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.

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.

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.