As the number of IoT devices increases, sustainability is becoming a bottleneck of the production process in industrial systems. As a matter of fact, inefficient management and scarce resources significantly impeded the development of sustainability. In recent years, it has been observed that the digital twin (DT) technology plays a promising role in facilitating the interaction between the Internet of Things (IoT) assets and digital services. However, high-fidelity models of DTs raise the requirement of efficient data flows, which is limited by realistic constraints, such as data collection strategy and energy supply. We propose a sustainable data collection and management approach to construct DTs for physical assets. With this approach, data packets are uploaded to the data brokers, namely, agents, by a large number of IoT devices. The challenge lies in the balance between enduring data collection and the information loss associated with the stale data. In this article, we aim to optimize the metrics of data fidelity and reveal delay while guaranteeing both sustainable energy and sustainable information. Additionally, a shareable and sustainable blockchain-based DT management architecture is proposed, which does not rely on data exchanges with a single centralized server. Our analytical and simulation results demonstrate the applicability of our proposed architecture.

The great market success of Blockchain makes it an extremely valuable target for attackers. A well-known attack in Blockchain is the forking attack, where divergent blockchains are produced for inserting some new features to facilitate security breaches. The state-of-the-art works mostly focus on how to detect attacks in real-time transactions, which is in hindsight and cannot deter the forking attack from the root. To take precautions, we employ the large deviation theory to study the vulnerability of blockchain networks incurred by intentional forks from a micro point of view, boosting forward-looking and strategic planning mechanisms for resisting the forking attack. Our study is fine-grained, because it offers not only the vulnerability probability of a blockchain network but also its decay speed, through which we find setting the parameter related to the robust level has more power than enhancing the computer power in speeding up the failure of attacks. This finding is valuable since it renders an opportunity to improve the robustness of a blockchain network in a cost-efficient way. Our analysis is complementary, since it studies both the impacts of the computational power as well as the number of confirmations on the vulnerability of a blockchain network, providing a theoretical basis to design reasonable schemes for invigorating a blockchain network from technical as well as managerial levels. Extensive experiments carried out on a large-scale cloud platform running the Ethereum protocol show the experimental and analytical results match well, verifying the effectiveness of our analysis.

The increasing number of products is the trend of current industry. However, the product development process is significantly limited by budget and testing risk. Recently, digital twin (DT) has emerged as a promising industrial paradigm that provides an integrated and cohesive view of the product design process. In this article, we propose a product design framework for Internet of Things (IoT) platforms, namely, DT-aided IoT platform design (DTIPD). This framework considers a large number of IoT devices performing different tasks with machine learning (ML) technologies. Each IoT device constructs a particular ML-based model that deals with its task automatically by feeding related data with labels. The challenges of large-scale network management and ground-truth shortage at the initial stage of product iteration are addressed. We propose a two-level hierarchical learning process using the real-time model status stored at DT servers (DTS), aiming to improve product quality while shortening the development lifecycle. The comprehensive experimental results for both the single-DTS and multiple-DTS scenarios demonstrate the applicability of our framework.

Digital Twins (DTs) have become essential tools for improving efficiency, security, and decision-making across various industries. DTs enable deeper insight and more informed decision-making through the creation of virtual replicas of physical entities. However, they face privacy and security risks due to their real-time connectivity, making them vulnerable to cyber attacks. These attacks can lead to data breaches, disrupt operations, and cause communication delays, undermining system reliability. To address these risks, integrating advanced security frameworks such as blockchain technology offers a promising solution. Blockchains’ decentralized, tamper-resistant architecture enhances data integrity, transparency, and trust in DT environments. This paper examines security vulnerabilities associated with DTs and explores blockchain-based solutions to mitigate these challenges. A case study is presented involving how blockchain-based DTs can facilitate secure, decentralized data sharing between autonomous connected vehicles and traffic infrastructure. This integration supports real-time vehicle tracking, collision avoidance, and optimized traffic flow through secure data exchange between the DTs of vehicles and traffic lights. The study also reviews performance metrics for evaluating blockchain and DT systems and outlines future research directions. By highlighting the collaboration between blockchain and DTs, the paper proposes a pathway towards building more resilient, secure, and intelligent digital ecosystems for critical applications.

The presence of digital twins (DTs) has expanded within the consumer electronics area due to its inherent benefits of high-fidelity modeling and predictive insight. However, DT deployment remains costly and constrained in providing the fundamental functionalities required by consumer electronics systems due to massive computation and communication. The emergence of mobile edge computing (MEC) has made DT deployment feasible to achieve data-driven modeling and consumer-centric concurrently with low communication costs. This article proposes an MEC-based DT deployment scheme in the smart home domain. The cloud service can predict subsequent sensor updates leveraging the MEC platform’s human activity recognition result of residential environments. In addition, with deep reinforcement learning (DRL), it can track the essential data to maintain sensor update consistency between both physical and virtual sides. We implement experimental evaluation with two real-world residential datasets, and the results demonstrate that our scheme maintains high-level sensor update consistency while being energy-efficient in different residential environments, which illuminates the promising prospects of DT implementation in MEC and consumer electronics.

There is growing attention in the metaverse from a variety of fields, and many Internet-of-Things (IoT) companies are exploring the possibility of integrating their existing business into the metaverse space. However, it still remains challenging to put the metaverse into practice. Among those challenges, trust management is critical to guarantee secure interactions and effective sharing among metaverse assets. In this article, we construct a TMETA system as a pioneer work for further establishing the metaverse and utilizing digital assets, which helps IoT startup companies expand their business during the cold-start phase with limited business knowledge and seed budget. In the TMETA system, IoT companies treat their interaction experience with resource providers as digital assets. When an IoT startup initiates IoT services with the assistance of expertise-diverse resource providers, task assignments are determined by leveraging the knowledge and trustworthy advice of expert IoT companies. With the aid of digital twin (DT) and blockchain technology, IoT tasks will be assigned to their suitable resource providers as the secured instruction from trust DTs of expert companies, which are evaluated and updated by trust evolution and advice aggregation. We demonstrate our proposed TMETA system’s practicability with synthetic and real-world data. The experimental results indicate that our proposed TMETA system can help IoT startups attain more favorable outcomes and reduce the task failure rate in various trust environments, thereby accelerating the cold-start process.