Yuanlong Cao

The CAP (Consistence,Availability,Network partitioning) theorem of distributed system determines that the blockchain system cannot guarantee the high availability of the system under the premise of data consistency and partition fault tolerance, which makes it difficult for blockchain-based decentralized application (DApp) systems to deliver as high performance as the classical centralized systems can offer. The performance bottleneck of blockchain applications is an important factor that hinders the implementation of blockchain applications, and how to improve the performance of blockchain system is an important part to improve the blockchain ecology nowadays. However, most of the current optimization algorithms for business flow focus on the business flow itself, and rarely pay attention to the role of the system on business optimization, but the emergence of business flow based on distributed system (blockchain) gives more room for business flow optimization. This paper proposes a performance optimization scheme based on Petri Net combined with blockchain business flow (PNBOA), and simulation experiments show that the scheme can effectively improve the reliability of the system and increase the throughput of the blockchain by 21.2% on the consortium chain system.

Multipath Quick UDP Internet Connections (MPQUIC) integrated with network coding offers a promising approach to improving the Quality of Experience (QoE) for video services over heterogeneous wireless networks. However, a significant challenge arises when encoding nodes transmit potentially redundant packets while awaiting decoding acknowledgments (ACKs) from endpoints. This behavior can limit effective transmission rates, thereby degrading real-time streaming performance and user QoE. In this paper, we propose MP²-QUIC, which addresses these challenges through a novel adaptive Model Predictive Control (MPC) framework for MPQUIC that optimizes both congestion window and encoding redundancy parameters via a discrete state transition model. By incorporating operating point linearization and leveraging the Central Limit Theorem, MP²-QUIC effectively enhances the control performance and effective throughput of the model in heterogeneous wireless network environments. MP²-QUIC further employs Band-Sparse Network Coding (Band-SNC) to minimize computational complexity at endpoints, while utilizing queuing theory principles to determine optimal encoded packet quantities. This integrated approach significantly enhances end-user QoE, and the experimental results demonstrate MP²-QUIC’s superior performance compared to existing MPQUIC encoding solutions, yielding a 68.85% reduction in peak decoding overhead and marked improvements in Peak Signal-to-Noise Ratio (PSNR).

APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitations, such as limited API text corpus, and are affected by the characteristics of the input text. To address these limitations, we propose utilizing large language models (LLMs) (e.g., GPT-3.5) as a neural knowledge base for API relation inference. This approach leverages the entire Web used to pre-train LLMs as a knowledge base and is insensitive to the context and complexity of input texts. To ensure accurate inference, we design an AI chain consisting of three AI modules: API Fully Qualified Name (FQN) Parser, API Knowledge Extractor, and API Relation Decider. The accuracy of the API FQN Parser and API Relation Decider is 0.81 and 0.83, respectively. Using the generative capacity of the LLM and our approach’s inference capability, we achieve an average F1 value of 0.76 under the three datasets, significantly higher than the state-of-the-art method’s average F1 value of 0.40. Compared to the original CoT and modularized CoT methods, our AI chain design has improved the performance of API relation inference by 71% and 49%, respectively. Meanwhile, the prompt ensembling strategy enhances the performance of our approach by 32%. The API relations inferred by our method can be further organized into structured forms to provide support for other software engineering tasks.

In recent years, indoor localization has attracted a lot of interest and has become one of the key topics of Internet of Things (IoT) research, presenting a wide range of application scenarios. With the advantages of ubiquitous universal Wi-Fi platforms and the "unconscious collaborative sensing" in the monitored target, Channel State Information (CSI)-based device-free passive indoor fingerprinting localization has become a popular research topic. However, most existing studies have encountered the difficult issues of high deployment labor costs and degradation of localization accuracy due to fingerprint variations in real-world dynamic environments. In this paper, we propose BSWCLoc, a device-free passive fingerprint localization scheme based on the beyond-sharing-weights approach. BSWCLoc uses the calibrated CSI phases, which are more sensitive to the target location, as localization features and performs feature processing from a two-dimensional perspective to ultimately obtain rich fingerprint information. This allows BSWLoc to achieve satisfactory accuracy with only one communication link, significantly reducing deployment consumption. In addition, a beyond-sharing-weights (BSW) method for domain adaptation is developed in BSWCLoc to address the problem of changing CSI in dynamic environments, which results in reduced localization performance. The BSW method proposes a dual-flow structure, where one flow runs in the source domain and the other in the target domain, with correlated but not shared weights in the adaptation layer. BSWCLoc greatly exceeds the state-of-the-art in terms of positioning accuracy and robustness, according to an extensive study in the dynamic indoor environment over 6 days.

