Edge intelligence is a critical enabler of intelligent application services in the Internet of Things (IoT). However, due to complex environmental factors, edge devices are subject to constant dynamic changes, which can result in security threats and sensitive information leakage. Therefore, it is essential to investigate data stream online analysis and detection strategies and implement an online releasing mechanism to ensure sensitive information is not leaked. Existing work rarely addresses these issues simultaneously or has poor performance, which poses a challenge. To address this challenge, we propose an intelligent edge dual-structure ensemble method (IEDSEM), consisting of three key components: 1) data preprocessing; 2) drift detection data analytics (IEDSEM-DDDA); and 3) privacy-preserving data releasing (IEDSEM-PPDR). Data preprocessing is used primarily to enhance the quality of data streams to improve the performance of model learning. IEDSEM-DDDA involves three sequential operations: 1) dynamic feature selection; 2) model learning and selection and 3) online model ensemble deployment to achieve anomaly detection of online data streams. Meanwhile, IEDSEM-PPDR uses differential privacy and online optimization operations to achieve intelligent hierarchical protection of edge data. To validate the performance of our proposed IEDSEM method, we conducted two comprehensive simulation experiments on real data machines, verifying the accuracy of the concept drift component detection and the privacy optimization performance of the privacy-preserving component, respectively. Simulation results show that compared with several other advanced high-performance algorithms, our algorithm has over 99% accuracy in data stream analysis detection and more outstanding privacy-preserving ability.

The dramatically increased wireless communication needs make non-renewable spectrum resources extremely scarce and costly. Consortium blockchain realizes trusted spectrum sharing among untrusted spectrum owners. Yet, most existing studies ignore spectrum sharing among blockchains, which greatly reduce spectrum utilization. In the paper, we focus on cross-chain spectrum sharing. We propose a Fine-grained Cross-chain Spectrum Sharing mechanism based on Oracle (FCSSO) to realize trusted and efficient cross-chain spectrum transactions. To guarantee benefits of spectrum owners, we design a fine-grained time partition method to decide spectrum renting time in transactions. The method reduces the waste of owners' available spectrum time caused by spectrum handoff. Extensive simulation proves the positive impact of the proposed fine-grained time partition method, and FCSSO outperforms two representative cross-chain mechanisms from two aspects: spectrum owners' benefits and spectrum utilization.

With the development of the Internet, the technical framework that integrates Internet of Things (IoT) and blockchain has gradually developed. However, how to ensure the secure with balanced performance of devices becomes a key issue. In this article, we design a new multistage session key negotiation protocols for the Internet of Things-blockchain environment by using “Agents.” We transfer bilinear operations to Agents, which has strong computing capabilities. Based on this, the resource of IoT devices in the key negotiation process is reduced without simplifying bilinear pairing compute and making them more secure. The simplified BPR model and the random oracle model ID-BJM are used to test the security of requirements in different stages of the scheme, furthermore, theoretically analyze its performance overhead. This protocol can reduce the computing overhead of IoT devices in the key negotiation process under the premise of ensuring security.

The rapid development of digital technology is reshaping the architecture of the Industrial Internet of Things (IIoT). The traditional architecture cannot process vast amounts of data exchanges and provide entities with trust. The future IIoT is expected to be a decentralized architecture in which blockchain and digital twin-driven IIoT can enable trusted data exchanges. However, this architecture cannot obtain huge amounts of external real-time data and isolated data. Moreover, it cannot handle complex industrial computing tasks. Therefore, we combine oracle with decentralized learning to propose a novel IIoT-oriented digital twin architecture. We also propose an effective decentralized collaboration mechanism to support external data and resources exchanges. Moreover, we propose a novel computing collaboration mechanism to expand the learning capabilities of the industrial ecology. Experiments show that our proposed paradigm has less processing time, a more stable process, and better learning ability compared to other paradigms.

Federated learning (FL) lately has shown much promise in improving the shared model and preserving data privacy. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. In this article, we propose a novel federated unsupervised learning method for image classification without the use of any ground truth annotations. In IoT scenarios, a big challenge is that decentralized data among multiple clients is normally nonindependent and identically distributed (non-IID), leading to performance degradation. To address this issue, we further propose a dynamic update mechanism that can decide how to update the local model based on weights divergence. Extensive experiments show that our method outperforms all baseline methods by large margins, including +6.67% on CIFAR-10, +5.15% on STL-10, and +8.44% on SVHN in terms of classification accuracy. In particular, we obtain promising results on Mini-ImageNet and COVID-19 data sets and outperform several federated unsupervised learning methods under non-IID settings.

