Internet of Things (IoT) platforms, particularly smart home platforms providing significant convenience to people’s lives, such as Apple HomeKit and Samsung SmartThings, allow users to create automation rules through trigger-action programming. However, some users may lack the necessary knowledge to formulate automation rules, thus preventing them from fully benefiting from the conveniences offered by smart home technology. To address this, smart home platforms provide predefined automation policies based on the smart home devices registered by the user. Nevertheless, these policies, being pregenerated and relatively simple, fail to adequately cover the diverse needs of users. Furthermore, conflicts may arise between automation rules, and integrating conflict detection into the IoT platform increases the burden on developers. In this article, we propose AutoIoT, an automated IoT platform based on large language models (LLMs) and formal verification techniques, designed to achieve end-to-end automation through device information extraction, LLM-based rule generation, conflict detection, and avoidance. AutoIoT can help users generate conflict-free automation rules and assist developers in generating codes for conflict detection, thereby enhancing their experience. A code adapter has been designed to separate logical reasoning from the syntactic details of code generation, enabling LLMs to generate code for programming languages beyond their training data. Finally, we evaluated the performance of AutoIoT and presented a case study demonstrating how AutoIoT can integrate with existing IoT platforms.

A redactable blockchain allows authorized individuals to remove or replace undesirable content, offering the ability to remove illegal or unwanted information. Access control is a mechanism that limits data visibility and ensures that only authorized users can decrypt and access encrypted information, playing a crucial role in addressing privacy concerns and securing the data stored on a blockchain. Redactability and access control are both essential components when implementing a regulated consortium blockchain in real-world situations to ensure the secure sharing of data while removing undesirable content. We propose a decentralized consortium blockchain system prototype that supports redactability and access control. Through the development of a prototype blockchain system, we investigate the feasibility of combining these approaches and demonstrate that it is possible to implement a redactable blockchain with access control in a consortium blockchain setting.

A metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blockchain, as one of the key supporting technologies for a metaverse design, provides a trusted interactive environment. However, the rich and varied scenes of a metaverse have led to excessive consumption of on-chain resources, raising the threshold for ordinary users to join, thereby losing the human-centered design. Therefore, we propose an adaptive and modular blockchain-enabled architecture for a decentralized metaverse to address these issues. The solution includes an adaptive consensus/ledger protocol based on a modular blockchain, which can effectively adapt to the ever-changing scenarios of the metaverse, reduce resource consumption, and provide a secure and reliable interactive environment. In addition, we propose the concept of Non-Fungible Resource (NFR) to virtualize idle resources. Users can establish a temporary trusted environment and rent others’ NFR to meet their computing needs. Finally, we simulate and test our solution based on XuperChain, and the experimental results prove the feasibility of our design.

Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is a practical zero-knowledge proof system for Rank-1 Constraint Satisfaction (R1CS), enabling privacy preservation and addressing the previous scalability concerns on zero-knowledge proofs. Existing constructions of zk-SNARKs require huge memory overhead to generate proofs in that the size of the zk-SNARK circuit can be large even for a very simple use case, which limits the applications for regular resource-constrained users. To reduce the memory utilization of zk-SNARKs, this paper presents a hash-based method “Split”. Concretely, Split intends to partition the zk-SNARK circuits so that components can be processed sequentially while ensuring strong security properties leveraging hash circuits. As a zk-SNARK circuit is partitioned, obsolete variables are no longer preserved in the memory. We further propose an enhanced Split as$n$-Split, which leads to better optimization by properly choosing multiple splits. Our experimental results validate the effectiveness and efficiency of Split in conserving memory usage for resource-constrained provers as long as the circuit can be partitioned to a Good Split, indicating that via Split zk-SNARKs can be brought one step closer to practical applications.

Decentralized learning involves training machine learning models over remote mobile devices, edge servers, or cloud servers while keeping data localized. Even though many studies have shown the feasibility of preserving privacy, enhancing training performance or introducing Byzantine resilience, but none of them simultaneously considers all of them. Therefore we face the following problem:how can we efficiently coordinate the decentralized learning process while simultaneously maintaining learning security and data privacy for the entire system?To address this issue, in this paper we propose SPDL, a blockchain-secured and privacy-preserving decentralized learning system. SPDL integrates blockchain, Byzantine Fault-Tolerant (BFT) consensus, BFT Gradients Aggregation Rule (GAR), and differential privacy seamlessly into one system, ensuring efficient machine learning while maintaining data privacy, Byzantine fault tolerance, transparency, and traceability. To validate our approach, we provide rigorous analysis on convergence and regret in the presence of Byzantine nodes. We also build a SPDL prototype and conduct extensive experiments to demonstrate that SPDL is effective and efficient with strong security and privacy guarantees.

The rapid development of information technology and Internet makes the sports information resources retrieval service more convenient and quick; sports policy in recent years lays a foundation for the development of the Internet + sports, the development of sports industry in the process of our country economy level of development status, and the development of sports industry into the era of information and big data. This paper takes OpenStack cloud platform as the research basis (1) to realize the sharing of sports industry information resources in OpenStack cloud technology and (2) to realize big data analysis of sports industry and (3) empirical research on big data of sports industry. The main content is to realize the construction of sports resources informatization based on the OpenStack cloud platform. Through the analysis and empirical study of the big data of the sports industry, the influence of the development of the sports industry in the process of China’s economic development is discussed. In this paper, the experimental results show that the sports industry showed a positive impact in the process of economic development, the sports economy for the development of the economy, the contribution rate reached 11.77%, the sports industry for the development of the economy, the pull rate of 1.056%, based on the cloud platform of information resources sharing of data analysis, sports industry for the development of the economy has a positive role in promoting.

This paper proposes the random error analysis method for stochastic computation based on autocorrelation sequence (AS), which is more general than the previous work based on Bernoulli sequence (BS). The analysis results show the use of proper ASs as input streams is able to reduce random error compared to the conventional use of BSs. In order to confirm that conclusion, we apply an AS, referred as Maximal Concentrated Autocorrelation Sequence (MCAS), into the stochastic computation system which implements Bernstein polynomial. Both the theoretical analysis and simulation results reveal that the use of MCAS reduces the random error.

This paper proposed a simulation method to assess the remote sensing imaging system by which we can get a clear and visible result in the form of image. It can assist the design and development at low-cost.