Ensuring reliable update and evolution of a virtual twin in human digital twin (HDT) systems depends on any connectivity scheme implemented between such a virtual twin and its physical counterpart. The adopted connectivity scheme must consider HDT-specific requirements including privacy, security, accuracy and the overall connectivity cost. This paper presents a new, secure, privacy-preserving and efficient human-to-virtual twin connectivity scheme for HDT by integrating three key techniques: differential privacy, federated multi-task learning and blockchain. Specifically, we adopt federated multi-task learning, a personalized learning method capable of providing higher accuracy, to capture the impact of heterogeneous environments. Next, we propose a new validation process based on the quality of trained models during the federated multi-task learning process to guarantee accurate and authorized model evolution in the virtual environment. The proposed framework accelerates the learning process without sacrificing accuracy, privacy and communication costs which, we believe, are non-negotiable requirements of HDT networks. Finally, we compare the proposed connectivity scheme with related solutions and show that the proposed scheme can enhance security, privacy and accuracy while reducing the overall connectivity cost.

Blockchain and cloud-edge computing techniques are promising technologies for next-generation, secure, and privacy-preserving data-sharing systems. By integrating these technologies, the data demands of many data users, such as research institutes, hospitals, manufacturers, etc., can be met. Despite this promising integration, it is yet to be understood how the vulnerability and uncertainties of wireless communication links between the data producers, blockchain systems, cloud-edge computing-based platforms, and data users, as well as unstable validation parameters can affect the overall performance of such blockchain-enabled data-sharing systems. In this article, we considered a collaborative data-sharing scheme, where multiple data providers and data users collaborate to accomplish data-sharing tasks through the proposed blockchain and cloud-edge computing schemes. We considered the spatial distribution of data providers and data users to follow the independent homogeneous Poisson point process, while the transactions generation rate at each node was also modeled using an independent Bernoulli process. We then obtained analyses for some selected performance metrics of interest and evaluate the performance of the system. The obtained results showed that the proposed analyses can be useful in investigating the performance of any blockchain-enabled data-sharing system. This will aid successful deployments of effective data-sharing systems.

This paper presents a multi-user, multi-class and multi-layer edge computing-based framework for effective task offloading and computation processes. Important system requirements that were not captured in the existing multi-layer solutions such as offloading, computations and deadline requirements were captured in the system modeling, while both wireless communications and task computation constraints were considered. We considered three layers system, where each device offloads its generated tasks in each time slot to any selected layer for computation. On its arrival at such a selected layer, the task is only accepted if the queue size is below the pre-defined threshold, otherwise, such a task is offloaded to the next layer. Tasks were classified into class 1 and class 2 tasks following tasks’ quality of service requirements. We adopted stochastic geometry, parallel computing and queueing theory techniques to model the performance of the considered integrated edge-fog-cloud computing environment and obtained analysis for various performance metrics of interest. The obtained analyses demonstrate the importance of multi-layer and multi-class edge computing systems towards improving the experience of both delay-sensitive and mission-critical applications in any task offloading environment.

Secure data sharing is imperative in human digital twin (HDT) systems due to the continuous communication requirements among physical and virtual twins, making data security and privacy essential concerns. Previous works have emphasized the significance of blockchain technology in mitigating security challenges within digital twin systems. Nevertheless, existing blockchain-based solutions often fall short of meeting the specific latency and throughput demands of HDT systems, primarily attributed to the complicated consensus process of conventional blockchain solutions. As a result, this article introduces a novel reputation-enhanced shard-based Byzantine fault-tolerant scheme designed for zero-trust HDT systems. We propose a parallel validation-based reputation-enhanced practical Byzantine fault tolerance consensus framework to address the need for improved throughput and reduced latency during data-sharing processes. This framework incorporates a priority-based block-appending process to prevent forking attacks, ensuring that critical aspects of the blockchain-enabled framework, such as security and decentralization, remain uncompromised. Moreover, we formalize the communication process among validators and their computation resource allocation as a Markov decision process. We then adopt the branching duelling Q-network approach to address the challenge posed by the large dimensions of the action space in our formulated problem. The results demonstrate that the proposed framework significantly enhances authentication, authorization, and validation processes in HDT through increased throughput and reduced latency, providing a robust solution for secure and efficient data sharing in HDT systems.

Data timeliness, privacy, and security are key enablers for data-sharing systems to support time-sensitive and mission-critical systems and applications. While blockchain-enabled data sharing frameworks can offer reliable security and privacy when properly implemented, the timeliness of data and the related latency are important issues that can limit the adoption of blockchain in large-scale mission-critical applications. This paper thus carried out a performance analysis of the blockchain-enabled data-sharing framework from latency and data age perspectives to investigate the suitability of blockchain technology in data sharing systems. To achieve this, the uniqueness of such systems such as transactions validation latency, transaction generation rate, waiting time, blockchain-appending rate, and overall communication latency were jointly studied. The communication latency was characterized following the spatiotemporal modeling approach. We further adopted the practical Byzantine fault tolerance (PBFT) consensus protocol due to its well discussed suitability in large-scale data sharing applications and captured the validation stages of such a PBFT scheme using the Erlang distribution of order $k$ . Simulations results show that various influential system parameters must be carefully considered when adopting blockchain technology in time-sensitive data sharing applications. This will guide the adoption of blockchain technology in various data sharing applications and systems.