The proliferation of devices and data in the Industrial Internet of Things (IIoT) has rendered the traditional centralized cloud model unable to meet the stringent requirements of wide-scale and low latency in these IIoT scenarios. As emerging technologies, edge computing enables real-time processing and analysis on devices situated closer to the data source while reducing bandwidth requirements. Blockchain, being decentralized, could enhance data security. Therefore, edge computing and blockchain are integrated in IIoT to reduce latency and improve security. However, the inefficient data transmission of blockchain leads to increased transmission latency in the IIoT. To address this issue, we propose a cluster-based data transmission strategy (CDTS) for blockchain network. Initially, an improved weighted label propagation algorithm (WLPA) is proposed for clustering blockchain nodes. Subsequently, a spanning tree topology construction (STTC) is designed to simplify the blockchain network topology, based on the above node clustering results. Additionally, leveraging clustered nodes and tree topology, we propose a data transmission strategy to speed up data transmission. Simulation experiments show that CDTS effectively reduces data transmission time and better supports large-scale IIoT scenarios.

In the context of smart transportation, the collaborative processing of video data sourced from multiple cameras plays a pivotal role in promoting efficient traffic management and augmenting safety measures. Nevertheless, the exponential surge in surveillance cameras deployment has concurrently engendered a rapid increase in the magnitude of video analysis tasks and data volume. To address these challenges, we propose a comprehensive framework for collaborative video processing. Primarily, a collaborative content analysis approach is proposed, and which employs a Transformer-based ReID (Re-identification) algorithm to construct key stickers. These key stickers are optimized with cross-cameras correlations and serve as the foundational structure for subsequent online video compression. Subsequently, we propose a collaborative resource allocation approach, and which involves the formulation of a queue model designed for the orchestration of online camera analysis tasks. In addition, we have devised an enhanced deep reinforcement learning algorithm to fine-tune the task scheduling configuration of multiple cameras, with guidance from the queue model. Extensive experiments and simulations were conducted to evaluate the proposed framework. The results demonstrate its effectiveness in achieving accurate and real-time analysis of video data in smart transportation scenarios.

With the continuous innovation of network technology, emerging network service models have begun to be proposed, which also puts forward higher requirements for business settlement. Business settlement, as the foundation of network services, involves the interests of users and service providers. The traditional centralized settlement schemes no longer respond to the needs of both parties in terms of transparency, security, and fairness. Therefore, some blockchain-based settlement solutions have been proposed to address these issues. However, due to the additional overhead brought by blockchain, it is difficult for these solutions to ensure both security and efficiency. In this paper, we propose a state channels-based business settlement scheme (SCBS) to complete network service settlement securely and efficiently. In SCBS, the settlement process can be effectively carried out off-chain. When non-cooperative behavior occurs, both parties can create, resolve, and refute the dispute through the state channel to assure safety. Finally, the test results from Hyperledger Fabric platform demonstrate the feasibility and effectiveness of our SCBS.

As the Internet of Things gathers pace and popularity, more and more data is collected at the edge. To unleash the value of data and make it tradable, data markets have been proposed. However, existing data markets generally depend on broker or blockchain, which inevitably raises concerns about one or more aspects of fairness, security, or efficiency. In addition, to promote data trading in the data market, a data trading incentive mechanism is also essential. In this paper, we propose a novel data trading scheme based on state channels and Stackelberg game. First, we propose aStateChannels-basedDataTrading (SCDT) framework to support non-repudiable and efficient data trading. The framework can arbitrate disputes arising from off-chain data trading through state channels, enabling traders to conduct efficient transactions off-chain without worrying about security issues. Second, we propose an optimal incentive mechanism to solve the pricing and purchasing problems. The tripartite interactions among the data seller, resource seller, and user service platform are formulated as a Stackelberg game to maximize the profits of all participants. Finally, we implement the data trading framework and analyze the incentive mechanism, which reveals the feasibility of the framework and the rationality of the incentive mechanism.

