, http://mc.manuscriptcentral.com/iot. When the authors reach the Article Type step in the submission process,
they should select SI: Ultra-reliable and low-latency satellite communications for ubiquitous Internet of Everything
(IoE). Solicited original submissions must not be currently under consideration for publication in other venues. Author
guidelines and submission information can be found at http://ieee-iotj.org/guidelines-for-authors/.

Important Dates:
• Submission Deadline: April 30, 2025
• First Reviews Due: July 30, 2025
• Revision Due: October 1, 2025
• Second Reviews Due/Notification: November 1, 2025
• Final Manuscript Due: November 15, 2025
• Publication Date: January 2026

Guest Editor:
• Long Yang (lyang@xidian.edu.cn), Xidian University, China.
• Lu Lv (lulv@xidian.edu.cn), Hangzhou Institute of Xidian University, China.
• Arumugam Nallanathan (a.nallanathan@qmul.ac.uk), Queen Mary University of London, UK.
• Zhiguo Ding (zhiguo.ding@manchester.ac.uk), Khalifa University, UAE.
• Octavia A. Dobre (odobre@mun.ca), Memorial University of Newfoundland, Canada

Special Issue on Intelligent IoT for Sustainable Agriculture and Food Industries
Submission Date: 2025-05-15

Agriculture and food industries are the cornerstones of human society. Unfortunately, modern agriculture and food industries confront many challenges and are far from sustainable. Many waste and carbon emissions are generated, and resource usage is not optimized in the agriculture and food industries. Luckily, the importance of sustainable agriculture and food industries has been well-recognized and has been included in the second goal (together with the other 16 goals) of sustainable development by the United Nations. On the way to sustainable agriculture and food industries, intelligent IoT technologies will play significant roles. It is expected that intelligent IoT will enable innovative solutions to promote waste reduction, carbon reduction, resource usage optimization, improved productivity, etc. Meanwhile, there are still significant technical challenges for intelligent IoTs in sustainable agriculture and food industries, such as the lack of commonly accepted IoT architecture, the security issues of IoT-enabled applications, and the optimal integration of diverse technologies. With these motivations, this special issue seeks innovative and groundbreaking intelligent IoT technologies and pioneering applications for sustainable agriculture and food industries.

Topics include, but are not limited to the following:
 Intelligent IoT architecture for sustainable agriculture and food industries
 Edge computing-enabled IoT for sustainable agriculture and food industries
 Artificial intelligence-enabled IoT for sustainable agriculture and food industries
 Cloud-native IoT in sustainable agriculture and food industries
 Blockchain-enabled IoT for sustainable agriculture and food industries
 Security and trustworthy IoT for sustainable agriculture and food industries
 Biodegradable and eco-friendly IoT for sustainable agriculture and food industries
 Intelligent IoT for carbon reduction in agriculture and food industries
 Intelligent IoT for waste reduction in agriculture and food industries
 Intelligent IoT for supply chain optimization in agriculture and food industries
 Next-generation IoT for sustainable agriculture and food industries
 etc.

Important Dates:
Submission Deadline: May 15, 2025
First Review Due: Jul. 15, 2025
Revision Due: Aug. 15, 2025
Sec. Reviews Due/Notification: Sept.15, 2025
Final Manuscript Due: Sept. 30, 2025
Publication Date: December 2025

Submission Guidelines:
All original manuscripts or revisions to the IEEE IoT Journal must be submitted electronically through IEEE Manuscript Central, https://mc.manuscriptcentral.com/iot. When the authors reach the “Article Type” step in the submission process, they should select “SI: Intelligent IoT for Sustainable Agriculture and Food Industries”. Solicited original submissions must not be currently under consideration for publication in other venues. Author guidelines and submission information can be
found at http://ieee-iotj.org/guidelines-for-authors/.

Guest Editors:
Prof. Yuemin Ding, University of Navarra, Spain, email: yueminding@unav.es (lead guest editor)
Dr. Zhibo Pang, ABB & KTH Royal Institute of Technology, Sweden, email: pang.zhibo@se.abb.com
Dr. Yu Liu, Ericsson Research, Sweden, email: yu.a.liu@ericsson.com
Dr. Kan Yu, La Trobe University, Australia, email: K.Yu@latrobe.edu.au
Prof. Fei Pan, Sichuan Agricultural University, China, Email: fei.pan@sicau.edu.cn

Special Issue on Integrated Transmissions and Computations for Non-Terrestrial Network-assisted Internet of Things
Submission Date: 2025-05-15

Non-Terrestrial Networks (NTNs) have advantages to complement the terrestrial
networks in coverage and flexibility, especially for Internet of Things (IoT) in harsh
environments including desert, marine, and mountainous regions. Future NTNs can
support not only the information transmissions but also real-time data processing with
the improved computation capacity of state-of-the-art satellites and Unmanned Aerial
Vehicles (UAVs). Moreover, future intelligent and diversified IoT applications
demand the optimization of both transmissions and computations. However, the high
dynamics, differentiated communication conditions, and heterogeneous hardware
platforms of NTNs, as well as the diversified service requirements, uneven
distributions, and the limited IoT device batteries, pose significant challenges for
network and performance optimization.

