• Collaborative Digital Twins Ecosystem • NFT base asset governance platform • Multi governance architecture Standalone Digital Twins (DTs) have demonstrated significant value in an increasingly digitalised world. However, collaboration among independent DTs and integration with supporting digital systems are required to maximise their full potential. Establishing a Collaborative Digital Twin Ecosystem (CDTE), incorporating systems such as enterprise resource planning and legacy systems, is essential for informed decision-making; yet, this remains challenging due to heterogeneity in implementations, domains, and standards. This study first classifies various DT types and examines their characteristics, and then compares leading DT reference architectures and security frameworks. Building on these insights, the previously proposed Collaborative Digital Twin Architecture (CDTA) is extended to incorporate NFT-based asset governance and dynamic policy enforcement via smart contracts. The enhanced CDTA is evaluated both qualitatively and quantitatively, considering its features and the value it provides to the CDTE. While policy enforcement introduces some computational overhead, it substantially improves trust, governance, and system-level security. Application-specific optimisation strategies, such as trust-based fast tracking, are proposed to mitigate these overheads. Compatibility with major reference architectures—including IIC, DTC, ISO, and RAMI 4.0—is validated, and integration with the IIC DT Security Maturity Model confirms the CDTA’s ability to support secure and scalable CDTEs across diverse domains, including smart cities, manufacturing, and critical infrastructure.

In collaborative Internet of Things (IoT) ecosystems, ensuring the trustworthiness of participating entities is critical to maintaining system integrity and security. This work presents a reputation model leveraging the outputs of an IoT Trust Analyser (TA). The TA evaluates IoT systems based on five key trust dimensions: security, reliability, resilience, uncertainty & dependability, and goal analysis. The reputation model incorporates behavioural classifications and a trust score moving average to dynamically assess and track system reputation over time. It is designed to detect and mitigate reputation-based attacks, including interaction based attacks like bad-mouthing and ballot-stuffing. Simulations based individual and group reputation attack scenarios demonstrate the effectiveness of the model by detecting attackers while maintaining an accurate classification of behaviour consistently exceeding 80%. To further improve robustness, the system supports configurable trust thresholds, peer-based verification, and potential integration with AI/ML techniques for anomaly detection. Overall, the proposed reputation model enhances trust management in distributed IoT environments, promoting security, reliability, and cooperation among devices.

Digital Twins (DTs) are increasingly deployed in collaborative ecosystems, enabling adaptive monitoring, simulation, and decision-making across interconnected systems. Similar to Internet of Things (IoT) systems, DTs rely on real-time data collection, analysis, and feedback loops to represent and manage physical entities. Both technologies generate large volumes of data and leverage AI/ML techniques to optimise operations, making them complementary in building intelligent, adaptive ecosystems. However, the heterogeneous and dynamic nature of these ecosystems introduces challenges in assessing trustworthiness and ensuring reliable collaboration. This paper presents a Trust Analyser for behavioural categorisation of DTs, leveraging key Trust Evaluation Categories including safety, privacy, security, reliability, resilience, uncertainty & dependability, and ecosystem goal alignment. A DT simulator is developed to generate normal, unpredictable, and malicious DT behaviours, facilitating controlled experiments in a scalable DT ecosystem. Experimental results demonstrate that the TA achieves high accuracy, above 85% in detecting DT behavioural types, maintaining robust performance even as the ecosystem scales, with minor reductions attributable to network-induced delays and overlapping behaviour patterns. The proposed approach highlights the effectiveness of trust-based behavioural analysis for ensuring resilient, secure, and accountable operation in complex collaborative ecosystems.

The Edge-Cloud Continuum stands out as a promising paradigm that brings computation closer to the data source to improve efficiency and reduce latency. However, deploying edge devices with limited resources poses significant challenges, as they play a vital role in the continuum and require trusted interactions for secure and reliable data exchange. The Zero Trust Architecture (ZTA) paradigm has emerged as a potential solution to address trust issues, operating on the principle of ‘never trust, always verify’. Yet, ZTAs increase overhead due to the increased computational demands of its dynamic policies. To balance the benefits of heightened security with potential performance overhead, this work proposes TRUDI, a technique to enhance Trust via RepUtation management and anomaly DetectIon in zero trust-based networks. TRUDI combines a performance-based trust assessment mechanism with an anomaly detection mechanism, allowing more consecutive sessions without resource-intensive authentication and authorisation, while maintaining system integrity. TRUDI has been evaluated in edge computing environments with limited resources, with simulation results confirming its effectiveness in zero trust settings.

