In multi-stakeholder data ecosystems, digital resources (e.g. shared datasets) are often co-owned by independent organizations, making access governance a collaborative challenge. Each stakeholder brings its own security and business constraints, so agreeing on concrete access rules across organizational boundaries is hard. For example, in an industrial IoT supply chain, machine data can be valuable to the factory operator, the equipment supplier, and a maintenance provider, each must consent to who can access what. Manual agreement is slow and contentious, motivating an automated negotiation mechanism to help stakeholders efficiently converge on a shared set of acceptable rules. 

Consensus is only half the problem, once access rules are agreed upon, they must be enforced reliably in a distributed system with no single authority to push updates. Nodes go offline, networks partition, and rule updates arrive out of order. Without careful design, different parts of the system will enforce different decisions. Ensuring that all parties can consistently uphold the agreed rules under stated assumptions requires robust, fault-tolerant mechanisms. This licentiate thesis tackles both agreement and enforcement, providing methods to reach common ground on access rules and to apply them securely despite distributed-systems challenges.

The thesis makes three contributions. First, it introduces a utility-based negotiation method that lets multiple stakeholders collaboratively arrive at a common access-rule set by quantifying preferences and using optimization to automate consensus, supporting more structured and reproducible agreement compared to informal negotiation. Second, it develops a formal verification toolchain for cloud-native infrastructures (shown on Kubernetes) to prevent misconfiguration and privilege escalation; RBAC and admission rules are translated into logical constraints, an SMT solver checks for unsafe conditions, and only verified rules are deployed; an integrated deny-overrides enforcement path then applies them consistently at runtime. Third, it outlines EQuack (to be detailed in forthcoming work), an offline-capable access control model for distributed ecosystems where continuous connectivity cannot be assumed. It ensures that even if nodes diverge while offline, they eventually converge to the same authorized state via deterministic deny-wins replay over update logs and tamper-evident audit trails, without relying on blockchains or other heavy consensus mechanisms.

Taken together, the results suggest a path toward more secure collaboration by supporting structured agreement over access rules and providing enforcement mechanisms that can remain consistent in distributed settings, within the limits of the evaluated scenarios and stated assumptions.

Industrial production systems depend on key assets to maintain process continuity and support essential operational tasks. To meet these requirements, assets must consistently deliver their intended functions. Failures can reduce system performance and result in downtime, production loss, and increased maintenance costs. Furthermore, severe failures may compromise asset integrity and introduce safety risks. These potential consequences highlight the importance of systematic health management to promote reliable and safe operation throughout the asset’s life cycle.

Maintenance plays a vital role in ensuring asset functionality. However, relying solely on corrective or schedule-based maintenance approaches often proves insufficient. When maintenance occurs too late, early signs of degradation may remain undetected and progress into more severe failures. In contrast, performing maintenance too early leads to unnecessary work and higher costs. Prognostics and Health Management (PHM), which encompasses state detection, diagnostics, and prognostics, and maintenance decision support, enhances the ability to understand the asset condition and intervene proactively.

The work described in this thesis aims at enabling PHM by establishing essential steps: system-level description of the asset and its functions, failure analysis, and detecting deviations in critical components. Assets are decomposed into subsystems and components to clarify their roles and interactions. Further, the thesis examines how asset functions can be impaired and identifies the components whose degradation is most critical to functional performance. These insights guide the selection of components that require condition monitoring. Methods are then developed for deviation detection from expected performance, forming the basis for state detection within a PHM framework. Additionally, a review of data-driven PHM approaches for mining processing machines highlights gaps in system-level health management and the use of different data modalities, further motivating the structured methods developed in this thesis.

The contributions are demonstrated through two case studies in construction and mining, respectively. In the construction case study, 3D point-cloud data are used to identify missing or deviated scaffolding elements that compromise structural stability. In mining, image-based analysis is applied to a Rotary Vacuum Drum Filter (RVDF) to detect wire failures that can damage the filter cloth and reduce asset availability. Early fault detection supports structural integrity, reduces downtime and lowers maintenance costs. By combining asset structure analysis, failure analysis to identify critical components, and methods for deviation detection, this work delivers essential building blocks for the enablement of PHM. Future work will explore the development of diagnostics, prognostics models and multimodal data integration to further support maintenance decision-making and to strengthen health management capabilities.

