Blockchain has been utilized to manage distributed multicloud storage in the industrial Internet of Things. Existing approaches commonly use trusted third-party servers or middlewares to search data allocation strategies and use blockchain to enhance security. However, finding a fair data allocation strategy is hard when the third-party brokers are manipulated. Moreover, the complex computing in generating blocks reduces efficiency and heavy communication cost in consensus leads to critical challenges to scalability. To address that, this article proposes a scalable two-layer blockchain system for distributed multi-cloud storage (STSM). We design a novel consensus mechanism called proof of storage allocation, which integrates data placement problems into leader selection to achieve fair strategy and high QoS of data storage. We also incorporate asynchronous consensus groups into the consensus process to enhance scalability. Extensive experiments verify that STSM gains high scalability and increases efficiency while achieving high QoS in distributed multicloud data allocation.

Blockchain, which stores data in an appending form, cannot achieve the purpose of expanding the storage capacity by increasing the number of nodes. As the system runs, nodes will face problems of insufficient storage space. In the existing peer-to-peer (P2P) blockchain network model, all network nodes participate in data storage, and the generated blocks need to be verified among the network-wide nodes. This approach suffers from low system transaction throughput and data storage redundancy. In order to solve the above existing problems, this article proposes a block data storage model based on the double-layer blockchain network (DLBN), which improves the internal data composition structure of the blockchain. The DLBN contains two types of blockchain nodes, which form the storage and consensus layers of the system, respectively. The consensus layer is responsible for tasks, such as transaction sequencing, validation, and block packing, thus increasing the system transaction throughput. The nodes in the storage layer are divided into multiple storage units (SUs), and all nodes in the SU jointly maintain a copy of the complete blockchain, thereby reducing the storage pressure on the nodes. Based on the DLBN model, we design a reputation-based consensus mechanism, block storage allocation algorithm, and transaction query optimization algorithm, respectively. Through experimental verification and analysis, the storage model based on the DLBN can effectively improve the system transaction throughput and reduce the node storage capacity while ensuring system security.

Smart vehicles can cooperate in teams to perform crowdsensing tasks in smart cities. A critical challenge in this regard is to build a secure model for nondeterministic vehicle teams to achieve maximum social welfare. Although several crowdsensing models have been proposed, none of them has focused on real-time vehicle teamwork. In this study, to the best of our knowledge, we propose the first secure model, called Blockchain-based Nondeterministic Teamwork Cooperation(BNTC), for nondeterministic teamwork cooperation in a vehicular crowdsensing system. We model the system as a multi-conditional NP-complete problem by explicitly considering the dynamic features of task issuers and workers. To solve the problem, we propose Winning Teams Selected(WTS) algorithm based on a reverse auction and utilize a knapsack-based method to solve the models. We consider credit of teams for determining the payment. Thus, we propose a Credit-based Team Payment(CTP) algorithm for BNTC to maximize the welfare of the system. We also propose a general blockchain-based framework to address trust issues and security challenges to make the method suitable for use in practical applications. Based on theoretical analyses and extensive simulations, we demonstrate that the proposed model performs better than the baselines and can achieve the maximum social welfare. Implementation with Ethereum suggests our model can operate within a reasonable cost.

Knowledge representation learning achieves the automatic completion of knowledge graphs (KGs) by embedding entities into continuous low-dimensional vector space. In knowledge graph completion (KGC) tasks, the inter-dependencies and hierarchical information in KGs have gained attention. Existing methods do not well capture the latent dependencies between all components of entities and relations. To address this, we introduce the mathematical theories of octonion, a more expressive generalized form of complex number and quaternion, and propose a translation-based KGC model with octonion (TransO). TransO models entities as octonion coordinate vectors, relations as the combination of octonion component matrices and coordinate vectors, and uses specific grouping calculation rules to interact between entities and relations. In addition, since hyperbolic Poincaré space in non-Euclidean mathematics can represent hierarchical data more accurately and effectively than traditional Euclidean space, we propose a Poincaré-extended TransO model (PTransO). PTransO transforms octonion coordinate vectors into hyperbolic embeddings by exponential mapping, and integrates the Euclidean-based calculations into hyperbolic space by operations such as Möbius addition and hyperbolic distance. The experimental results of link prediction indicate that TransO outperforms other translation-based models on the WN18 benchmark, and PTransO further achieves state-of-the-art performance in low-dimensional space on the well-established WN18RR and FB15k-237 benchmarks.

Sharding is widely regarded as a highly promising solution to address the scalability limitations of blockchain. However, the scalability and throughput improved by using sharding are limited by the verification of cross-shard transactions. To reduce cross-shard transaction and improve the throughput of the blockchain, the existing sharding schemes are based on factors such as the edge-end structure of Industrial Internet of Things (IIoT) for sharding. But these schemes are centralized, leading to the problems of low sharding efficiency, poor scalability, and poor security. Moreover, these schemes adopt a nonoverlapping sharding architecture, so the verification cost of cross-shard transactions is significantly higher than that of intrashard transactions. In order to solve the above problems, this article proposes an overlapping self-organizing sharding scheme (deep reinforcement learning (DRL)-OSS) for large-scale IIoT blockchain. Based on local blockchain information, such as nodes’ information and transaction interaction frequency, DRL-OSS uses DRL to achieve self-organizing sharding with the aim to maximize the throughput and security of blockchain. In addition, based on the threat model, this article also designs a block complaint scheme (BCS) to further improve the security of the blockchain, thereby avoiding the reduction in resistance to 1% attack due to the poor anti-predictability of shards and the dilution of computing power. Through experimental verification and analysis, DRL-OSS improves the throughput by 50% when compared to state-of-the-art sharding schemes and has higher system security.