Blockchain-based Federated Learning (FL) technology enables vehicles to make smart decisions, improving vehicular services and enhancing the driving experience through a secure and privacy-preserving manner in Intelligent Transportation Systems (ITS). Many existing works exploit two-layer blockchain-based FL frameworks consisting of a mainchain and subchains for data interactions among intelligent vehicles, which resolve the limited throughput issue of single blockchain-based vehicular networks. However, the existing two-layer frameworks still suffer from a) strong dependency on predetermined and fixed parameters of vehicular blockchains which limit blockchain throughput and reliability; and b) high communication costs incurred by interactions among intelligent vehicles between the mainchain and subchains. To address the above challenges, we first design an adaptive blockchain-enabled FL framework for ITS based on blockchain sharding to facilitate decentralized vehicular data flows among intelligent vehicles. A streamline-based shard transmission mechanism is proposed to ensure communication efficiency almost without compromising the FL accuracy. We further formulate the proposed framework and propose an adaptive sharding mechanism using Deep Reinforcement Learning to automate the selection of parameters of vehicular shards. Numerical results clearly show that the proposed framework and mechanisms achieve adaptive, communication-efficient, credible, and scalable data interactions among intelligent vehicles.

In the Industrial Internet of Things (IIoT), model and computing power sharing among devices can improve resource utilization and work efficiency. However, data privacy and security issues hinder the sharing process. Besides, in the process of model sharing, due to the customization of industrial equipment functions and the high separation of model and task types between devices, it is difficult to share model and optimize models among devices with different task requirements. In this article, we propose an adaptively federated multitask learning (AFL) for IIoT devices efficiently model sharing. Inspired by the parameter sharing mechanism, AFL builds a sparse sharing structure by designing an iterative pruning network and generating subnets for each task. Moreover, for better share relevant information, we further propose tailored task mask layers for effectively training specialized subnets, and an adaptive loss function to dynamically adjust the priority between tasks. Extensive experiments show that AFL can successfully fit hundreds of tasks from different devices into one model, which preserves both high accuracy and system scalability, and outperforms other related approaches that naively combine federated learning with multitask learning.

Blockchain as a new decentralized chain structure can be used in edge computing to solve the security issue caused by edge nodes in model training. However, large amounts of data exchanges in the process of model training of edge computing reduce the performance of blockchain, and meanwhile, the block needed to be saved in the edge node challenges storage capacity of the edge node. Therefore, in the paper we propose a safe and efficient model training mechanism based on onchain and offchain collaboration. In the mechanism, edge nodes train models locally, store the model parameters in offchain and only return identifiers for model aggregation. By the method, the storage pressure of the edge node is reduced and the efficiency of executing consensus algorithms are increased. Moreover, in the mechanism we design a reputation evaluation model based on confidence factors to avoid the uploading of random and wrong data of edge nodes. Evaluation results show that our schemes can reduce the average delay and resources consumption, increase transaction throughput and maintain security compared with a state-of-the-art scheme.

As things currently stand, the blockchain industry is siloed among many different platforms and protocols resulting in various islands of blockchains. Restrictions regarding assets transfers and data migration between different blockchains reduce the usability and comfort of users, and hinder novel developments within the blockchain ecosystem. Interoperability will be the main topics of next-generation blockchain technologies. In this paper, we focus on how to enable interoperability between two heterogeneous blockchains in the context of data migration. We first build an cross-chain data migration architecture based on data migration oracle. Second, we design a data migration mechanism based on former architecture. By employing the proposed data migration architecture, it is equivalent to opening a secure channel between two heterogeneous blockchains allowing secure data migration. By applying data migration mechanism, the confidentiality, integrity and security of migrated data can be well guaranteed.

To alleviate the security concerns caused by the openness of the edge computing network and meet the time-sensitive requirements of the edge devices' collaborative tasks, an effective trust evaluation mechanism is needed urgently to resist multi-attacks from various malicious devices. In this work, a light- weight trust mechanism based on multi-source feedback is proposed for edge computing. First, we design a light-weight data-processing algorithm executed in edge brokers and edge devices, which could reduce the data transmission pressure in communication networks effectively and work efficiently in large-scale edge networks. Then, a comprehensive evaluation method is designed for edge brokers based on the Dempster Shafer theory and multi-source feedback mechanism, which makes our mechanism more reliable and pluralistic when resisting various multi-attacks at the same time. At last, we originally develop a neural network in the centralized cloud to update edge brokers' hyper-parameters and weights of the key factors by auditing trust evaluation results uploaded from the edge network according to deep Q-learning algorithm, which are usually weighted manually and subjectively in traditional schemes. The experimental results show the proposed trust mechanism outperforms existing methods in reliability and calculation efficiency when resisting various malicious attacks.

