Suayb S. Arslan

The development of the healthcare industry is closely tied to the history of human development. The integration of sensing, communication, computing, and control technologies, along with cloud-based solutions, enables the realization of the Internet of Things concept and forms a series of Internet of Healthcare Things (IoHT) applications. However, providing realtime health services is one of the most important challenges for IoHT systems. To address this issue, a fog computing architecture (FC) is proposed as an additional computing layer to support the cloud computing layer, aiming to reduce service response time, computing costs, and energy consumption. In this study, we conduct a comprehensive evaluation to optimize resource allocation for hospitals under the constraints of scale and patient volume, as well as SLA thresholds. Then, we provide recommendations to optimize investment costs for computing infrastructure supporting real-time health services. Finally, we discuss challenges and open issues.

Cloud-native computing is a software development approach that uses cloud computing to build and run scalable applications by leveraging best practices and technologies in the field such as DevOps, general unified architectures (e.g., based on serverless functions, containers, container orchestrators, sidecar, service meshes, microservices), agile software development and so on. It is also used for application development environments that enable an abstraction layer on top of non-cloud provider services. Cloud-native is expected to increase the efficiency of development, operation, and verification of 5G and beyond network functionalities along with automated service upgrades and deployments, and ultimately reduce vendor lock-in so that applications can be easily migrated to other environments seemlessly. In this paper, we provide a state-of-the-art overview of existing cloud-native approaches for telcos, their key features, the role of open-source and Linux Foundation projects, and all associated standardization efforts. After presenting key findings of the recent research studies and related implications under “Lessons Learned”, we highlight the potential challenges, current issues, and future directions of cloud-native computing for telecommunication networks.

The growing demand for mobile data services has made it necessary to find efficient and cost-effective ways to share networks. Blockchain technology offers a promising solution to the challenges of network sharing, such as interoperability, trust, and accountability. This article provides a comprehensive classification and categorization of blockchain-based network–sharing scenarios, highlighting their advantages and limitations. Seven network sharing scenarios are identified, ranging from centralized network sharing to fully decentralized spectrum sharing. The suitability of some selected blockchain consensus algorithms (namely Proof-of-Work (PoW) with Ethereum, Proof-of-Authority (PoA) with Ethereum, Practical Byzantine Fault Tolerance (PBFT) with Tendermint and Proof-of-Stake (PoS) with Cosmos) is assessed for selected scenarios through extensive evaluations. This article also identifies gaps and opportunities in blockchain–based network sharing solutions and outlines future research directions.

Human intelligence, the most evident and accessible form of reasoning, hosted by biological hardware, has evolved and been refined over thousands of years, positioning itself today to create new artificial forms and preparing to self–design their evolutionary path forward. Beginning with the advent of foundation models, the rate at which human and artificial intelligence interact with each other has exceeded any anticipated quantitative figures. The close engagement led both bits of intelligence to be impacted in various ways, which naturally resulted in complex confluences that warrant close scrutiny. Recent advances, such as DeepSeek, exemplify this interplay: the novel contributions, we argue, draw indirect inspiration from biological principles like modular neural specialization and sparse episodic encoding, addressing computational bottlenecks while aligning with human-inspired scalability. In the sequel, using a novel taxonomy, we shall explore this interplay between human and machine intelligence, focusing on the crucial role humans play in developing ethical, responsible, and robust intelligent systems. We briefly delve into various aspects of implementation inspired by the mechanisms underlying neuroscience and human cognition. In addition, we propose future perspectives, capitalizing on the advantages of symbiotic designs to suggest a human-centered direction for next-generation developments, focusing on the augmentation role of AI. We finalize this evolving document with some thoughts and open questions yet to be addressed by the broader community.

Reconfigurable production lines empowered by an intent-based approach and next-generation wireless networks can be a cornerstone of the transformative Industry 5.0 paradigm. This article presents a novel framework for developing an intent-based reconfigurable production line enriched with blockchain technology. By using blockchain as an enabler for decentralization, the proposed approach aims to integrate security and efficiency into the fabric of industrial automation. Experimental evaluations confirm the effectiveness of the proposed approach (in terms of scalability and latency) and highlight its strengths and potential for improvement. To be more precise, the blockchain systems are 85 to 95 seconds faster than the cloud service for 50 simultaneous transactions and offer around 69% lower latency at maximum utilization with 10 000 events. Important challenges such as integration into the existing infrastructure, security, standardization and energy efficiency are highlighted and recommendations for overcoming these hurdles are also given at the end of the article.

