Over the last few years, Digital Twin (DT) has emerged as an innovative concept that integrates multiple technologies to mirror physical assets, systems, and processes. Assisted by data analytics, predictive models, and optimisation techniques, DTs are suitable for enhancing operations in virtual space before transferring information into real-world counterparts. Moreover, initiatives considering DTs as part of a composite complex system might consider federated ecosystems for exchanging insights and relevant information. In this context, the Federated Digital Twin (FDT) concept is a potential solution to address interaction among virtual entities, enabling advanced operations and ensuring collaborative decision-making. This study describes a comprehensive FDT framework inspired by principles and methodologies considered by well-studied federated systems. Furthermore, a reference abstract architecture allowing seamless integration among multiple agent-based DTs is provided as a tool to develop a wide range of DT-based applications.

Urban vehicle emissions are one of the main contributors to air pollution since most vehicles still rely on fossil fuels, despite the growing popularity of alternative options such as hybrids and electric cars. Recently, Artificial Intelligence (AI) and automation-based controllers have gained attention for their potential use in adaptive traffic signal control. Many studies have been conducted on the application of Deep Reinforcement Learning (DRL) models to reduce travel time in adaptive traffic signal control. However, limited research has been done on adapting traffic signal control to reduce CO2 emissions and fuel consumption in urban vehicles. As such, this work proposes a digital-twin-based adaptive traffic signal control approach that relies on a digital twin of urban traffic network and uses the DRL Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to optimise for reduced fuel consumption and CO2 emission. The system is designed to simulate different traffic scenarios and control strategies, enabling for adaptation in traffic signal adjustments. To assess the effectiveness and applicability of the proposed approach, a quantitative simulation is performed using synthetic and real-world traffic datasets from a multi-intersection network in a neighbourhood in Amman, Jordan, during peak hours. The findings suggest that the DRL approach based on digital twins on synthetic networks can reduce CO2 emissions and fuel consumption even when using a basic reward function based on stopped vehicles.

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence; and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems.

Blockchain offers a distributed ledger to record data collected from Internet of Thing (IoT) devices as immutable and tamper-proof transactions and securely shared among authorized participants in a Peer-to-Peer (P2P) network. Despite the growing interest in using blockchain for securing IoT systems, there is a general lack of systematic research and comprehensive review of the design issues on the integration of blockchain and IoT from the software architecture perspective. This article presents a catalog of architectural tactics for the design of IoT systems supported by blockchain as a result of a Systematic Literature Review (SLR) on IoT and blockchain to extract the commonly reported quality attributes, design decisions, and relevant architectural tactics for the architectural design of this category of systems. Our findings are threefold:<?brk?> (i) identification of security, scalability, performance, and interoperability as the commonly reported quality attributes; (ii) a catalog of twelve architectural tactics for the design of IoT systems supported by blockchain; and (iii) gaps in research that include tradeoffs among quality attributes and identified tactics. These tactics might provide architects and designers with different options when searching for an optimal architectural design that meets the quality attributes of interest and constraints of a system.

The use of Internet of Things (IoT) has introduced genuine concerns regarding data security and its privacy when data are in collection, exchange, and use. Meanwhile, blockchain offers a distributed and encrypted ledger designed to allow the creation of immutable and tamper-proof records of data at different locations. While blockchain may enhance IoT with innate security, data integrity, and autonomous governance, IoT data management and its allocation in blockchain still remain an architectural concern. In this article, we propose a novel context-aware mechanism for on-chain data allocation in IoT-blockchain systems. Specifically, we design a data controller based on fuzzy logic to calculate the Rating of Allocation (RoA) value of each data request considering multiple context parameters, i.e., data, network, and quality and decide its on-chain allocation. Furthermore, we illustrate how the design and realization of the mechanism lead to refinements of two commonly used IoT-blockchain architectural styles (i.e., blockchain-based cloud and fog). To demonstrate the effectiveness of our approach, we instantiate the data allocation mechanism in the blockchain-based cloud and fog architectures and evaluate their performance using FogBus. We also compare the efficacy of our approach to the existing decision-making mechanisms through the deployment of a real-world healthcare application. The experimental results suggest that the realization of the data allocation mechanism improves network usage, latency, and blockchain storage and reduces energy consumption.