The Industrial Internet of Things (IIoT) is the essential component of Industry 4.0. Blockchain is a promising technology for secure data sharing and trustable cooperation between IIoT devices. However, the ever-growing transaction records make it difficult for the storage-limited IIoT devices to join the blockchain network. In this article, an adaptive compression scheme is proposed to decrease the storage volume on each node. In the scheme, the block body is compressed by representing the included transactions as their remainders stored in the distributed nodes. The original transaction could be recovered based on the Chinese remainder theorem. In particular, each node adapts its compression ratio according to its storage resource. The nodes storing more data have advantages in transaction recovery, introducing an incentive mechanism for efficient storage utilization. The theoretical analysis and simulation results show that the proposed scheme can achieve a high compression ratio with good service availability. The proposed scheme dramatically lowers the threshold for IIoT devices to join the blockchain network, which is important for the large-scale application of blockchain in Industry 4.0.

Blockchain has a broad development potential and application prospects. At present, huge storage volume on each node has become one of the primary bottlenecks that restrict the expansibility of blockchain. Optimization for storage mechanism has become a necessary way to accelerate the improvement of blockchain. There are a variety of solutions to relieve the storage burden by modifying the architecture of blockchain, but most of them weaken the decentralization property of the system, which is the core advantages of the blockchain. In this paper, an optimization scheme based on the redundant residual number system is proposed to dramatically reduce the storage volume of nodes in the blockchain system, and fault tolerance mechanism is designed based on the new storage scheme. The simulation experiments are performed to prove the effectiveness of the proposed mechanism.

Huge storage volume is one of the main bottlenecks for the development of blockchain, so how to release the burden by optimizing the storage mechanism has become an important problem. Most of the current solutions would modify the architecture of blockchain, which weakens the characteristics of the decentralization, such as cloud storage. In this paper, a storage optimization mechanism based on residual number system is proposed to reduce the storage volume on each node. In addition, the recovery procedure of CRT-II (The new Chinese Remainder Theorem) is used to detect garbled data from devil nodes, which enable the proposed storage mechanism with strong fault tolerance capability. Both theoretical analysis and simulation results prove the effectiveness and reliability of the proposed scheme.

Federated learning is a privacy-preserving machine learning framework where multiple data owners collaborate to train a global model under the orchestra of a central server. The local training results from trainers should be submitted to the central server for model aggregation and update. Busy central server and malicious trainers can introduce the issues of a single point of failure and model poisoning attacks. To address the above issues, the trusty decentralized federated learning (called TrustDFL) framework has been proposed in this paper based on the zero-knowledge proof scheme, blockchain, and smart contracts, which provides enhanced security and higher efficiency for model aggregation. Specifically, Groth 16 is applied to generate the proof for the local model training, including the forward and backward propagation processes. The proofs are attached as the payloads to the transactions, which are broadcast into the blockchain network and executed by the miners. With the support of smart contracts, the contributions of the trainers could be verified automatically under the economic incentive, where the blockchain records all exchanged data as the trust anchor in multi-party scenarios. In addition, IPFS (InterPlanetary File System) is introduced to alleviate the storage and communication overhead brought by local and global models. The theoretical analysis and estimation results show that the TrustDFL efficiently avoids model poisoning attacks without leaking the local secrets, ensuring the global model’s accuracy to be trained.

The blockchain-based Internet of Things (IoT) has served widely across various industries for authentication, cooperation, and data sharing but faced the severe challenge of storage scalability. The storage burden gets worse for IoT devices with limited resources. The state data is essential for efficient transaction issuance and validation. This article proposes the Verkle accumulator-based stateless transaction validation (VA-STV) scheme for permissionless blockchains to decrease the storage burden of the state data on each node with the acceptable overhead of computation and communication. In the scheme, the current state is summarized as the commitment maintained in the latest block header, and one witness is generated for each token to guarantee its validity. State transitions are realized by updating the commitment and witnesses so that no state is stored on nodes acting as validators and miners. Only the nodes acting as traders should maintain the tokens controlled by themselves and the witnesses locally. The VA-STV is based on the Verkle accumulator (VA), which is a combination of the Verkle tree (VT) and the KZG polynomial commitment scheme. Simulation results show that the VA-STV provides a smaller witness size ( $0.6\times $ – $0.74\times $ ) and faster commitment generation ( $6\times $ – $14\times$ ) than the existing stateless schemes in the same settings, which indicates the advantages of VA-STV in succinctness and efficiency. Besides, a tradeoff between the communication and computation requirements can be achieved by adjusting the branching factor, which improves the adaptability of the proposed scheme for different IoT scenarios.

As the distributed ledger technology underlying cryptocurrencies such as Bitcoin and Ethereum, blockchain has empowered various industries, such as supply chain management, healthcare, government services, e-voting, etc. However, the ever-growing ledger on each node has been the main bottleneck for blockchain scalability as the network scale expands, which worsens in blockchain-enabled IoT scenarios with resource-limited devices. With the support of the Reed–Solomon (RS) code, the Cuckoo-Store (CS), a ledger storage optimization engine, is proposed in this paper to dramatically decrease the storage burden on each node by encoding the ledger as data segments with redundancy and distributing them to multiple nodes. These distributed data segments can be collected and decoded using RS code to recover the original ledger. Furthermore, the Cuckoo filter (CF) is used to guarantee the integrity of the encoded segments, which helps detect the forged segments and facilitates the process of ledger recovery. Theoretical analysis and simulation results show that the CS engine can decrease the storage in each node by more than 94%, and the original ledger can be recovered efficiently with acceptable communication overheads.