Blockchain has been widely used to solve data privacy and security issues in edge computing scenarios. However, the blockchain based on edge computing still has some performance problems, such as insufficient scalability, difficulty in balancing security and edge device power consumption, and inability to simultaneously meet low latency, high throughput, high security and privacy issues, etc. In order to solve these problems, this paper proposes a generally improved Byzantine consensus mechanism based on the K-medoids clustering algorithm - FIBFT. Considering the different performance characteristics of each node in the network, the node’s state is first abstracted into a multi-dimensional state space containing eigenvalues, and then the nodes are divided into subnets by the efficient K-medoids clustering algorithm. Each subnet uses a Byzantine consensus mechanism based on arbitration for consensus and data interaction, and the consensus data could be exchanged between the subnets without interfering with the consensus process. The research results show that FIBFT has better scalability and throughput while ensuring high security compared with the traditional Byzantine consensus algorithm.

With the development of edge computing technology, the resource-limited Internet of Things (IoT) devices can offload computation-intensive artificial intelligence tasks, such as model training and inference to edge servers through resource trading. However, due to the increase in the number of intelligent applications and the rise of peer-to-peer (P2P) resource trading, the existing resource trading schemes based on the blockchain can no longer meet the needs of efficiency and security at the same time. In this article, a new state channels-based resource trading scheme is proposed for IoT, which can improve scalability without sacrificing security and fairness. In our scheme, most of the trading process could be completed off-chain, and the blockchain is used as an adjudicator to determine malicious behavior according to the users’ actions. Moreover, a method without being reliant on support from third parties is presented to defend against execution forks that must be faced when using the state channels. Finally, the feasibility and efficiency of our scheme are experimentally verified in the realistic testbed.

In recent years, with the development and widespread use of blockchain, many projects have introduced blockchain technology to solve the increasingly serious security problems of the Industrial Internet of Things (IIoT). However, due to the conflict between the operational performance and security of the blockchain system, the conflict between transparency and privacy, and the compatibility issues with a large number of IIoT devices running together, the mainstream blockchain system cannot be applied to IIoT scenarios. In order to solve these problems, in this article, we propose an IIoT distributed data system based on blockchain technology. We provide a novel system architecture for different IIoT devices to deploy high-performance blockchain systems in many scenarios, such as smart factory networks. To improve the performance of the blockchain network, we adopt the sharding hashgraph consensus mechanism and introduce a node evaluation mechanism based on the state of the node, which is applied to divide a large number of nodes into many shards dynamically. We abstract the node sharding problem as a joint optimization problem and use deep reinforcement learning to solve it. Finally, we compared with asynchronous Byzantine consensus algorithms, such as HoneybadgerBFT and BEAT, which validated the performance of this system architecture.

In the Industrial Internet of Things (IIoT) scenario, the increase of surveillance equipment brings challenges to the transmission of real-time video. It needs more efficient approaches to finish video transmission with more stability and accuracy. Therefore, we propose a self-adaptive transmission scheme of videos for multi-capture terminals under IIoT in this paper. To fit for the constant variation of network environment, we compress the videos that wait for transmitting from multi-capture terminals by reducing the non-key frames with Graph Convolutional Network (GCN). Moreover, a self-adaptive strategy of transmission is implemented on the Mobile Edge Computing (MEC) server to adjust the transmission volume of processed videos, and a multi-objective optimization algorithm is utilized to optimize the strategy of transmission during the video transmission. The relative experiments are conducted to validate the performance of the proposed scheme.

Named Data Networking (NDN) is a prominent fully- fledged Information-Centric Networking (ICN) architecture. NDN can help users to take advantage of multiple access networks in Heterogeneous Wireless Networks (HWNs) more efficiently than IP. In HWNs with NDN, which we call information-centric HWNs, jointly designing forwarding strategy and resource allocation has great potential to improve network performance, which is ignored in the literatures. To fill in this blank, we propose a jointly designed forwarding strategy and resource allocation algorithm called Dynamic Forwarding and Resource Allocation (DFRA) that can adapt variable wireless environment. We also establish the fundamental throughput limitations of information-centric HWNs and prove that DFRA is throughput-optimal. By the cooperation between forwarding strategy and resource allocation, DFRA enables users to utilize wireless communication resource in information-centric HWNs more efficiently. From simulation results, DFRA can provide larger network throughput, faster download speed and better fairness than forwarding strategy that doesn't explicitly cooperate with resource allocation.