Blockchain-enabled mobile edge computing (MEC) must jointly optimizetask offloadingandconsensus finalityunder highly heterogeneous AIoT devices, where latency/energy constraints and fairness-sensitive incentives coexist with time-varying validator reliability. We proposeFE-CTDE, a unified framework that couples (1) a Stackelberg pricing-and-allocation layer that reaches a unique equilibrium and reduces utility disparity, (2) a reliability-aware dynamic BFT committee and block-packing mechanism that stabilizes confirmation delay under intermittent connectivity, and (3) a centralized-training/decentralized-execution multi-agent policy that outputs a continuous offloading ratio while requiring only local observations at run time. Extensive simulations across diverse heterogeneity, workload burstiness, and link intermittency show that FE-CTDE consistently improves social welfare and fairness while reducing end-to-end latency/energy and sustaining highereffectiveconsensus throughput, outperforming strong baselines by up to22.23%. We further report protocol/learning overheads and provide reproducible implementation details.

The convergence of federated learning (FL) and blockchain in edge-end-cloud systems offers promising opportunities for privacy-preserving collaborative intelligence. However, existing blockchain-enhanced FL (BFL) approaches remain vulnerable to malicious participants and lack robust protection for model updates. To address these issues, we propose SEPP-FLBC, a Secure and Efficient Privacy Protection framework based on Federated Learning and Blockchain Committees. SEPP-FLBC introduces a novel blockchain committee consensus mechanism to validate model updates and defend against unreliable nodes. It further employs a refined multi-party communication paradigm to facilitate indirect and secure data interactions, reducing the risk of information leakage. Additionally, differential privacy noise is applied to model updates to enhance resistance to inference attacks. A formal convergence analysis is conducted to ensure model stability and minimize overhead. Extensive experiments on benchmark datasets demonstrate that SEPP-FLBC achieves superior accuracy while maintaining strong privacy guarantees and communication efficiency, outperforming state-of-the-art BFL methods in both security and performance.

Blockchain technology underpins the value internet, yet the isolation of blockchain systems creates "data and value islands," limiting interoperability. To address this challenge, we propose a scalable and secure cross-chain framework that eliminates reliance on relay chains and enhances performance through decentralized execution and state sharding. Each business blockchain operates as an autonomous node, collaboratively maintaining a virtual cross-chain transaction blockchain. Our framework achieves significant improvements: when state operations are executed on-chain, the framework(with a block size of 2) delivers 66% and 149% higher transactions/s compared to TCIP and BitXhub, respectively, with 20% lower latency. Furthermore, off-chain execution(with a block size of 2) boosts maximum transactions/s by 63% and reduces latency by 36%. The introduction of state sharding enhances parallel transaction execution(with a block size of 128 and a number of sharding nodes of 16), achieving an additional 108% performance improvement. This work demonstrates a novel approach to cross-chain interoperability, offering a secure, efficient, and scalable solution for complex blockchain ecosystems.

While directed acyclic graph (DAG) blockchain technologies improve scalability and throughput over traditional blockchains, they still face critical challenges, particularly in efficiently determining transaction order and ensuring verifiable transaction selection. These limitations significantly hinder their applicability in high-demand environments like IoT, where secure, high-throughput processing is essential. To address these issues, we propose a DAG-partitioned multichain architecture (DPMA), which generates distinct chains for each user node, enabling parallel transaction processing. In addition, we introduce a transaction selection algorithm with verifiable random function (TSAV), which implements a verifiable two-tier transaction selection process, ensuring secure and transparent transaction ordering. To further enhance security, we propose a dynamic transaction confidence analysis method that adjusts VRF parameters in response to network conditions. Experimental results demonstrate that our approach effectively identifies malicious behavior and improves transaction credibility. Compared to existing methods, our solution offers enhanced scalability and security, making it well-suited for large-scale decentralized applications.