To optimize the provided Integrated Transmissions And Computations (ITAC)
services, extensive research can be conducted from the topics including network
architecture design, protocol design, resource allocation, task offloading, hardware
design, etc. Thus, topics include, but are not limited to, the following:

 Network architecture design for NTNs and IoT integration
 Scalable and adaptive NTN architectures design for harsh environments
 Deployment of satellites and UAVs for seamless ITAC service provision
 Terahertz and FSO communications in NTNs for ITAC services
 Access, handover, routing, and switching protocols for NTN-assisted IoTs
 Hierarchical computing and caching techniques
 Energy issues in NTN-assisted IoTs for ITAC services
 Privacy-preserving techniques for computation and transmission in NTNs
 AI, Generative AI, and Large Language Models (LLMs) for NTN-assisted IoT
 Digital twin/metaverse-based simulation platforms in NTN-assisted IoTs

Guest Editors:

Bomin Mao, Northwestern Polytechnical University, China, maobomin@nwpu.edu.cn
Shuai Han, Harbin Institute of Technology, China, hanshuai@hit.edu.cn
Yuanqiu Luo, Futurewei Technologies, Inc., USA, Yuanqiu.Luo@futurewei.com
Igor Bisio, University of Genoa, Italy, igor.bisio@unige.it
Ekram Hossain, University of Manitoba, Canada, ekram.hossain@umanitoba.ca

Important Dates:

Submission Deadline: May 15th, 2025 
First Reviews Due: August 15th, 2025 
Revision Due: October 15th, 2025 
Final Reviews Due: November 15th, 2025
Final Manuscript Due: November 30th, 2025
Publication Date: January 2026

Submission Guidelines:

The submission information is available at http://ieeeiotj.org/guidelines-for-authors/.
All original manuscripts and revisions must be submitted electronically through IEEE
Manuscript Central, http://mc.manuscriptcentral.com/iot, with the selection “Special
Issue on Integrated Transmissions and Computations for Non-Terrestrial
Network-assisted Internet of Things” for the type.

Special Issue on Challenges and Opportunities of IoT-based Healthcare Industry 5.0
Submission Date: 2025-05-31

Healthcare Industry 5.0 refers to the fifth industrial revolution in the healthcare field, in which the specific needs of patients and healthcare providers can be met. In the past, Industry 4.0 facilitated the implementation of mass customization, but this alone was insufficient. Presently, the healthcare sector is seeking to achieve mass personalization while maintaining a human touch. Therefore, Healthcare Industry 5.0 facilitates the transition from mass customization to mass personalization. It offers a customized product to patients and healthcare professionals based on their individual needs. Cloud computing, blockchain, big data analytics, the IoT, and 6G networks are some of the enabling technologies for Healthcare Industry 5.0.

Amidst the current paradigm shift, a sensor or IoT equipped with artificial intelligence will expeditiously analyze the data. Machines will possess the adaptability to make sophisticated decisions based on the requirements of patients. Within the medical domain, these technologies can accurately measure and monitor many physiological parameters of the human body according to the specific needs of the patient. This aids in monitoring the body's reaction with enhanced efficiency. It facilitates the integration of healthcare services and enables the accurate and efficient digital sharing of information.

Although Industry 5.0 offers several benefits, its implementation in healthcare faces challenges such as the need for a robust infrastructure, concerns over data protection, and ethical considerations. However, the healthcare industry is expected to undergo substantial progress in the near future as a result of the ongoing improvements in Industry 5.0. The proposed SI will cover the broad spectrum of Healthcare Industry 5.0. Topics of interest include (but are not limited to) the following:

• IoT Protocols for Healthcare Industry 5.0
• Disease diagnosis for automated monitoring of health
• IoTs for optimized healthcare
• IoT-based precision medicare
• Embedded healthcare systems
• Big Data Analytics for Healthcare Industry 5.0
• Edge and cloud collaboration for IoT-based Healthcare Industry 5.0
• Enabling B5G/6G in IoT-based Healthcare Industry 5.0
• Privacy and security in IoT-based Healthcare Industry 5.0
• Innovative IoT applications for Healthcare Industry 5.0

Submission Guidelines:
All original manuscripts or revisions to the IEEE IoT Journal must be submitted electronically through IEEE Manuscript Central, http://mc.manuscriptcentral.com/iot. Solicited original submissions must not be currently under consideration for publication in other venues. Author guidelines and submission information can be found at http://ieee-iotj.org/guidelines-for-authors/.

Important dates:
• Submission Deadline: May 31st, 2025
• First Review Due: July 15th, 2025
• Revision Due: August 15th, 2025
• Second Reviews Due/Notification: September 15th, 2025
• Final Manuscript Due: October 15th, 2025
• Publication date: December 2025

Guest editors
Ghulam Muhammad, King Saud University, Saudi Arabia. Email: ghulam@ksu.edu.sa
Victor C.M. Leung, The University of British Columbia, Vancouver, Canada. Email: VLeung@ece.ubc.ca
Cheng-Xiang Wang, Southeast University, Nanjing, China. Email: chxwang@seu.edu.cn
Danda B. Rawat, Howard University, Washington, D.C., United States. Email: db.rawat@ieee.org
Jalel Ben Othman, University of Paris 13, France. Email: jalel.benothman@centralesupelec.f