Digital Communications and Networks最新文献

Digital Twin (DT) supports real time analysis and provides a reliable simulation platform in the Internet of Things (IoT). The creation and application of DT hinges on amounts of data, which poses pressure on the application of Artificial Intelligence (AI) for DT descriptions and intelligent decision-making. Federated Learning (FL) is a cutting-edge technology that enables geographically dispersed devices to collaboratively train a shared global model locally rather than relying on a data center to perform model training. Therefore, DT can benefit by combining with FL, successfully solving the "data island" problem in traditional AI. However, FL still faces serious challenges, such as enduring single-point failures, suffering from poison attacks, lacking effective incentive mechanisms. Before the successful deployment of DT, we should tackle the issues caused by FL. Researchers from industry and academia have recognized the potential of introducing Blockchain Technology (BT) into FL to overcome the challenges faced by FL, where BT acting as a distributed and immutable ledger, can store data in a secure, traceable, and trusted manner. However, to the best of our knowledge, a comprehensive literature review on this topic is still missing. In this paper, we review existing works about blockchain-enabled FL and visualize their prospects with DT. To this end, we first propose evaluation requirements with respect to security, fault-tolerance, fairness, efficiency, cost-saving, profitability, and support for heterogeneity. Then, we classify existing literature according to the functionalities of BT in FL and analyze their advantages and disadvantages based on the proposed evaluation requirements. Finally, we discuss open problems in the existing literature and the future of DT supported by blockchain-enabled FL, based on which we further propose some directions for future research.

The digital twin is the concept of transcending reality, which is the reverse feedback from the real physical space to the virtual digital space. People hold great prospects for this emerging technology. In order to realize the upgrading of the digital twin industrial chain, it is urgent to introduce more modalities, such as vision, haptics, hearing and smell, into the virtual digital space, which assists physical entities and virtual objects in creating a closer connection. Therefore, perceptual understanding and object recognition have become an urgent hot topic in the digital twin. Existing surface material classification schemes often achieve recognition through machine learning or deep learning in a single modality, ignoring the complementarity between multiple modalities. In order to overcome this dilemma, we propose a multimodal fusion network in our article that combines two modalities, visual and haptic, for surface material recognition. On the one hand, the network makes full use of the potential correlations between multiple modalities to deeply mine the modal semantics and complete the data mapping. On the other hand, the network is extensible and can be used as a universal architecture to include more modalities. Experiments show that the constructed multimodal fusion network can achieve 99.42% classification accuracy while reducing complexity.

The rapid development of 5G/6G and AI enables an environment of Internet of Everything (IoE) which can support millions of connected mobile devices and applications to operate smoothly at high speed and low delay. However, these massive devices will lead to explosive traffic growth, which in turn cause great burden for the data transmission and content delivery. This challenge can be eased by sinking some critical content from cloud to edge. In this case, how to determine the critical content, where to sink and how to access the content correctly and efficiently become new challenges. This work focuses on establishing a highly efficient content delivery framework in the IoE environment. In particular, the IoE environment is re-constructed as an end-edge-cloud collaborative system, in which the concept of digital twin is applied to promote the collaboration. Based on the digital asset obtained by digital twin from end users, a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention (TPA) enabled Long Short-Term Memory (LSTM) model. Then, the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning (RL) technology. Finally, a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing overhead. The experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate, the average throughput, the successful content delivery rate and the average routing overhead.

With the maturity and development of 5G field, Mobile Edge CrowdSensing (MECS), as an intelligent data collection paradigm, provides a broad prospect for various applications in IoT. However, sensing users as data uploaders lack a balance between data benefits and privacy threats, leading to conservative data uploads and low revenue or excessive uploads and privacy breaches. To solve this problem, a Dynamic Privacy Measurement and Protection (DPMP) framework is proposed based on differential privacy and reinforcement learning. Firstly, a DPM model is designed to quantify the amount of data privacy, and a calculation method for personalized privacy threshold of different users is also designed. Furthermore, a Dynamic Private sensing data Selection (DPS) algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds. Finally, theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection, in particular, the proposed DPMP framework has 63% and 23% higher training efficiency and data benefits, respectively, compared to the Monte Carlo algorithm.