The Edge-Cloud Continuum (ECC) has emerged as a key enabler for the next generation of data-driven AI applications to effectively address challenges related to latency, bandwidth and regulatory compliance. Nonetheless, securing ECC environments remains a challenge, as traditional perimeter based security measures are inadequate for the inherently distributed and borderless nature of ECC, where data processing occurs across multiple devices and domains. The Zero Trust (ZT) security paradigm emerges to address these critical security challenges by emphasising stringent access controls, continuous authentication and least privileged access policies to minimise attack surfaces and mitigate potential cyber threats. Despite its relevance, there is a notable lack of ZT-enabled testing environments that allow researchers and developers to evaluate ECC applications under realistic security and regulatory constraints. This paper presents a ZT-enabled testbed (ECC Testbed) that enables evaluation of data-driven applications within authentic security and regulatory contexts. The ECC testbed is engineered to support the practical deployment of secure, efficient and regulation-aware edge-cloud solutions. It also streamlines the onboarding process for new application deployments. By filling the current gap in testing environments for ECC applications, ECC testbed serves as a valuable resource for researchers and developers. Through the integration of robust security measures, it supports and promotes the safe adoption of ECC solutions in real-world scenarios.

IoT systems consist of multiple heterogeneous sensors, actuators, and control logic. These systems not only collect data from the physical world around us, they play a critical role in supporting decision-making processes. Trust is essential in this context, as decision-makers must rely on the system's ability to perform its assigned tasks reliably. To ensure system-level trust, a trust analyser is implemented to assess the trustworthiness of IoT systems across seven distinct evaluation categories. Various tools and techniques can be applied within these categories, and the accuracy of each tool must be considered when aggregating trust evaluations to produce a representative trust score. To address this, several machine learning based aggregation methods are explored and compared, including Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN), and the Tsetlin Machine (TM). ANN and TM achieved 98% accuracy in correctly detecting trust attacks, while ANFIS achieved 76% accuracy. In addition, both ANFIS and TM offer interpretability, providing valuable insight into how they detect and flag attacks within the IoT system.

The proposed work investigates the use of lightweight convolutional neural networks (CNNs) for detecting vulnerability patterns in Solidity RGB-encoded smart contracts. Unlike heavy CNN models, which can be computationally intensive and fall short of optimal accuracy levels, the proposed study emphasizes efficiency. Transforming smart contract source code into RGB images not only reinforces security and protects proprietary information but also addresses compactness concerns, enabling convenient storage on online platforms. This approach ensures efficient use of bandwidth, enabling rapid scanning of contracts for potential vulnerabilities post-deployment. The streamlined mechanism allows for quick and simultaneous assessment of thousands of contracts within seconds, a task that proves challenging with rigorous formal verification tools. This methodology aligns with the need for both security and efficiency in the dynamic landscape of smart contract development and deployment.

Traditional federated learning (FL) adopts a client-server architecture where FL clients (e.g., IoT edge devices) train a common global model with the help of a centralized orchestrator (cloud server). However, current approaches are moving away from centralized orchestration toward a decentralized one in order to fully adapt FL for a cross-silo configuration with multiple organizations acting as clients. State-of-the-art decentralized FL mechanisms make at least one of the following assumptions: 1) clients are trusted organizations and cannot inject low-quality model updates for aggregation and 2) client local models can be shared with other clients or a third party for verification of low-quality updates. This article proposes a Blockchain-based decentralized framework for scenarios where participatory organizations are believed to be fully capable of injecting low-quality model updates as they are not willing to expose their local models to any other entity for verification purpose. The proposed decentralized FL framework adopts a novel hierarchical network of aggregators with the ability to punish/reward organizations in proportion to their local model quality updates. The framework is flexible and unlike state-of-the-art solutions, prevents a single entity from possessing the aggregated model in any FL round of training. The proposed framework is tested with respect to off-chain and on-chain performance in two Industry 4.0 use cases: 1) predictive maintenance and 2) product visual inspection. A comparative evaluation against the state-of-the-art reveals the proposed framework’s utility in terms of minimizing model convergence time and latency while maximizing accuracy and throughput.