Industrial systems are moving towards Industry 5.0, where digital technologies are expected to enhance rather than replace human capabilities. This puts human well-being, resilience, and sustainability at the centre of industrial processes. In sectors such as railways, construction, and mining, as well as other similar industrial sectors that rely on complex technical systems, achieving this vision in maintenance requires a strong Human-System Interaction (HSI) foundation. Despite the increasing integration of immersive and intelligent technologies in maintenance, many solutions remain system-centred and treat humans as peripheral users rather than as an integral part of the system. As a result, digital maintenance support is often insufficiently aligned with real maintenance workflows, operational constraints, and decision-making conditions. Existing research and industrial solutions frequently address HSI as a secondary usability concern, rather than as a primary design driver grounded in how maintenance activities are actually performed and supported in practice.The purpose of this research is to contribute to the development of a human-centric maintenance in the context of Industry 5.0. This research is framed through the concept of supportability, focusing on how maintenance support is implemented and delivered during operation. The emphasis is on maintenance as performed in practice, where task execution, interaction quality in terms of usability and cognitive support, and HSI are critical.The research is based on a combination of a structured literature review, applied design studies, and empirical investigations. A systematic analysis of immersive technologies in maintenance results in a taxonomy of HSI challenges, highlighting the interconnected nature of usability, cognitive workload, trust, and contextual alignment. Building on these findings, human-centric maintenance support solutions are designed and studied in industrial contexts, including railway inspection, to examine how task-aligned and contextualised maintenance information and interaction influence usability-related interaction aspects and cognitive workload. In addition, empirical investigations of decision-support powered by intelligent technologies examine how interpretability and trust influence user experience (UX) and mental effort.The results provide: (i) a structured understanding of HSI challenges associated with immersive and intelligent technologies in maintenance; (ii) a human-centric maintenance support framework integrating maintenance information, decision-support, and interaction during maintenance activities; and (iii) design-oriented insights into the implications of human-centric maintenance for usability, cognitive workload, and maintenance task execution. Together, these contributions support the alignment of immersive and intelligent maintenance technologies with human work, contributing to the realisation of human-centric maintenance systems in line with Industry 5.0.

Deep underground hardrock mines face escalating rockburst risk as depth and stress increase. This thesis develops and demonstrates an integrated framework for stress management and rockburst mitigation that combines destress blasting and destress drilling with energy‐based indices, advanced monitoring, and geostatistics, tailored to Swedish deep mining conditions. Four objectives structure the work: (i) derive a design framework for destress blasting from six decades of international and Swedish practice; (ii) construct a quantitative evaluation methodology that integrates the strain energy storage coefficient (F), brittle shear ratio (BSR) and burst potential index (BPI) with fracture and seismic observations; (iii) execute and analyse a controlled field trial of destress drilling at Zinkgruvan mine; and (iv) develop a geostatistical concept (semi-variograms and kriging) to predict destress efficiency at unsampled locations with quantified uncertainty. 

The methodology adopted for the thesis study integrates structured literature and case-history analysis, numerical and energy-based reasoning, and in situ experimentation with high-resolution 3D laser scanning and cloud-to-cloud (C2C) analysis. The destress blasting component organises key rockmass, stress, and explosive parameters into a conceptual decision framework and design guidance; the evaluation framework specifies how F, BSR and BPI are computed and interpreted alongside monitored fracture and seismic responses; the Zinkgruvan mine practical field trial isolates the mechanical effect of uncharged inclined boreholes by keeping production-blast variables constant and quantifying geometric outcomes through C2C and volume-added metrics; and the geostatistical study shows how semi-variogram modelling and ordinary kriging can map performance indicators and their uncertainty to support risk-aware planning. 

Across case histories and supporting analyses, destress blasting is shown to be effective but highly localised and transient: stress relief typically extends only a few metres from the blast, and benefits decay rapidly as faces advance, necessitating continuous inclusion of destress features in each round within burst-prone zones. Mechanistic interpretation links reductions in boundary tangential stress and strain energy density to blast-induced fracture networks whose extent depends on rockmass brittleness and charging/timing choices; highly brittle rocks are both more burst-prone and more responsive when patterns and charge intensities are matched to site conditions. 

The practical Zinkgruvan mine field trial (depth of 1285 m) provides quantitative evidence that destress drilling stabilises development drifts. Rounds with 46 mm, 4 m destress drilling holes inclined at 20° (roof and shoulders) exhibited up to 2.5 m3 less scaled ‘volume added’ per metre of advance and a 20 – 30 % reduction in C2C profile standard deviation relative to non-destressed rounds, indicating lower overbreak and improved excavation profile control. It was observed that the first two rounds after a destressed round also performed comparably well, evidencing a short-range residual benefit that dissipates by the third non-destressed round, an operationally important finding for sequencing and cost-risk optimisation. 

The thesis study advances practice by: (i) organising destress blasting design considerations into a transferable, Swedish-context-aware framework; (ii) unifying energy indices (F, BSR, BPI) with fracture/deformation and seismic monitoring for quantitative evaluation at excavation scale; (iii) providing the first high-fidelity, field-validated C2C/volume-based assessment of destress drilling in a deep, burst-prone European mine; and (iv) introducing a geostatistical prediction concept that generates mine-wide efficiency maps with confidence bounds to reduce hazardous measurement campaigns and guide targeted data acquisition. 

The main conclusions and recommendations are that destress measures must be engineered and applied continuously in high-risk zones; design should be matched to rockmass brittleness and in situ stress; evaluation should jointly track energy indices, deformation/fracture, and seismicity; soft-scaling (where appropriate) practices should be integrated to minimise added volume; and geostatistical mapping and digital tools (3D scanning/C2C, IIoT) should underpin adaptive, feedback-driven planning.