In traditional digital twin communication system testing, we can apply test cases as completely as possible in order to ensure the correctness of the system implementation, and even then, there is no guarantee that the digital twin communication system implementation is completely correct. Formal verification is currently recognized as a method to ensure the correctness of software system for communication in digital twins because it uses rigorous mathematical methods to verify the correctness of systems for communication in digital twins and can effectively help system designers determine whether the system is designed and implemented correctly. In this paper, we use the interactive theorem proving tool Isabelle/HOL to construct the formal model of the X86 architecture, and to model the related assembly instructions. The verification result shows that the system states obtained after the operations of relevant assembly instructions is consistent with the expected states, indicating that the system meets the design expectations.

Digital twinning enables manufacturers to create digital representations of physical entities, thus implementing virtual simulations for product development. Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages, failing to cover the gap between physical and digital spaces. This work mines real-world consumer feedbacks through social media topics, which is significant to product development. We specifically analyze the prevalent time of a product topic, giving an insight into both consumer attention and the widely-discussed time of a product. The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution. Therefore, these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics. To this end, this work combines deep learning and survival analysis to predict the prevalent time of topics. We propose a specialized deep survival model which consists of two modules. The first module enriches input covariates by incorporating latent features of the time-varying text, and the second module fully captures the temporal pattern of a rumor by a recurrent network structure. Moreover, a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction. Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

In the context of Industry 4.0, a paradigm shift from traditional industrial manipulators to Collaborative Robots (CRs) is ongoing, with the latter serving ever more closely humans as auxiliary tools in many production processes. In this scenario, continuous technological advancements offer new opportunities for further innovating robotics and other areas of next-generation industry. For example, 6G could play a prominent role due to its human-centric view of the industrial domains. In particular, its expected dependability features will pave the way for new applications exploiting highly effective Digital Twin (DT)- and eXtended Reality (XR)-based telepresence. In this work, a novel application for the above technologies allowing two distant users to collaborate in the programming of a CR is proposed. The approach encompasses demanding data flows (e.g., point cloud-based streaming of collaborating users and robotic environment), with network latency and bandwidth constraints. Results obtained by analyzing this approach from the viewpoint of network requirements in a setup designed to emulate 6G connectivity indicate that the expected performance of forthcoming mobile networks will make it fully feasible in principle.

As an emerging joint learning model, federated learning is a promising way to combine model parameters of different users for training and inference without collecting users’ original data. However, a practical and efficient solution has not been established in previous work due to the absence of efficient matrix computation and cryptography schemes in the privacy-preserving federated learning model, especially in partially homomorphic cryptosystems. In this paper, we propose a Practical and Efficient Privacy-preserving Federated Learning (PEPFL) framework. First, we present a lifted distributed ElGamal cryptosystem for federated learning, which can solve the multi-key problem in federated learning. Secondly, we develop a Practical Partially Single Instruction Multiple Data (PSIMD) parallelism scheme that can encode a plaintext matrix into single plaintext for encryption, improving the encryption efficiency and reducing the communication cost in partially homomorphic cryptosystem. In addition, based on the Convolutional Neural Network (CNN) and the designed cryptosystem, a novel privacy-preserving federated learning framework is designed by using Momentum Gradient Descent (MGD). Finally, we evaluate the security and performance of PEPFL. The experiment results demonstrate that the scheme is practicable, effective, and secure with low communication and computation costs.

As autonomous vehicles and the other supporting infrastructures (e.g., smart cities and intelligent transportation systems) become more commonplace, the Internet of Vehicles (IoV) is getting increasingly prevalent. There have been attempts to utilize Digital Twins (DTs) to facilitate the design, evaluation, and deployment of IoV-based systems, for example by supporting high-fidelity modeling, real-time monitoring, and advanced predictive capabilities. However, the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied topic. In addition, this paper explains how DTs can benefit IoV system designers and implementers, as well as describes several challenges and opportunities for future researchers.

With the prevalence of the Internet of Things (IoT) systems, smart cities comprise complex networks, including sensors, actuators, appliances, and cyber services. The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks, especially privacy-related attacks such as inference and data poisoning ones. Federated Learning (FL) has been regarded as a hopeful method to enable distributed learning with privacy-preserved intelligence in IoT applications. Even though the significance of developing privacy-preserving FL has drawn as a great research interest, the current research only concentrates on FL with independent identically distributed (i.i.d) data and few studies have addressed the non-i. i.d setting. FL is known to be vulnerable to Generative Adversarial Network (GAN) attacks, where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors. This paper proposes an innovative Privacy Protection-based Federated Deep Learning (PP-FDL) framework, which accomplishes data protection against privacy-related GAN attacks, along with high classification rates from non-i. i.d data. PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other, where class probabilities are protected utilizing a private identifier generated for each class. The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets. The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8% as accuracy improvements.