Industry 5.0 places human-centricity, sustainability, and resilience at the core of next-generation enterprise systems. Blockchain has been increasingly recognized as a potential enabler of this transition due to its decentralized architecture, transparency, and trust-enhancing capabilities. Yet, existing literature on enterprise systems remains fragmented in positioning blockchain’s role in enabling these foundational pillars. To address this knowledge gap, we screened and examined 11 peer-reviewed journal articles published between 2019 and 2024 using a scoping review methodology. We adopted the three pillars as our analytical lens. Results showed that blockchain, to some extent, can enable enterprise systems transitions to fulfill the core values of Industry 5.0. Two exemplar success stories, one in maritime logistics and one in healthcare, demonstrate feasibility but underscore unresolved scalability limits. Also, the results reveal issues needing further research and development, such as energy–latency trade-offs that hinder green transitions, underdeveloped governance models for human-in-the-loop decision-making, and fragile cross-chain interoperability that threatens operational resilience. Future research on the adoption of blockchain in enterprise systems must consider energy-efficient consensus protocols, standardized blockchain-IoT interfaces, and ethically grounded governance frameworks. Also, future research should include the development of ethical frameworks to ensure transparency, fairness, and explainability. Drawing on these insights, we outline a research agenda to move the discourse from conceptual promise to industrial practice.

The rapid proliferation of electric vehicles (EVs) and the corresponding expansion of charging systems, driven by global regulatory mandates, present significant challenges to the electric grid. These systems also generate massive volumes of heterogeneous, distributed data, necessitating advanced edge-cloud computing solutions. Deploying Distributed Edge Intelligence (DEI) offers a transformative approach by contextualizing raw data, minimizing dependency on centralized resources, enhancing task performance through experience-based learning, predicting outcomes under uncertainties, optimizing data routing, and enabling self-organization of networked devices. Traditional centralized machine learning approaches are often unsuitable due to high communication costs, low reliability, legal constraints, and scalability limitations. In this context, Federated Learning (FL) emerges as a promising solution, enabling distributed machine learning models to be trained locally on decentralized data while preserving privacy and reducing communication overhead. This paper provides a comprehensive analysis of the key challenges associated with EV charging systems, including scalability, security, and privacy concerns. It also explores the integration of blockchain technology with FL at distributed edges to enhance system efficiency, reliability, and privacy. By addressing these challenges, the study outlines a framework for sustainable EV adoption and efficient charging infrastructure deployment in the context of evolving technological and regulatory landscapes.

Extended reality (XR) enables both new opportunities but also introduces new barriers for inclusion in society. Furthermore, XR is less researched than web, desktop and mobile applications. This position paper presents the concept of an XR platform for inclusion, with the purpose to make people on the autism spectrum and with other disabilities, more independent of help from others in everyday life situations. Based on previous research, our position is that, through current and future XR technologies combined with civic and artificial intelligence, it is possible to create individually personalised support for this purpose, grounded in practice to ensure validation.

A common challenge for people on the autism spectrum with intellectual disability, is indoor navigation and related daily activities, as found in previous research. In this paper we report on co-design of assistive augmented reality applications, where the goal is to help people on the autism spectrum to gain more independence in their daily lives. This study is based on initially two full-day workshops with staff only, followed by ten individual workshops with the end-users and their staff at day centers, using a mix of methods and prototypes. The results show a clear potential of augmented reality as assistive technology for indoor navigation, depending on individual capability and/or complexity of environments, as well as for other activities. We also found that new barriers may arise, which are discussed for future research.

The rapid advancement in sensors and communications has led to the expansion of the Internet of Things (IoT) services, where many devices need access to the transport network using fixed or wireless access technologies and mobile Radio Access Networks (RAN). However, supporting IoT in RAN is challenging as IoT services may produce many short and variable sessions, impacting the performance of mobile users sharing the same RAN. To address this issue, network slicing is a promising solution to support heterogeneous service segments sharing the same RAN, which is a crucial requirement of the upcoming fifth-generation (5G) mobile network. This paper proposes a two-level network slicing mechanism for enhanced mobile broadband (eMBB) and Ultra-Reliable and Low Latency communications (URLLC) in order to provide end-to-end slicing at the core and edge of the network with the aim of reducing latency for IoT services and mobile users sharing the same core and RAN using the O-RAN architecture. The problem is modeled at both levels as a Markov decision process (MDP) and solved using hierarchical reinforcement learning. At a high level, an SDN controller using an agent that has been trained by a Double Deep Q-network (DDQN) allocates radio resources to gNodeBs (next-generation NodeB, a 5G base station) based on the requirements of eMBB and URLLC services. At a low level, each gNodeB using an agent that has been trained by a DDQN allocates its pre-allocated resources to its end-users. The proposed approach has been demonstrated and validated through a real testbed. Notably, it surpasses the prevalent approaches in terms of end-to-end latency.

