Nowadays, collaborative federated learning (CFL) is developing rapidly in the Internet of Things (IoT), which allows clients to jointly train models without compromising private data. The existing research has studied alone either trust enhancement or privacy preservation issues in CFL. Due to the highly coupled nature of trust and privacy in a collaborative environment, it is worth investigating how to balance appropriate trust and privacy tradeoffs for realizing high-quality CFL. In this paper, we come up with the idea of "trading Trust for Privacy", and propose a novel socially-motivated personalized privacy-preserving federated learning (SP-PFL) framework, which aims to realize social trust-grained privacy protection. First, we design a social trust evaluation method among CFL clients, which is based on topological relation and attribute similarity. Based on the obtained trust value, we then propose a trust-grained privacy budget allocation strategy for SP-PFL, which could further adaptively adjust the differential privacy (DP) noise perturbation. Besides, we provide an analysis of privacy and convergence for our SP-PFL. Finally, we experiment with different models and parameter settings on different datasets. Extensive experimental results show that our method maintains personalized privacy and effectively improves the accuracy by 6.11% on the CNN model and MNIST dataset.

In recent years, the rapid development of the Industrial Internet of Things (IIoT) has enabled real-time communication and data sharing among devices, significantly enhancing industrial production efficiency and security. Furthermore, the introduction of edge learning allows models to be trained on edge industrial devices. However, with the continuous growth of industrial data, challenges such as resource optimization in edge environments, edge node motivation, and threats from malicious nodes are increasingly posing obstacles to the advancement of edge learning. In this paper, we propose Blockchain and Coded Computing based Secure Edge Learning (BCC-SEL). First, we introduce a coded edge learning framework with Lagrange Coded Computing (LCC) for resource-efficient use of idle nodes during training. Based on the blockchain, we further propose an incentive mechanism to reward and punish the participating training clients. Finally, we guarantee the robustness of the framework using the detection method based on cosine-similarity. We provide theoretical proof that our approach effectively reduces the computational consumption of training nodes. In terms of experiments, our method effectively rewards honest nodes that participate in training and penalizes malicious nodes while guaranteeing accuracy.

The existing Artificial Intelligence (AI)-based industrial defect detection methods have received extensive attention in the industrial Internet of Things (IoT). However, due to the limited defect samples, it is difficult for discriminative models to achieve better performance in imbalanced learning. In addition, the privacy concerns surrounding sensitive information hinder the sharing of synthetic industrial data. In this paper, we propose a novel framework called the Differentially Private Generative AI-empowered Digital Twin (DPG-DT) framework, aiming to synthesize realistic samples while satisfying differential privacy and empowering the construction of digital space and its connection with physical space. Specifically, the core of the DPG-DT framework is the proposed Private Synthetic Industry Energy-guided model (PSIE), in which we privatize the energybased model-empowered Langevin Markov Chain Monte Carlo (MCMC) sampling method with Gaussian noise and random response. Our method could replace the conventional generator while guaranteeing privacy. Extensive experiments on real-world industrial datasets NEU-CLS and DeepPCB demonstrate that the proposed framework is capable of generating synthetic industrial images with both high fidelity and differential privacy. Moreover, the achieved downstream accuracy outperforms baselines by 23.9 % in industrial scenarios.

Digital Twin (DT) has become the key technology in the Intelligent Transportation Systems (ITS) in smart cities to keep the health and reliability of various DT requesters, such as private vehicles, public transportation, energy systems, etc. The combination of DT and ITS can further release the potential of participants in smart cities and guarantee their efficiency and reliability. Despite the advantages of DT-enabled ITS, not all requesters need the same level of DT service due to the highly dynamic nature of ITS. Safe and reliable matching between DT and ITS still needs to be resolved. To address these issues, we propose the blockchain-enabled Digital Twin as a Service (DTaaS) for ITS. First, we propose an on-demand DTaaS architecture to fully utilize the sensing capabilities of ITS and the macro perspective of DT. Second, a double-auction model and a price adjustment algorithm are proposed to realize the optimal DT matching for ITS requesters and ensure the benefits of participants. Third, a permissioned blockchain and a novel DT-DPoS consensus mechanism are established to enhance the security and efficiency of DTaaS. Simulation shows that the proposed DTaaS and double-auction can efficiently stimulate and facilitate DT transactions. The proposed DT-DPoS also has obvious advantages.