Decentralized applications (Dapps), based on smart contract technology, have been increasingly applied in various fields such as healthcare, industrial IoT, agriculture, financial services, supply chain management, and insurance. In certain complex business scenarios, blockchain may require machine learning models to assist contract business. However, on-chain computations are often costly and slow, and there are limitations on contract size. Due to the transparency of on-chain data, there are also privacy concerns regarding user data during model training and inference. To address these challenges, we propose a trusted off-chain machine learning solution that integrates ZK-SNARK and Oracle technologies. Following the principle of “off-chain computation, on-chain verification”,our approach leverages ZK-SNARK to delegate the computation tasks of machine learning models to a trusted environment under the Oracle off-chain server. This solution significantly reduces the computational costs of the blockchain. User data and models are executed off-chain, effectively safeguarding user privacy. The execution results generate zero-knowledge proofs returned for on-chain verification. We have implemented TOMLS-ZKSO and conducted relevant experiments on insurance contract business on the Chainmaker. Experimental results demonstrate the effectiveness of our approach, with model proof generation taking approximately 0.4 seconds and Dapp response time around 0.52 seconds.

In consortium blockchain, full nodes face challenges such as increased storage space usage, higher storage costs, and longer query times due to the growing amount of data. In this paper, we propose a secure and flexible solution to address these issues by transferring old blockchain data to a trusted storage system. First, we introduce a full-node data transfer scheme that packages and stores block data off-chain to tackle insufficient node storage space. Second, we combine blockchain with Transparent Data Encryption (TDE), which stores the data in ciphertext to ensure the security of off-chain data. Additionally, in order to restore the initial state of the node, we add the data recovery function. Experimental results demonstrate that this scheme can save 60% to 70% of node storage space, improve query efficiency by 20% to 50%, and ensure data security.

Federated learning(FL) promotes collaborative learning among devices in the Internet of Things (IoT), achieving privacy-preserving data sharing. However, federated learning in IoT faces challenges of insufficient trust and low efficiency. Existing methods have not effectively addressed both problems simultaneously. In this paper, we proposed a blockchain-based trustworthy and efficient federated learning framework. Firstly, we introduced a proof-of-federated-work(PoFW) consensus algorithm, designed specific block and transaction structures to address trust problem. Secondly, to alleviate the low efficiency caused by the heterogeneous environment, we introduced a deep reinforcement learning-based client selection strategy to optimize training efficiency. Extensive experiments validated that our proposed solution established trust among federated learning participants within the IoT environment, simultaneously improving learning efficiency by 1.22× to 2.63× compared to existing solutions.

Aiming at the cross-chain interoperation difficult problem between isomorphic or heterogeneous blockchain systems, we propose a cross-chain mechanism based on the cross-chain smart contract execution framework. Firstly, a cross-chain smart contract execution framework is proposed to weaken the blockchain properties of relay chains. Secondly, a reliable cross-chain scheme based on the Pedersen VSS and VRF is designed for the framework. Lastly, we conduct multiple sets of experiments on the proposed cross-chain mechanism. The experimental results proved that the proposed cross-chain mechanism is able to execute cross-chain transactions efficiently and securely with realistic feasibility. Its cross-chain interoperation TPS is about 8 times higher than that of the traditional relay chain platform BitXHub on average, and about 82% higher than that of TCIP.

Cross-chain technology has emerged to address the challenges of achieving asset and data interoperability between different blockchains. However, existing cross-chain schemes face issues related to transaction privacy leakage. Therefore, we propose a solution to solve the issues. First, we design a cross-chain model based on a relay chain for storing the information generated during the entire cross-chain process on the chain. Second, under this model, a privacy protection scheme and a relay chain key sharing scheme are proposed, employing cryptographic techniques to safeguard the privacy and security of transaction information and achieving an auditable function. Based on the above proposed schemes, a system model is designed. Finally, the proposed schemes and models are implemented, performance tests are conducted, and comparisons are made with other relay chain solutions with privacy protection capabilities, along with an analysis of security aspects. The experimental results showed that the proposed schemes and the system model are feasible.

Federated Learning (FL) has become an ideal privacy-preserving learning technique that can train a global model in a collaborative manner while preserving the privacy of local data. Federated learning in the Industrial Internet of Things (IIoT) faces the threat of data poisoning attacks. In this paper, we propose a blockchain-based federated learning framework in IIOT, which can defend against poisoning attacks. Moreover, we propose a robust aggregation algorithm to eliminate poisoned local model from malicious participants during training. Experimental results demonstrate the efficacy of the blockchain-based federated learning framework. When tested on the CIFAR-10 and Fashion-MNIST datasets, the framework achieves higher accuracy compared to the Krum algorithm by 3.57% and 0.84%, respectively.