The rapid development of Internet of things (IoT) and edge computing technologies has brought forth numerous possibilities for the intelligent and digital future. The frequent communication and interaction between devices inevitably generate a large amount of sensitive information. Deploying a blockchain network to store sensitive data is crucial for ensuring privacy and security. The openness and synchronicity of blockchain networks give rise to challenges such as transaction privacy and storage capacity issues, significantly impeding their development in the context of edge computing and IoT. This paper proposes a reliable fog computing service solution based on a blockchain fog architecture. This paper stores data files in the inter planetary file system (IPFS) and encrypts the file hash values used for retrieving data files with stream cipher encryption. It employs a steganographic transmission technique leveraging AlphaZero's Gomoku algorithm to discretely transmit the stream cipher key across the blockchain network without a carrier, thus achieving dual encryption. This approach aims to mitigate the storage burden on the blockchain network while ensuring the security of transaction data. Experimental results demonstrate that the model enhances the transmission capacity of confidential information from kilobytes (KB) to megabytes (MB) and exhibits high levels of covert and security features.

The Quick UDP Internet Connection (QUIC) protocol has been utilized in traditional cloud computing environments. However, in consumer electronic devices under edge cloud scenarios, QUIC may struggle to take advantage of its benefits due to its unique network characteristics and resource constraints. This problem arises when the network quality fluctuates or resources are insufficient to handle incoming data, causing rapid buffer expansion, significant delays, and potential packet loss. This study proposes a queue management algorithm inspired by the classical control theory of the Proportional-Integral-Differential (PID) control, which aims to support QUIC data flows to further enhance delay control and improve goodput. The algorithm adds differential operations to the traditional PI control to predict the error trend to respond to queue changes in advance. Combining expert experience in integral separation and queue error management, it makes the control strategy more relevant to the specific needs of real application scenarios. Simulation results demonstrate that the PID-Delay algorithm achieves an average goodput improvement of 1.98 times and reduces the standard deviation by 26.1% than the classical algorithm. In comparison to other delay algorithms, it exhibits an average 1.81 times increase in goodput and an 18.5% reduction in standard deviation.

The Internet Engineering Task Force (IETF) developed a standard wireless communication protocol stack called 6TiSCH to provide low-power and high-reliability communications in harsh industrial environments. However, the current IETF 6TiSCH protocol lacks adequate consideration of inter-device authentication and key management, exposing the IETF 6TiSCH-based wireless network to security risks such as key leakage and malicious attacks. Meanwhile, existing authentication and key management schemes cannot be directly applied to the resource-constrained IETF 6TiSCH industrial wireless network. To address this challenge, we propose a novel scheme called SAKMS, which is tightly integrated with the IETF 6TiSCH network and incorporates the following key components: (i) improved elliptic curve cryptography (ECC) operation, a regular window method is proposed to accelerate the computation of the ECC-related operations; (ii) secure authentication process, SAKMS distributes implicit certificates for each device in the network and adopts the improved ECC algorithm to achieve secure and trustworthy authentication between devices, and the process only consist of hashing, XOR, and a few ECC multiplication operations; (iii) dynamic key update, after successful authentication, devices can negotiate and dynamically update link keys, ensuring resilience against potential key leakage issues. We perform formal and informal security analyses to demonstrate the resilience of SAKMS against various known attacks. Finally, we extensively evaluate the performance of SAKMS in a real 6TiSCH wireless sensor network. The experimental results show a 37% improvement in the computational efficiency of ECC operations compared to existing works, with link key establishment taking only 0.9 s on the OpenMoteSTM platform.