Currently, the highly dynamic fog computing resource requirements introduced by the diverse services of the Internet of Things (IoT) result in an imbalance between computing resource providers and consumers. However, current computing resource scheduling schemes cannot cognize the dynamic resources available and do not possess decision-making or management capabilities, which leads to inefficient use of computing resources and a decreased quality of service (QoS). Balancing computing resources cognitively at the IoT edge remains unresolved. In this paper, a cognition-centric fog computing resource balancing (CFCRB) scheme is proposed for edge intelligence-enabled IoT. First, we propose a cognitive balance architecture with a cognition plane, which includes service demand monitoring, policy processing and knowledge storage of cognitive fog resources. Second, we propose the fog functions structure with sensing, interaction and learning functionalities, realizing the knowledge-based proactive discovery and dynamic orchestration of resource sharing nodes. Finally, a distributed edge learning algorithm is proposed to construct knowledge of the balance between computing resource helpers and requesters in cognitive fogs, which is further proved with mathematics. The simulation results indicate the efficiency of the proposed scheme.

The development of massive IoT has not only brought about a wealth of hardware resources but also brought about the problems of difficult data management, resource running and low efficiency. The emergence of sixth-generation (6G) network will not only provide faster data rates, more device connections but also bring ubiquitous virtual reality/augmented reality (VR/AR) services. In the 6G era, large-scale IoT devices will generate VR/AR service and resource requirements, and the network will also face unprecedented pressure to respond to the ubiquitous VR/AR requirements. To address the above issues, this article proposes the information-centric massive Internet of Things (IC-mIoT) suitable for 6G large-scale VR/AR content distribution to improve the efficiency of IC-mIoT and fully guarantee the Quality of Service (QoS) of users. First, this article introduces the blockchain for IC-mIoT nodes and proposes a new consensus mechanism Proof-of-Cache-Offloading (PoCO). Second, an architecture using blockchain-enabled IC-mIoT for VR/AR is proposed in this article. The massive IoT resources are fully integrated and scheduled to support large-scale VR/AR applications and IC-mIoT. Third, a Stackelberg game model and a cache index selection and calculation algorithm are formulated for blockchain-enabled cache offloading. The analysis and performance simulation results indicate the superiority and effectiveness of the proposed scheme.

Network edge equipment has generated a large amount of fast- growing data, which has placed a heavy burden on the collaboration of heterogeneous networks. Due to the diversity of edge computing application scenarios, many new requirements are advocated for unified data storage management, such as latency and processing efficiency. Traditional centralized cloud storage can no longer meet the on- demand of edge computing in the case of a surge in data volume. Therefore, a unified storage architecture is required for the current improvements in computational offloading schemes and storage optimization algorithms. To solve these challenges and make data intelligent collaborative storable, this paper proposes a novel unified storage architecture for big data in the edge-cloud, which supports edge services in order to extend Hadoop at the edge. The functions of the edge nodes are proposed to synchronize the edge nodes of the same neighborhood and store data dynamically via Q- learning based on popularity, in order to mitigate network load pressure and improve the efficiency of edge services. An intelligent scheme that impacts the quality of service (QoS) through data marginal storage is proposed to improve the resource scheduling and to the distribution of storage space. Simulation results demonstrate the merits and efficiency of the proposed intelligent architecture is superior to the comparison schemes.

The diverse applications and high-quality services in satellite-enabled smart cities have led to geographical unbalance of computation requirements. Traditional centralized cloud services and massive migration of computing tasks result in the increase of network delay and the aggravation of network congestion. Deploying fog nodes at the network edge has become a way to improve the quality of service (QoS). However, the dynamic requirements and application in various scenarios still challenge the network, resulting in geographical unbalance of computing resource demands. Nowadays, computing resources of on-board computers and devices in the Internet of Vehicles (IoV) are abundant enough to mitigate the geographical unbalances in computing power demand. Efficient usage of the natural mobility of constantly moving vehicles to solve the problems above remains an urgent need. In this paper, vehicle mobility-based geographical migration model of vehicular computing resource is established for satellite-enabled smart cities. By using the road- status-awareness of fog nodes, the status of roads is precisely quantified as the basis for vehicle mobility- based resource migration. An incentive scheme that affects the vehicle path selection through resource pricing is proposed to balance the resource requirements and to geographically allocate computing resources. Simulation results indicate that the advantages and efficiency of the proposed scheme are significant.