Industrial automation is expected to benefit from the capabilities of Beyond 5G (B5G)/6G, particularly in improving real-time decision making and control in information-driven scenarios, supporting improved efficiency, precision and safety in connected manufacturing environments. While not all aspects of industrial automation are equally affected, certain areas such as real-time monitoring, wireless control and predictive maintenance can benefit significantly from the high reliability, ultra-low latency and scalability of next-generation wireless systems. Motivated by the increasing convergence between operational technology (OT) and advanced communication infrastructures, this paper surveys how B5G/6G can serve as an enabler for data-centric industrial ecosystems. The main contribution lies in analysing evolving industrial architectures, the integration of OT systems and application domains including digital twins, cloud robotics and smart devices. At the end of the paper, we also offer strategic insights into enabling technologies and future challenges, providing a comprehensive overview of how B5G/6G can support the evolution of Industry 5.0.

In the next decade, electric vehicles (EVs) are expected to contribute to reducing climate change and transforming road mobility significantly. However, the security and privacy of EV charging systems present considerable challenges that need to be addressed. This paper introduces a novel approach by integrating blockchain-based self-sovereign identity (SSI) to enhance the security and privacy of EV charging systems. By leveraging decentralized and immutable nature of blockchain, the proposed SSI framework can ensure secure and private data exchanges between EVs, charging stations, and backend systems. This three-way integration addresses the vulnerabilities identified in existing EV charging methods, such as conductive, inductive, and battery swapping, and complies with cybersecurity regulations like UNECE R155. This paper provides a comprehensive analysis, practical case study, and evaluation of the security and privacy enhancements achieved through the proposed SSI framework, offering valuable insights for industry professionals and researchers. We have conducted extensive end-to-end testing to evaluate the performance of our blockchain-based SSI framework in the EV charging ecosystem, focusing on identity verification, credential management and service orchestration. The results show that the system enables fast wallet creation, efficient metadata retrieval and low-latency service deployment, ensuring seamless identity management and service orchestration.

Traditional network sharing and federation models face growing challenges in ensuring secure identity management, interoperability, and trust among multiple network operators. This article presents a secure, decentralized framework that integrates self-sovereign identity (SSI) with blockchain-based consensus mechanisms to address these limitations in multivendor network-sharing environments. The proposed architecture leverages consensus models—such as Proof-of-Work (PoW), Proof-of-Authority (PoA), Practical Byzantine Fault Tolerance (PBFT), and Proof-of-Stake (PoS)—to evaluate performance tradeoffs in terms of resource consumption, consensus latency, and scalability. Experimental results show that the PBFT consensus mechanism provides the highest resource efficiency and minimal federation latency overhead when integrating SSI. In contrast, Cosmos PoS offers a balanced tradeoff between scalability, throughput, and moderate resource utilization, making it well-suited for future federated network deployments that require interoperability and high transaction capacity. In addition, threat models along with security and privacy considerations are addressed to ensure compliance with the General Data Protection Regulation (GDPR) and to support data sovereignty.

The evolution of consumer electronics is increasingly driven by advancements in industrial automation, where generative artificial intelligence (AI) plays a pivotal role, particularly in intent-based systems. This study presents a comprehensive framework for integrating generative AI into intent-based industrial automation, which aims to align high-level business goals with precise operational tasks on the factory floor. The proposed architecture synergistically combines three main components: Business Intents, the Intent Layer and the Factory Floor Shop. This framework leverages foundation models such as the most prominent large language models (LLMs) and advanced communication technologies to translate strategic business intents — such as cost optimization, order management and market adaptation — into actionable tasks to improve operational efficiency, responsiveness and strategic alignment. We also propose a process of fine-tuning LLMs to optimize their interaction flow within the intent-based system, highlighting the augmentation techniques employed to adapt LLMs for industrial contexts. Numerical results demonstrate the effectiveness of the proposed framework and show percentage improvements compared to pre-trained LLM models.

Blockchain network (BCN)-based Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federated Learning (FL), on the other hand, provides a collaborative framework to update Machine Learning (ML) models without relying explicitly on data exchange between the users. This paper investigates identity management and authentication for vehicle users in the context of FL. We propose a novel approach based on blockchain-based SSI, which focuses on maintaining the authenticity and integrity of vehicle users’ identities and data exchanged between the users and the aggregation server during the execution of the FL iterations. A primary objective of this paper is to achieve shorter durations for credential operations in an FL setting as the system size scales out. Integrating BCN-based SSI into the FL framework addresses several critical FL challenges, ensuring enhanced system security and operational integrity. This synergy of BCN-based SSI with federated learning enables robust identity verification providing a solution to fundamental trustworthiness issues in FL without sacrificing the benefits of decentralized data control, improving both the performance and reliability of the FL system. Experimental results suggest that the proposed FL-based system, together with credential management on a blockchain platform, has the potential to significantly improve data integrity and ensure the authentication of users. More specifically, the results of the FL system demonstrate that it takes longer (on the order of a hundred seconds) as the number of rounds and clients increase, while the implemented Decentralized Identifier (DID) system relying on BCN-based SSI has dramatically shorter dedicated time for completing credential operations.