The Industrial Internet of Things (IIoT) connects vast networks of sensors, machines, and control systems to enable real-time data collection, analysis, and decision-making in industrial settings. This connectivity drives improved operational efficiency, reduced downtime, and enhanced productivity in sectors such as manufacturing, mining, and energy. Traditional architectures, however, rely heavily on cloud computing, which struggles with latency requirements and bandwidth constraints. Distributed edge-to-cloud computing offers a promising alternative by bringing computational resources closer to data sources, thereby enabling faster responses while reducing network demands.

Remote industrial environments present unique difficulties. Network connectivity in underground mines, offshore platforms, and isolated facilities is often intermittent or unavailable. Cloud-centric systems fail under such conditions, leaving critical monitoring gaps that threaten safety and operational continuity. Edge computing addresses these challenges by processing data locally, but questions remain about how existing IoT platforms support key IIoT requirements that edge-to-cloud architectures must satisfy and how systems can maintain reliable operation during connectivity disruptions.

This thesis investigates distributed edge-to-cloud computing for IIoT applications, with particular attention to remote and connectivity-constrained environments. It presents three contributions. First, a comprehensive survey identifies key IIoT requirements from the distributed edge-to-cloud computing perspective and provides a comparative analysis of three prominent IoT platforms. It reveals how each platform addresses the identified key IIoT requirements. Based on this analysis, open challenges and research opportunities are identified for advancing edge-to-cloud systems in industrial contexts.

Second, an experimental study implements and evaluates a fully edge-based data analytics solution for remote industrial environments using an open source platform. The solution combines real-time monitoring, alarm management, and sensor health analysis without dependence on cloud connectivity. Experiments conducted in an underground mining setting with LiDAR perception systems demonstrate low-latency performance and reliable operation under realistic conditions. Performance evaluation in terms of throughput, latency, and failure rate provides practical insights for future deployments in similar settings.

Third, a distributed edge-to-cloud AI architecture is proposed for maintaining prediction capabilities during intermittent connectivity. The architecture deploys machine learning models at both edge and cloud tiers, with a store-and-forward messaging layer that ensures zero data loss during network outages. A case study on methane hazard prediction in underground coal mining validates the approach using over nine million sensor readings from an operational mine. Experiments across three network scenarios demonstrate continuous edge prediction, stable inference latency, and complete data preservation regardless of connectivity conditions.

Together, these contributions advance both understanding and practical application of distributed edge-to-cloud computing in industrial settings. The findings demonstrate that edge-based solutions can operate independently when cloud connectivity fails, that open source platforms provide viable foundations for industrial deployments, and that distributed edge-to-cloud AI architectures can deliver resilient monitoring for safety-critical applications.

Powder metallurgy (PM) offers numerous advantages as a manufacturing technique. Minimal raw material waste and near net shapes strengthens PM as a competitive industrial production method. There is, however, a need to enhance the properties of PM steels as they have inherent porosity of up to 10% compared to wrought steels. There are two ways to increase PM performance; increase the density by sintering or altering the alloying elements for tailoring the microstructure. These two ways can also be combined.  There are several ways to introduce alloying elements, premixing with different powders, prealloying, diffusion bonding and admixing. 

Hardenability is a key property that determines utilization of PM steels in high performance applications. This property is mainly influenced by the alloying elements in PM steel. Better hardenability can be obtained by using alloying elements such as Cr, Si, B and Mn. Moreover, these elements have a lower carbon footprint compared to Ni and Cu which are the traditional PM alloying elements. One way to introduce these oxygen-sensitive elements while overcoming potential drawbacks of sintering is the master alloy (MA) method. The concept of master alloy has been known for decades but is still not widely implemented in PM industry due to requirements for conditions beyond the conventional ones. 

This work focuses on development and application of master alloys to improve the properties of PM steels. Design and optimization of MA comprising Cr, Mn, Si and B have been performed with the help of thermodynamic simulations using low solidus temperature as the main criteria. This is to enable liquid phase sintering. Different atomisation techniques, namely water, gas and gas-water atomisation, were evaluated to establish their effects on the optimised MAs. Additionally, influence of particle size fractions of MA was carried out using fine and course MA powders. To understand the role of sintering parameters on the MA route, different sintering temperatures were used while evaluating the resultant microstructure and final properties.

The results show that adding MA into base powders significantly improved the steel’s hardenability. Continuous cooling transformations showed increase in martensite formation at lower temperatures due to elements from the MA especially with B. The same result was obtained after sintering experiments where bainitic and martensitic transformations were evident in the microstructure. Better final mechanical properties after sintering were obtained due to martensitic structure. This was reflected in higher tensile strength and apparent hardness with MA. Higher sintering temperatures facilitated homogenisation of alloying elements, thus leading to better properties. Fine size fraction MA powder speeded up homogenisation process and left smaller pores after sintering. In as much as gas atomisation gives better control of oxygen in the MA, water atomisation is a more economical and robust process. Overall, addition of MA yields similar or better results than Ni hence MA is potentially a sustainable viable replacement in PM steels.