The Metaverse offers a simulated environment that could transform healthcare by providing immersive learning experiences through Internet applications and social forms that integrate a network of virtual reality environments. The Metaverse is expected to contribute to a new way of socializing, where users can enter a verse as avatars. The concept allows avatars to switch between verses seamlessly. Virtual Reality (VR) in healthcare has shown promise for social-skill training, especially for individuals with Autism Spectrum Disorder (ASD), and social challenge training for patients with Post-Traumatic Stress Disorder (PTSD) requiring adaptable support. The problem lies in the limited adaptability and functionality of existing Metaverse implementations for individuals with ASD and PTSD. While studies have explored various implementation ideas, such as VR platforms for training social skills, social challenge, and context-aware Augmented Reality (AR) systems for daily activities, many lack adaptability of user input and output. A proposed solution involves a context-aware system using AI, Large Language Models (LLMs), and generative agents to support independent living for individuals with ASD and a tool to enhance emotional learning with PTSD.

Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds.

Attribute-based encryption (ABE) has been used to provide data confidentiality and fine-grained access control in the Internet of Vehicles (IoV). However, the attributes of vehicles in IoV might change frequently due to the movements of vehicles. Thus, the invalid attributes need to be revoked in time and efficiently to ensure the security of the system. In this paper, we propose a data-sharing scheme based on ABE for IoV. By using a binary tree and attribute version keys, flexible revocation can be achieved for IoV. Moreover, the ciphertext can be stored on clouds, and the distribution and revocation of attribute keys can be realized by distributed attribute authorities. We performed the security analysis and proved the security of the proposed scheme. The results showed that the proposed scheme has lower average computing overhead in terms of attribute revocations compared with other schemes based on ABE, and can satisfy the performance requirement of data sharing for IoV.

This article proposes a new Artificial Intelligent (AI) and Machine Learning (ML) based framework for 6G-enabled Intelligent edge computing. The framework will be equipped with multiple cognitive controllers to harmoniously control various aspects in distributed intelligence toward edge nodes collaboration. Autonomic cognitive controller for edge computing is a popular computing paradigm where the distributed metadata processing and edge intelligence are performed at edge node in 5G/6G network for management, connectivity and interoperability. Some of studies focused on edge management improvement such as reduce the response time and bandwidth costs. However, the previous approaches are inadequate to support autonomously management for large-scale deployment for connectivity for dynamic and reliable communication. We propose a cognitive controller for edge autonomy and collaboration application development. Finally, we discuss challenges and open issues toward cognitive controller and distributed edge intelligence.

COVID-19 is a viral infectious disease that has created a global pandemic, resulting in millions of deaths and disrupting the world order. Different machine learning and deep learning approaches were considered to detect it utilizing different medical data. Thermal imaging is a promising option for detecting COVID-19 as it is low-cost, non-invasive, and can be maintained remotely. This work explores the COVID-19 detection issue using the thermal image and associated tabular medical data obtained from a publicly available dataset. We incorporate a multi-modal machine learning approach where we investigate the different combinations of medical and data type modalities to get an improved result. We use different machine learning and deep learning methods, namely random forests, Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Overall multi-modal results outperform any single modalities, and it is observed that the thermal image is a crucial factor in achieving it. XGBoost provided the best result with the area under the receiver operating characteristic curve (AUROC) score of 0.91 and the area under the precision-recall curve (AUPRC) score of 0.81. We also report the average of leave-one-positive-instance-out cross- validation evaluation scores. This average score is consistent with the test evaluation score for random forests and XGBoost methods. Our results suggest that utilizing thermal image with associated tabular medical data could be a viable option to detect COVID-19, and it should be explored further to create and test a real-time, secure, private, and remote COVID-19 detection application in the future.