With the development of the edge computing and cloud computing technology, the cloud-edge collaboration framework is proposed as a new effective computing architecture and applied in many fields. However, due to the openness of the edge networks, the security of cloud-edge framework is an unavoidable problem and most recent trust mechanism could not resist mixed malicious attacks at the same time. In this work, a light-weight and reliable trust mechanism based on the improved LightGBM algorithm is originally proposed to evaluate the credibility of edge devices. First, we design a light-weight trust mechanism for edge devices to process raw interaction data and extract trust features, which reduces the amount of data transmission and the pressure on the communication networks. In addition, an evaluation algorithm based on the entropy weight method (EWM) and punishment factors is designed for edge brokers to distinguish the malicious devices from the normal ones, which performs great against mixed malicious attacks. At last, we propose an improved LightGBM algorithm developed in the centralized cloud to learn other researchers' evaluation methods and check the evaluation uploaded from edge brokers, which could make the punishment factors of edge networks weighted adaptively with the change of edge networks. The experimental results show the proposed trust mechanism outperforms existing methods in the accuracy and discriminating speed under mixed malicious attacks.

Task offloading has been a hot topic in the field of edge computing. Resources fairness of edge computing servers which is the destination of task offloading directly impacts life of server and the process quality of task. In this paper, we propose a subtask-virtual machine mapping model (subtask-VM mapping model) to complete task offloading from the terminals to the servers. Considering the reasonable allocation of server resources, we also propose stack-based cache mechanism (SCM) to ensure the fairness of server resources allocation. We transform the problem of mapping model solution into the problem of optimal matching in the bipartite graph, and verify the performance of our algorithm by contrast experiment. In particular, the fair performance of our algorithm for server-side is over 84%.

To solve the issue of scarce spectrum resources of the existing cellular network, LTE-U that expands LTE service to the unlicensed 5GHz spectrum is proposed. However, the centralized medium access control protocol of LTE largely decreases the performance of Wi-Fi networks operating in the same unlicensed spectrum. In the paper, to solve the problem, we propose a new mechanism based on the duty-cycle method. It can adaptively adjust the percentage of the airtime used by a LTE Small cell Base Station (SBS) according to the bandwidth of the licensed spectrum of the SBS and downlink data rate demands of the SBS users to maximize throughput of the SBS network on the unlicensed spectrum while ensuring fairness between the Wi-Fi and SBS network. The fairness is based on 3GPP proposed fairness coexistence criterion. To ensure the fairness, we propose a method to construct a W-Fi network offering the same level of the SBS traffic load, and throughput maximization of the SBS is formulated as a constrained non-linear optimization problem solved by an optimal algorithm. We evaluate the proposed mechanism from two aspects. The first is to prove the proposed Wi-Fi network construction method is valid. The second is to evaluate the performance of our proposed method. Simulation results show that our approach is valid and it can maximize the throughput of the SBS network and ensure the fairness criterion.

Mobile edge computing is a promising paradigm to compensate for the lack of traditional cloud computing, which has a variety of application scenarios. However, the migration of user-generated data in edge networks is a key issue which involves in transmission costs, the mobility of users, transmission resources, etc. In this paper, we focus on migrating deadline-varying user-generated data to edge servers, considering the tasks characteristics and contact patterns between nodes. We design a heuristic algorithm and propose the online algorithm using real-time information to save the cost of transmission. Further, we conduct the extensive simulations to demonstrate the effectiveness of our algorithms.

Opportunistic offloading can be utilized to offload computing tasks and traffic data in Mobile Edge Computing (MEC). To improve the ratio of successful data offloading and reduce unnecessary data redundancy in opportunistic forwarding process, some methods of evaluating a device’s forwarding capability are proposed. However, most of these methods do not consider the temporal impact from device mobility and the efficiency influence from the capability computation process. To settle these problems, we proposed a Transient‐cluster‐based Capability Evaluation Method (TCEM) to evaluate a device’s data forwarding capability. The TCEM can be divided into two steps. The first step aims to reduce computational complexity by evaluating a device’s possibility of contacting the destination within a time constraint based on the transient cluster generated by our proposed Transient Cluster Detection Method (TCDM). The second step is to calculate a device’s probability of directly and indirectly forwarding data to the destination. The probability as a metric of evaluating a device’s forwarding capability can be used in different data forwarding strategies. Simulation results demonstrate that the TCEM‐based data forwarding strategy outperforms other data forwarding strategies from the aspect of the proportion of the data delivery ratio to the data redundancy.