Distributed ledger technology such as blockchain is considered essential for supporting large numbers of micro-transactions in the Machine Economy, which is envisioned to involve billions of connected heterogeneous and decentralized cyber-physical systems. This stresses the need for performance and scalability of distributed ledger technologies. Addressing this, sharding techniques that divide the blockchain network into multiple committees are a common approach to improve scalability. However, with current sharding approaches, costly cross-shard verification is needed to prevent double-spending. This paper proposes a novel and more scalable distributed ledger method named ScaleGraph that implements dynamic sharding by using routing and logical proximity concepts from distributed hash tables. ScaleGraph addresses cybersecurity in terms of integrity and availability to support frequent micro-transactions between autonomous devices. Benefits of ScaleGraph include a total storage space complexity of O(𝑡), where t is the global number of transactions (assuming a constant replication degree). This space is sharded over N nodes so that each node needs O(𝑡/N) storage in expectation, which provides a high level of concurrency and data localization as compared to other delegated consensus proposals. ScaleGraph allows for a dynamic grouping of validators that are selected based on a distance metric. We analyze the consensus requirements in such a dynamic setting and show that a synchronous consensus protocol allows shards to be smaller than an asynchronous one, and likely yields better performance. Moreover, we provide an experimental analysis of security aspects regarding the required size of the consensus groups with ScaleGraph. Our analysis shows that dynamic sharding based on proximity concepts brings attractive scalability properties in general, especially when the fraction of corrupt nodes is small.

Edge computing is emerging as a transformative approach in Industrial Internet of Things (IIoT) applications, particularly within remote industrial environments where cloud connectivity is unreliable. This study investigates a fully edge-based data analytics solution using an open source platform to enhance real-time monitoring, alarm systems, and sensor health analysis without reliance on cloud infrastructure. The experimental setup utilizes Flasheye's LiDAR perception system (which monitors areas in underground mining) integrated with MQTT to process and visualize data on ThingsBoard, focusing on critical parameters such as throughput, latency, and failure rate. Evaluation results demonstrate that this edge-based approach supports low-latency performance and reliable alarm management, underscoring the practicality of localized analytics for remote industrial operations. By implementing and evaluating an open source edge computing solution in remote industrial environments, the study contributes a fully edge-based data analytics solution for IIoT deployments, enhancing operational efficiency and safety in connectivity-limited environments.

Many optimization problems cannot be solved to optimality in practice due to time constraints. Instead, the goal is to find sufficiently good solutions in reasonable time. This creates a trade-off between running time and solution quality. Some problems require iterative and interactive solving due to uncertainties in the definition of the problem instance or objective function. These present an additional challenge because the desired trade-off can vary between iterations. We focus on simple assembly line balancing, an NP-hard combinatorial optimization problem, as a use case. Absolute termination criteria, such as a time limit or iteration/generation limit, are often inadequate due to their rigidity. We therefore evaluate the effect of more flexible termination criteria (e.g. unimproved time limit and score limit) on the performance of a solver, focusing on the ability to reduce unproductive time and dynamically achieve an adjustable time-quality tradeoff. Our results show that it is possible to simultaneously reduce running time and increase solution quality on average. While quality improvements are modest, time savings can be substantial. In some cases, running time can be reduced by as much as 55 % without sacrificing solution quality.

Access control in modern digital ecosystems is challenging due to dynamic resources and diverse stakeholders. Traditional mechanisms struggle to adapt, causing inefficiencies and inequities. We propose a novel algorithm that automates access control policy negotiation via objective optimization and utility-based methods. It enables stakeholders to jointly select policies aligned with their preferences, provided a suitable policy exists. Suggested criteria guide the evaluation of predefined policies, and a mathematical formulation quantifies stakeholder preferences with utility functions, using optimization to achieve consensus. The algorithm’s multilinear scalability is demonstrated through time and space complexity analysis. An evaluation tool supports practical testing, and the approach enhances efficiency and trust by ensuring equitable data access within digital ecosystems.

