Abraham O. Fapojuwo’s research while affiliated with University of Calgary and other places

Amidst the technological revolution, the convergence of Industrial Artificial Intelligence of Things (Industrial AIoT) signifies a profound transformation in industrial operations. Nonetheless, persistent concerns revolve around data privacy, security, and connectivity challenges. Drones emerge as pivotal aids for Industrial AIoTs, particularly in areas with limited connectivity. While Federated Learning (FL) and Meta-Learning (ML) address data privacy and adaptability, challenges like data heterogeneity, scarcity, model positioning, unauthorized data tampering, and cyber threats endure. To tackle these issues, this paper presents a Federated Meta-Learning (FML)-based secure data consolidation scheme, utilizing drones for data consolidation, especially in remote, poorly connected regions, followed by secure blockchain storage. It incorporates an Information Gain Ratio (IGR)-based feature selection method to manage data diversity, a two-phase authentication system merging XOR filtering and Chronological Nonce Authentication for entity validation, and secure model consolidation using Hampel filters and performance checks to validate model updates. A real-world proof of concept demonstrates superior performance compared to state-of-the-art literature.

Beyond fifth generation (B5G) is expected to tremendously improve network capabilities by using a higher frequency band compared to 5G, capable of delivering higher network capacity with much lower latency. It is expected that there will be around 30 billion connected objects by 2030, approximately 3.5 times the population then which underscores the pressing need for advanced network capabilities to support diverse applications ranging from smart transportation and energy management to healthcare and public safety. Network slicing enables sharing of network resources by transforming the physical network into logically independent networks, each specifically tailored to meet the requirements of heterogeneous services (e.g., Internet of things applications, gaming services, holographic communication). Each slice is an end-to-end logical network comprising network, compute, and storage resources. Softwarization and virtualization are the main drivers for innovation in B5G, enabling network developers and operators to develop network-aware applications to match customer demands. Smart cities vertical offers unique service characteristics, performance requirements, and technical challenges in B5G network slicing. Therefore, this paper provides a comprehensive survey on B5G network slicing use cases, synergies, practical implementations and applications based on their quality of service parameters for smart cities applications. The paper gives a detailed taxonomy of the B5G network slicing framework requirements, design, dynamic intra-slice and inter-slice resource allocation techniques, management and orchestration, artificial intelligence/machine learning-empowered network slicing designs, implementation testbeds, 3GPP specifications and projects/standards for B5G network slicing. Furthermore, the paper provides a thorough discussion on the technical challenges that can arise when implementing B5G network slicing for smart cities applications and offers potential solutions. Finally, the paper discusses B5G network slicing current and future research directions for smart cities applications.

This Special Issue of the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) is dedicated to disseminating the latest research results and practical applications on the analysis and decision-making of complex cyber-multitudinal-physical systems (CMPSs).

This Special Issue of IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) is dedicated to showcasing the latest developments and achievements in circuits and systems for Industry X.0 applications.

IEEE JETCAS is a famous journal sponsored by the IEEE Circuits and Systems Society. For more information, please follow the link: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5503868 To address the emerging challenges of modern complex systems and to promote the development of both novel classical and new AI-based research paradigms, we seek original papers with the latest research results and practical applications in the study of the analysis and decision-making of complex cyber-multitudinal-physical systems and the application of AI techniques. Topics of interest to this special issue include, but are not limited to: 1 Modeling and analysis of real-world complex cyber-multitudinal-physical systems, both bottom-up and top-down methodologies; 2 Control and decision-making strategies for real-world infrastructures in terms of complex cyber-multitudinal-physical systems; 3 Effects of coupling of heterogeneous multitudinal subsystems on system stability and operational robustness; 4 Novel stability and robustness assessment metrics and methods applicable to complex cyber-multitudinal-physical systems; 5 Applications of artificial intelligence techniques to stability improvement, robustness enhancement, and operational improvement of complex cyber-multitudinal-physical systems; 6 Applications of novel complex network-based analytical/control techniques to complex cyber-multitudinal-physical systems; 7 Novel applications in power grids, transportation systems, economics, social networks, communications networks, and other real-world complex connected systems.

p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p