• 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.

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.

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.

The INSPEX H2020 project main objective is to integrate automotive-equivalent spatial exploration and obstacle detection functionalities into a portable/wearable multi-sensor, miniaturised, low power device. The INSPEX system will detect and localise in real-time static and mobile obstacles under various environmental conditions in 3D. Potential applications range from safer human navigation in reduced visibility, small robot/drone obstacle avoidance systems to navigation for the visually/mobility impaired, this latter being the primary use-case considered in the project.

Energy consumed by computation and cooling represents the greatest percentage of the average energy consumed in a data centre. As these two aspects are not always coordinated, energy consumption is not optimised. Data centres lack an integrated system that jointly optimises and controls all the operations in order to reduce energy consumption and increase the usage of renewable sources. GENiC is addressing this through a novel scalable, integrate energy management and control platform for data centre wide optimisation. We have implemented and prototype of the platform together with workload and thermal management algorithms. We evaluate the algorithms in a simulation based model of a real data centre. Results show significant energy savings potential, in some cases up to 40%, by integrating workload and thermal management.

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.

The current growth in embedded ICT infrastructure, driven by visions such as the "Smart Cities" concept, is leading to the deployment of a wide range of embedded systems in our environment, which motivates the need for a reusable, flexible and manageable Wireless Sensor Network or WSN infrastructure. However, to simplify the system operation and maintenance as well as to reduce costs, WSNs mus become an infrastructure that is capable of providing services to multiple end users concurrently, rather than having to roll out individual infrastructures for specific purposes. Here, we present the concept of a WSN infrastructure as a WSN Cloud, which provides services to multiple application and data collection systems which adheres to the cloud computing paradigm. Each instance of the WSN Cloud (i.e. a specific set of services configured by a particular end user/system) utilises the WSN infrastructure as if it was a unique network provisioned for their specific requirements. This realisation of the WSN Cloud as Network as a Service or NaaS requires the WSN to support a service orientated software architecture allowing other systems to provision the WSN infrastructure for their needs with NaaS allowing multiple systems to use the WSN uniquely and concurrently. The WSN-Service Orchestration Architecture "WSN-SOrA" presented here is a novel approach to orchestrate service provisioning for embedded networked systems, and enables WSNs to act as cloud ready infrastructures that facilitate on-demand provisioning for potentially multiple individual backend systems.

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.

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.

With recent advances in vehicular technology, a variety sensors, radars and onboard computing systems have enabled vehicles to become powerful information gathering and processing platforms. Sensors can continuously monitor and interpret the vehicle's local environment and quickly detect dangerous situations. As vehicles in close proximity detect the same dangerous situation they will inevitably broadcast messages relating to the same event. As all these vehicles report on the same event broadcasting leads to dramatically excessive message redundancy. In the paper we present the Event Suppression for Safety Message Dissemination (ESSMD) scheme that reduces the number of broadcasting vehicles reporting on the same event. This scheme was compared with existing aggregation strategies for safety-related message dissemination. Experimental results using the OPNET simulator demonstrate that ESSMD significantly reduces redundant data transmissions and does not add any extra end-to-end delay compared with existing aggregation strategies. However, ESSMD had a decreasing probability of event reception due to an unreliable broadcasting protocol. The reception rate was improved by the introduction of a scheme called ESSMD+Rep that increases the reliability of the broadcasting protocol by repeating broadcasts at the source vehicles.

Vehicular communication is regarded as a major innovative feature for in-car technology. While improving road safety is unanimously considered the major driving factor for the deployment of Active Safety Intelligent Vehicle Safety Systems, the challenges relating to reliable multi-hop broadcasting are many in vehicular networking. In fact, safety applications must rely on very accurate and up to date information about the surrounding environment, which in turn requires the use of accurate positioning systems and smart communication protocols for exchanging information. Communications protocols for vehicular ad hoc networks (VANETs) must guarantee fast and reliable delivery of information to all vehicles in the neighbourhood, where the wireless communication medium is shared and highly unreliable with limited bandwidth. In this paper, we present a geo-broadcasting extension to the Slotted Restricted Mobility-Based (SRMB) broadcasting protocol and compare it with existing geobroadcasting protocols in highway environments.