The proliferation of mobile devices and the increasing demand for portable services have led to a surge in computationally intensive and time-sensitive applications, necessitating efficient computation offloading in Mobile Edge Computing. Existing research often overlooks the distinct response time requirements of different applications and their mutual interference during queuing. This paper addresses these gaps by optimizing offloading strategies with multiple response time constraints, focusing on the queuing impact of various tasks. We model edge servers as M/M/1 queuing systems and develop a set of mathematical models. Using algorithms based on Karush-Kuhn-Tucker conditions, we derive optimal offloading strategies to minimize system power consumption. Our approach significantly reduces system power consumption in practical applications, enhancing resource utilization for service providers. Our work introduces a comprehensive and realistic model of task offloading, considering multiple task types, their distinct average response time requirements, and their mutual interference during queuing. This advancement ensures efficient and effective offloading strategies, addressing the complexity of multiple tasks and varying response time constraints.

Abstract In the era of 5G, billions of terminal devices achieve global interconnection and intercommunication, which leads to the generation of massive data. However, the existing cloud‐based data‐sharing mechanism faces challenges such as sensitive information leakage and data islands, which makes it difficult to achieve secure sharing across domains. In this paper, the authors propose a fine‐grained data‐sharing scheme based on blockchain and ciphertext policy attribute‐based encryption, and design a verifiable outsourced computation method to reduce the computational pressure of end users. Second, the authors comprehensively consider the user's identity privacy and transaction privacy, and propose a multi‐level privacy protection method based on ring signature and garbled bloom filter, which enhance the user's data privacy and availability, and prevent the traceability of requests. Finally, the authors design a set of interconnected smart contracts, and verify that their scheme can achieve secure and efficient data sharing through security analysis and performance testing.

Industrial big data has experienced from data silos due to its high potential value and strong security requirements, making it difficult to share securely across domains. Blockchain-based solutions allow nodes to establish access control to trusted data on unreliable or trustless networks, but still face issues such as inefficient data sharing and leakage of sensitive information. In this paper, we propose a blockchain-based access control scheme for privacy security and dynamic regulation. First, ciphertext policy attribute-based encryption (CP-ABE) is developed to gain fine-grained access to node resources, with verifiable outsourcing decryption method to significantly reduce computational pressure on end users. Second, a policy hiding method based on multi-chain architecture is proposed, which performs double hiding of attribute information and access policy information on the blockchain. Finally, a supervisory policy that incorporates dynamic trust assessment and smart contracts is proposed to achieve effective detection and hierarchical classification punishment of malicious behavior. Security analysis and experimental results show that our scheme can limit the complexity of terminal decryption at a constant level and effectively achieve secure and efficient access control in the industrial Internet environment.

Smart contract is a trusted service provided on the blockchain, while cloud service is a traditional service mode with a large number of resources. The combination of the blockchain and cloud service is of great significance to the trusted expansion of services and the access to services inside and outside the blockchain. In this paper, we study the smart contract extension service in blockchain-cloud collaborative computing. The service module decoupling method of smart contract is proposed, and the parallel execution algorithm of smart contract service is designed, which improves the execution efficiency of smart contract service. Finally, this paper designs a secure data interaction method of smart contract and cloud service, which helps to maintain the data consistency between cloud computing and blockchain. The experimental results show that the proposed method can save at most 42.13% of the running time, and it can promote the data consistency between the cloud service and the blockchain.

The traditional electronic voting (e-voting) has the problems of dead vote, repetition and missing registration, which cannot reflect the voting result correctly, objectively and fairly. With the booming development of blockchain in recent years, blockchain technology provides a new solution in the field of e-voting. Multi-District election is an important method of election in real life, e.g. in US presidential election. Inspired by the electoral college system, we propose a blockchain-based e-voting scheme for multi-district election. First, we propose a blockchain-based voter registration method that all voters are authenticated by the blockchain system. Second, we design a two-layer blockchain architecture, where the lower layer records the votes of voters in each district and the higher layer records the votes of electors. Then we describe the voting process and evaluate the security and availability of the proposed scheme. The experimental results show that the proposed scheme can satisfy the needs of multi-district election.