The rapid acceleration of digitalization has intensified the focus on the use of Digital Twin (DT) across industries. While the concept of DT is not new in itself, the large-scale adoption across the industries is still maturing. The number of DTs deployed will continue to increase significantly1and will represent components, systems, interactions, people, and even business processes. This will drive the need to connect multiple DTs that can operate seamlessly across systems and business boundaries, forming a collaborative digital ecosystem. The term Composite Digital Twin (CDT) is used to represent this interconnection and integration of DTs. To implement a CDT, several challenges such as trust, interoperability, governance, ownership, security, and privacy need to be addressed. First, this study explores the requirements to create a CDT covering operational, management, and security standpoints. After analyzing CDT requirements, an architecture is proposed to encapsulate the core functional and security requirements to enable emerging collaborative digital ecosystems. The architecture emphasis is placed on security and trust among participants to ensure that developers, providers, and users can have confidence in the services these CDT provide.

Collaborative robots (cobots) are becoming more prominent in the manufacturing industry due to their ability to operate outside safety zones and work in tandem with humans to perform precise and repetitive tasks, such as visual inspection, product categorization, quality control, etc. Cobots generally have limited computational resources that limit their ability to perform complex machine learning (ML) tasks and as such, various cloud- and fog-based computational task offloading mechanisms have been proposed. However, a growing reluctance to share manufacturing data on the cloud, cybersecurity concerns, and demand of agile decision making is encouraging researchers to design resource-sharing frameworks for on-floor cobots where they can share the execution of complex ML tasks. However, agile on-floor environments and the potential presence of malicious elements make reliable task offloading a significant challenge. This article investigates reliability issues and their effects on executing complex ML task executed by participating on-floor cobots. Specifically, this article aims to answerwhom to offload?with the objective of ensuring reliability, security, and data protection when offloading computation tasks. To this end, a reputation-based collaborative robotic learning (CoRoL) framework is proposed with the ability to isolate and/or minimize the impact of malicious or poor-performing cobots on computation task execution. In addition, CoRoL is supported by split learning for privacy-preserving task offloading with minimum data exchange. Simulation results and comparative analysis will demonstrate CoRoL’s efficiency in terms of percentage of completed tasks, achieved accuracy, and impact on energy consumption.

The EU data strategy postulates that by 2025 there will be a paradigm shift towards more decentralized intelligence and data processing at the edge. The convergence of a large number of nodes at the IoT edge along with multiple service providers and network operators exposes data owners and resource providers to potential threats. To address cloud-edge risks, trust-based decentralized management is needed. Blockchain technology has created an opportunity to decentralize IoT ecosystems, through its intrinsic properties and together with machine learning (ML) it can be used to provide a trusted backbone for managing IoT ecosystems to support automated and adaptive trust management. This paper presents a novel approach for crosslayer intelligent trust computation modelling leveraging ML and Blockchain for decentralized trust management in IoT ecosystems. The effectiveness of the proposed approach for flow-based trust assessment is demonstrated using the Hyperledger Framework and the Cooja-based simulation environment. Finally, an initial evaluation is presented to understand the performance in terms of scalability and trust convergence of the proposed model.

This paper presents a passive data rate estimation method that leverages commonly available parameters of commercial modems with application in Intelligent Transportation Systems. The estimation is performed by utilizing an Adaptive Similarity-based Regression (ASR) approach. This constitutes the use of Support-Vector Regression (SVR) in conjunction with a similarity-based unlearning algorithm. It is demonstrated that this approach can adapt to the various properties of different mobile networks, while maintaining a fixed training set size. This is particularly useful in cases where training data is not available in large quantities, or the uplink rate is limited by the users subscription. ASR is developed as a set of modular components and as such can be used as an enhancement to protocols such as multipath Transmission Control Protocol (TCP), Software-Defined Networking (SDN), or as an aid to Quality-of-Service (QoS) routing. It is shown that the algorithm can achieve satisfactory performance with as little as 24 training samples, and can be deployed across different mobile networks without the need of pre-training. The solution is validated using a custom test-bed to perform mobile network measurements, gathering over 15, 000 measurement samples. In addition, the algorithm is tested using measurements collected under real life conditions both in a moving car and train. As there is a shortage of open source data in the field of rate estimation in mobile networks, we publish all of the data sets used in this paper to encourage further research on the subject.