Authentication and access control for Cyber-Physical Systems (CPSs) are pivotal for protecting systems and their users from problems related to harmful actions and the malicious use of retrieved data. In some situations, making access decisions requires using user information, thereby challenging their privacy. Attribute-based access control (ABAC) supports dynamic and context-aware access decisions that are attractive in cyber-physical system environments. However, privacy preservation for access decisions is an open issue for authorization and is not supported by existing ABAC models. For example, if access decisions need to be made based on private attribute values such as health data, the corresponding access control policies need to be revealed. This paper reviews the ABAC, homomorphic encryption (HE), and zero-knowledge proof (ZKP) approaches, confirming the gap in privacy preservation in ABAC. Based on this observation, we further present the application of a new ZKP-based protocol in which ABAC allows for the privacy-preserving evaluation of attributes. This protocol is implemented and evaluated in terms of its performance and security. The evaluation demonstrates that there is a possibility for privacy-preserving ABAC, which may benefit the use of CPS, e.g., in underground and open-pit mines.

The efficiency of an assembly line depends on how the work is distributed along the line. This is known as the Assembly Line Balancing Problem, an NP-hard optimization problem. Automatic solvers for this problem have been studied for decades but have not been widely adopted in the industry, resulting in a theory-practice gap. The typical automation approach assumes that all constraints and objectives are known and can be statically defined ahead of time such that solvers with a precisely defined objective function can take a fully specified problem instance as input and produce a (near) optimal solution as output. In some industries, meeting these assumptions is particularly challenging because of properties such as mixed-model production with high model variance, multi-manned stations, large task graphs, etc. This paper explains why, in certain industries, such as automotive end assembly, complete automation is likely infeasible in practice due to challenges in modeling the problem, collecting data, and specifying the objective function. Manual intervention by an engineer as a decision-maker is therefore unavoidable. We argue that maximizing automation, by helping the decision-maker be as effective as possible, requires a decision support system (DSS) that supports an interactive and iterative workflow, thereby enabling assisted planning. Furthermore, we identify solver features that become relevant in the DSS context, thus making the case that focusing on standalone solvers, and treating the integration into a DSS as an implementation detail, is not a viable option. We conclude that decision support systems play a central role in closing the theory-practice gap.

A mixed-model assembly line produces a variety of product models on a single assembly line. Since models differ e.g. in work content, some stations may be unable to finish their tasks in time if multiple work-intensive models are placed consecutively on the assembly line, resulting in costly overload situations. This leads to the product sequencing problem, an NP-hard optimization problem that consists in arranging orders into a suitable sequence. While sequences are typically decided and frozen some time before production to enable logistics planning, late changes are sometimes unavoidable, which has received little attention. This paper presents the early stages of developing a decision support system for handling such late changes.

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 allow access 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.

Conventional approaches to agreeing on transactions in monetary systems rely on trusted central authorities. Blockchain defies this model by achieving consensus through a community of peers in a decentralized network for validating transactions and maintaining an ordered record of transactions. The application domains for blockchain have expanded from cryptocurrencies to potentially any industrial system. However, there is still a lot of confusion regarding the advantage of using the blockchain instead of alternative technologies. The number of successfully deployed blockchain-based systems is still relatively small due to the lack of clarity regarding blockchain governance and the challenge of assessing the tradeoffs of blockchain-based solutions. As there are different types of blockchains, such as permissionless and permissioned, it often becomes hard to choose a suitable solution for a specific use case scenario. In this paper, we address these challenges by analyzing blockchain governance and related tradeoffs, including energy consumption, consensus, performance, and security. We contribute with a taxonomy guideline to provide insights on these tradeoffs and to facilitate effective identification of the suitable governance structure for blockchain-based solutions in various scenarios. The proposed taxonomy discusses different features of blockchain that includes energy consumption, consensus methods, performance, and security aspects to suggest the governance model (permissionless/permissioned/consortium) for a blockchain-based solution. Furthermore, this paper provides a tradeoff analysis of different consensus methods, such as PoW, PoS, PBFT, and PoA, considering important properties.