The state-of-the-art HotStuff operates an efficient pipeline in which a stable leader drives decisions with linear communication. However, with the unifying proposing-voting pattern, it takes two rounds of messages to produce a certified proposal, which severely limits the performance of the consensus protocol and makes it difficult for the blockchain to exert the bandwidth and concurrency of modern operating systems. Thus, this paper developed a new consensus protocol, called Multi-pipeline HotStuff, for permissioned blockchain. To the best of the authors’ knowledge, this is the first protocol that combines multiple pipelines of HotStuff to propose batches in order, such that proposals are built optimistically when a correct replica realizes that the current proposal is valid and will be certified by quorum votes in the near future. Simultaneous proposing and voting allow the protocol to produce more proposals in every two rounds of messages, it further boosts the throughput at a comparable latency with that of HotStuff. The evaluation experiment confirmed that the throughput of the proposed protocol outperformed HotStuff by approximately 60% without significantly increasing end-to-end latency under varying system sizes. Even if the protocol frequently performs view-change phase due to network asynchrony, its optimization continues to demonstrate better performance.

Community discovery can discover the community structure in a network, and it provides consumers with personalized services and information pushing. It plays an important role in promoting the intelligence of the network society. Most community networks have a community structure whose vertices are gathered into groups which is significant for network data mining and identification. Existing community detection methods explore the original network topology, but they do not make the full use of the inherent semantic information on nodes, e.g., node attributes. To solve the problem, we explore networks by considering both the original network topology and inherent community structures. In this paper, we propose a novel nonnegative matrix factorization (NMF) model that is divided into two parts, the community structure matrix and the node attribute matrix, and we present a matrix updating method to deal with the nonnegative matrix factorization optimization problem. NMF can achieve large‐scale multidimensional data reduction processing to discover the internal relationships between networks and find the degree of network association. The community structure matrix that we proposed provides more information about the network structure by considering the relationships between nodes that connect directly or share similar neighboring nodes. The use of node attributes provides a semantic interpretation for the community structure. We conduct experiments on attributed graph datasets with overlapping and nonoverlapping communities. The results of the experiments show that the performances of the F1‐Score and Jaccard‐Similarity in the overlapping community and the performances of normalized mutual information (NMI) and accuracy (AC) in the nonoverlapping community are significantly improved. Our proposed model achieves significant improvements in terms of its accuracy and relevance compared with the state‐of‐the‐art approaches.

Wireless Sensor Networks (WSN) interconnects thousands of sensor nodes to support the services of Internet of Things (loT). However, data collected from sensor nodes may be tempered, forged and divulged. Traditional WSN data process platform handle the data centralized in terminal devices which is vulnerable to attacks and reduce the security of the system. Blockchain is a kind of distributed databases. It has been successfully applied in finance, securities, digital currency and other fields. We first propose a blockchain-based distributed collocation storage architecture for data security process platform of WSN with consensus protocol and asymmetric signature scheme. The security and efficiency are verified on the simulation to enable blockchain as a good solution.

rCOS, the abbreviation of Refinement Calculus for Object Systems, is designed to present mathematical characterization of essential object-oriented concepts for an object-based language with a rich variety of features including subtypes, inheritance, type casting, dynamic binding and polymorphism. This paper represents an operational semantics for the rCOS language based on labeled transition systems. The result semantics shows the process of how the effects of an rCOS program are produced. It can be a secure guide for the implementation of the rCOS language, which is being carried out by our group. For the purpose of extending verifiability and functionality, a set of auxiliary language features is introduced to the rCOS language. Concurrent execution structure is designed to specify multi-threaded programs. Also the simulation is introduced to specify the observable behaviors of objects, and it can be regarded as the refinement relation defined in denotational domain to some extent.

In this paper, we give a bigraphical model for web services composition. We investigate how to represent scope-based compensation handing mechanism by means of Bigraphical Reactive Systems (13RSs for short), which have been proposed to provide a uniform way to model spatially distributed systems that both compute and communicate. The service composition language we focus on is WSBPEL, which is the standard of web service composition and orchestration. This bigraphical model can be regarded as a unifying semantics of BPEL-like languages with the key concepts related to compensation handling. The rationality of the model is discussed by investigating the relationship between BPEL language and BRSs. Based on the bigraphical model, the algebraic laws for BPEL are proved as well.