Energy consumption scheduling algorithms allow energy harvesting Internet of Things (IoT) devices to maximize the amount of harvested energy that they consume, while maintaining uninterrupted, indefinite, operation. The existing works in the area show a tradeoff between solution quality and computational complexity. At one end fast but suboptimal algorithms can lead to energy waste and power outages. At the other, optimal algorithms are computationally prohibitive for the constrained hardware of the IoT. This paper argues that the tradeoff can be avoided, and presents the MAllEC energy consumption scheduler that maximizes the allowed energy consumption while minimizing energy waste and power outages, with linear time complexity. MAllEC is compared against the state of the art through simulations using long term (14 years) traces of solar irradiance, and shown to consistently achieve the minimum energy waste and power outage. The linear time complexity of MAllEC is measured on constrained IoT hardware (8-bit Tmote Sky) to be low enough so that MAllEC can be executed unintrusively. This paper provides proof of MAllEC’s optimality and shows that, in an application with dynamic, adjustable packet rate, MAllEC can maintain indefinite, uninterruptible, operation at an average rate of almost 100 packets per minute, where a 3-Ah battery powered device, at the same rate, would deplete after less than 200 days.

This paper describes the creation of an IoT driven architecture to support the realization of an OpenBMS approach to managing blocks of buildings. The objective is to overcome the complexities of integration, operation and management of heterogeneous building systems by leveraging existing IoT approaches. The goal is to eliminate vertical data silos and enable the holistic management of energy across existing and new building blocks.

This paper provides an overview of the architecture for self-organizing, co-operative and robust Building Automation Systems (BAS) proposed by the EC funded FP7 SCUBA1project. We describe the current situation in monitoring and control systems and outline the typical stakeholders involved in the case of building automation systems. We derive seven typical use cases which will be demonstrated and evaluated on pilot sites. From these use cases the project designed an architecture relying on six main modules that realize the design, commissioning and operation of self-organizing, co-operative, robust BAS.

There is a strong push towards smart buildings that aim to achieve comfort, safety and energy efficiency, through building automation systems (BAS) that incorporate multiple subsystems such as heating and air-conditioning, lighting, access control etc. The design, commissioning and operation of BAS is already challenging when handling an individual subsystem; however when introducing co-operation between systems the complexity increases dramatically. Balancing the contradictory requirements of comfort, safety and energy efficiency and coping with the dynamics of constantly changing environmental conditions, usage patterns, user needs etc. is a demanding task. This paper outlines an approach to the systematic engineering of cooperating, adaptive building automation systems, which aims to formalize the engineering approach in the form of an integrated tool chain that supports the building stakeholders to produce site-specific robust and reliable building automation.

Wireless senor networks (WSNs) for building automation are a low cost solution in terms of installation and retrofit. WSN provide building operators with the opportunity to monitor and control building performance to improve efficiency by becoming more energy usage aware and demand responsive. However, the penetration of wireless sensing technology has been hampered by concerns regarding the reliability and manageability of wireless systems in harsh operating environments. The traditional solutions to address WSN reliability and manageability are to employ high levels of node redundancy and to embed self-management functions within communications protocols themselves. However the disadvantages of this approach are cost and non-optimum behaviour in large scale systems. The main motivation for a building operator to deploy a WSN is cost reduction and hence the costly requirement for high levels of node redundancy is unlikely to provide a satisfactory solution. WSNs deployed for building monitoring, unlike a typical communications network, is part of a broader building management business designed to curtail operational overheads for an enterprise. For buildings the physical deployment of a building management system is likely to be a once off roll out. However, the internal layout of the building is often dynamic as the traditional role of building owner/user has shifted and it is common practice now for several companies to lease space within the one building. With a once off BMS deployment the installation and use of the network tended to be completely independent activities. It is now well accepted that a continuous commissioning approach to building operation is needed to maintain optimum building performance. We argue that this also needs to extend to the wireless sensing infrastructure, creating demand for a continuous wireless infrastructure (wi) design, deployment and reconfiguration lifecycle process that optimises the wireless infrastructure aspects of the BMS. We propose Wi* an innovative solution for the design and management of wireless sensing infrastructure capable of interfacing with the BMS to provide an integrated technology platform for fine grained building automation.

