As sixth-generation (6G) cellular networks emerge, promising unparalleled connectivity and capabilities, yet it amplifies concerns regarding security vulnerabilities. These networks include a broader array of devices and sensors compared to earlier generations, increasing the potential for attackers to exploit weaknesses. Existing security frameworks contribute to safeguarding enterprises against external threats that originate beyond the network perimeter. These frameworks operate under the assumption that all entities inside the defined perimeters are reliable, and their primary objective is to authorize access to resources based on assigned roles and permissions. However, this strategy could be more effective today since attacks might originate from any source, including within the network perimeter. To address this issue, a zero-trust architecture (ZTA) could be a potential solution that assumes neither users nor devices can be inherently trusted, and it consistently evaluates potential risks to decide whether to allowaccess to resources. This article will explore the zero-trust approach and its significance in contemporary network security. We describe the role of authentication and access control in ZTA and present an in-depth discussion of state-of-the-art authentication and access control techniques in different scenarios. This article examines the applicability of the zero-trust concept in 6G networks and analyzes the associated challenges and opportunities. This article also examines case studies demonstrating the practical application of the zero trust paradigm in 6G or comparable networks. It explores the research scope and tries to identify relevant research gaps in this area.

The Industrial Internet of Things (IIoT) has the potential to improve the production and business processes by enabling the extraction of valuable information from industrial processes. The mining industry, however, is rather traditional and somewhat slow to change due to infrastructural limitations in communication, data management, storage, and exchange of information. Most research efforts so far on applying IIoT in the mining industry focus on specific concerns such as ventilation monitoring, accident analysis, fleet and personnel management, tailing dam monitoring, and pre-alarm system while an overall IIoT architecture suitable for the general conditions in the mining industry is still missing. This article analyzes the current state of Information Technology in the mining sector and identifies a major challenge of vertical fragmentation due to the technological variety of various systems and devices offered by different vendors, preventing interoperability, data distribution, and the exchange of information securely between devices and systems. Based on guidelines and practices from the major IIoT standards, a high-level IIoT architecture suitable for the mining industry is then synthesized and presented, addressing the identified challenges and enabling smart mines by automation, interoperable systems, data distribution, and real-time visibility of the mining status. Remote controlling, data processing, and interoperability techniques of the architecture evolve all stages of mining from prospecting to reclamation. The adoption of such IIoT architecture in the mining industry offers safer mine site for workers, predictable mining operations, interoperable environment for both traditional and modern systems and devices, automation to reduce human intervention, and enables underground surveillance by converging operational technology (OT) and information technology (IT). Significant open research challenges and directions are also studied and identified in this paper, such as mobility management, scalability, virtualization at the IIoT edge, and digital twins.

The Industrial Internet of Things (IIoT) promises automation, efficiency, and data-driven decision-making by real-time data collection and analysis. However, traditional IIoT architectures are cloud-centric and, therefore, struggle to handle large volumes of data, edge bandwidth constraints, and data confidentiality. Distributed edge-to-cloud computing emerges as a potential solution, also paving the ground for edge-to-cloud data analytics and distributed Artificial Intelligence (AI) to obtain insights for decision-making and predictive maintenance. Despite the potential, however, there is a lack of comprehensive studies identifying key requirements for distributed edge-to-cloud IIoT and analyzing to what extent emerging IoT platforms meet those requirements. The scope of this article is to survey existing literature to identify key requirements in IIoT from the perspective of distributed edge-to-cloud computing. We provide a comparative analysis of three prominent IoT platforms, namely ThingsBoard, Eclipse Ditto, and Microsoft Azure IoT, and assess how these platforms meet the key IIoT requirements. Finally, we identify open challenges and potential research opportunities based on the insights gained from the analysis of the three IoT platforms, thereby setting the stage for future work.

Decentralization is essential when trust and performance must not depend on a single organization. Distributed Ledger Technologies (DLTs) and Decentralized Hash Tables (DHTs) are examples where the DLT is useful for transactional events, and the DHT is useful for large-scale data storage. The combination of these two technologies can meet many challenges. The blockchain is a DLT with immutable history protected by cryptographic signatures in data blocks. Identification is an essential issue traditionally provided by centralized trust anchors. Self-sovereign identities (SSIs) are proposed decentralized models where users can control and manage their identities with the help of DHT. However, slowness is a challenge among decentralized identification systems because of many connections and requests among participants. In this article, we focus on decentralized identification by DLT and DHT, where users can control their information and store biometrics. We survey some existing alternatives and address the performance challenge by comparing different decentralized identification technologies based on execution time and throughput. We show that the DHT and machine learning model (BioIPFS) performs better than other solutions such as uPort, ShoCard, and BBID.

In the realm of Industrial Internet of Things (IoT) and Industrial Cyber-Physical Systems (ICPS), Digital Twins (DTs) have revolutionized the management of physical entities. However, existing implementations often face constraints due to hardware-centric approaches and limited flexibility. This article introduces a transformative paradigm that harnesses the potential of distributed Digital Twins as proxies, enabling software-centricity and unlocking composability and flexibility for purpose-oriented digital twin development and deployment. The proposed microservices-based architecture, rooted in service-oriented architecture (SOA) and microservices principles, emphasizes reusability, modularity, and scalability. Leveraging the Lean Digital Twin Methodology and packaged business capabilities expedites digital twin creation and deployment, facilitating dynamic responses to evolving industrial demands. This architecture segments the industrial realm into physical and virtual spaces, where core components are responsible for digital twin management, deployment, and secure interactions. By abstracting and virtualizing physical entities into individual digital twins, this approach establishes the groundwork for purpose-oriented composite digital twin creation. Our key contributions involve a comprehensive exposition of the architecture, a practical proof-of-concept (PoC) implementation, and the application of the architecture in a use-case scenario. Additionally, we provide an analysis, including a quantitative evaluation of the proxy aspect and a qualitative comparison with traditional approaches. This assessment emphasizes key properties such as reusability, modularity, abstraction, discoverability, and security, transcending the limitations of contemporary industrial systems and enabling agile, adaptable digital proxies to meet modern industrial demands.

Authentication, authorization, and digital identity management are core features required by secure digital systems. In this, authorization is a key component for regulating the detailed access credentials with respect to required service resources. Authorization, therefore, plays a significant role in the trust management of autonomous devices and services. Due to the heterogeneous nature of cyber-physical systems and the Internet of Things, several authorization techniques using different access control models, accounts, groups, tokens, and delegations have both strengths and weaknesses. Many studies exist in the literature that focus on other main security requirements, such as authentication, identity management, and confidentiality. However, there is a need for a comprehensive review of different authorization techniques in cyber-physical systems and the Internet of Things. A specific target of this paper is authorization in the cyber-physical system and Internet of Things networks with non-constrained devices in an industrial context with mobility, subcontractors, and autonomous machines that are able to carry out advanced tasks on behalf of others. We study the different authorization techniques using our three-dimensional classification, including access control models, subgranting models, and authorization governance. We focus on the state of the art of authorization subgranting, including delegation techniques by access control/authorization server and self-contained authorization using a new concept of power of attorney. Comparisons are performed with respect to several parameters, such as type of communication, method of authorization, control of expiration, and use of techniques such as public key certificate, encryption techniques, and tokens. The results show the differences and similarities of server-based and power of attorney-based authorization subgranting. The most common standards are also analyzed in light of those classifications.