WiFi localisation has become very popular in recent years. Most widely used are fingerprinting based techniques where a map of received signal strengths is used to infer the position based on comparing the current signal strength measurement to this map. Most research focuses on this inference itself and on the creation of accurate fingerprinting maps. In this paper we will not address those issues in depth but focus on analysing the fingerprint maps themselves in more detail. By looking closely into the maximum likelihood estimation for the position we will derive its expected uncertainty and show that it can be calculated for every possible position in advance from the fingerprint maps alone. This allows to derive an expected localisation accuracy map of the environment that can be used to assess and optimise the WiFi design based on localisation accuracy needs rather than relying on given access point placements solely based on coverage or signal-to-noise criteria.

The design and deployment of a wireless sensor network (WSN) for building automation applications is a complex operation that requires expert knowledge and experience. This paper presents an evaluation of a WSN deployment support framework for in-building wireless infrastructures. A case study consisting of a sample network deployment for environmental monitoring is used to investigate the need for such support tools. The network infrastructure design suggested by the deployment support tool is compared against designs done using basic planning guidelines and a design based on an extensive site survey and experience. It will be shown how the deployment support tools provide a WSN with a reduced infrastructure cost and improved sensing packet delivery ratio when compared to the designs using traditional approaches.

As wireless embedded systems become more and more common and used across many application domains there is a need for modeling and design tools to support the deployment process. Although a significant amount of research has been carried out in the area of protocol design, middleware and energy, packaging and embedded systems design, there remains a lack of support tools for designers and system integrators when deploying complex indoor wireless infrastructures (Wi) to support site specific applications. In this paper we present a modeling tool known as Wi-Design that was developed to provide deployment support for engineers and system integrators when planning a wireless sensor infrastructure with particular focus on in building wireless applications. We show how the tool can simplify the deployment process and provide enhanced confidence in wireless deployments.

The complex nature of wireless local area networks (WLAN) design has led many of the deployments being done in an ad-hoc fashion without efficient design methodologies. Although this approach may work for a small environment with a small number of access points, it is infeasible to use such a process when designing a larger wireless infrastructure. Due to the low cost that is indicative of WLAN deployments, many practitioners view formal optimization techniques as being too complex and costly to implement. There have been a number of research works that investigate the use of formal optimization techniques for the accurate design of a WLAN. Unfortunately, the approaches taken do not address one major issue when designing a complex and demanding wireless network infrastructure, namely scalability. An optimization algorithm must consider a multitude of design criteria and therefore needs to be scalable to be successfully applied to large scenarios. The main contribution of the work presented in this paper is the development of a scalable optimization algorithm based on the tools of distributed artificial intelligence, which overcomes the failings of current approaches and can be utilized for WLAN design regardless of size or complexity of site specific requirements.

The complex nature of Wireless Local Area Network (WLAN) design, especially when designing large scale networks underlines the need for an automatic WLAN planning tool. The current approach to WLAN design is ad hoc and can lead to an adverse affect on network and service quality. There has been significant research into optimisation techniques and planning tools, many of which use a centralised optimisation approach that is not scalable especially when designing large scale WLAN. The research presented in this paper proposes an approach to WLAN design that is fully distributed based on a scalable optimisation technique. The proposed approach uses elements of Artificial Intelligence and Game Theory to design a viable WLAN regardless of the environment size.

The rapid increase in the use of IEEE 802.11 wireless local area networks (WLAN) for a diverse range of applications, has introduced an increased complexity into WLAN design, as regards to accurate access point (AP) position assessment, which can severely impact the performance of large scale WLANs. This paper presents two approaches to WLAN design that allows the designer to describe where the WLAN is to be deployed and define the design requirements based on signal coverage and usage. Both methods automatically optimise and suggest a WLAN design that satisfies the user requirements. Both approaches have been implemented and evaluated based on signal coverage and maximum achievable throughput.