With the rapid advancement of quantum computing, the security of classical blockchain systems is facing severe challenges. The emergence of quantum blockchain offers a promising solution to fundamentally address these issues. Consortium blockchains, due to their inherent characteristics, are particularly well-suited for the deployment and implementation of quantum blockchains at the current stage. This paper proposes a novel quantum consensus mechanism named Q-PnV (Quantum Proof and Voting), tailored for consortium blockchains. The consensus extends the classical Proof of Vote (PoV) consensus mechanism into the quantum domain and innovatively integrates quantum voting, quantum identity authentication, and quantum random number generation technologies. By combining Q-PnV with a quantum blockchain architecture based on weighted hypergraph states, this work presents a comprehensive quantum blockchain solution for consortium scenarios. Theoretical analysis demonstrates that, compared to classical approaches, a quantum blockchain based on Q-PnV not only effectively resists quantum attacks, but also significantly enhances the security and fairness of the consensus process, thereby providing a reliable foundation for consortium blockchain applications in a future quantum computing environment.

The traditional network, designed around the TCP/IP protocol, achieves simple and efficient inter-connectivity between various protocols at the lower and upper layers through the thin-waist stack. However, the IP protocol primarily focuses on facilitating mutual communication between two terminal nodes. With the emergence of new network services and application scenarios, there has been an increasing demand for data services. Consequently, the traditional network architecture is no longer suitable for current and future users. In this paper, focusing on the multi-identifier scenarios, we propose MIN-ENL, an evolutionary network layer for future network, for communication, which includes an extensible multi-identifier management scheme and a general addressing scheme based on extensible identifier structure. Firstly, based on the standard definitions of identifiers and their related concepts, we design an extension identifier management scheme that supports the co-existence of multiple identifiers. Then, we design a general addressing scheme, which includes an identifier detection mechanism and a candidate identifier sorting algorithm. This scheme detects relationships between multiple identifiers in the form of a graph and selects an effective identifier for addressing. It allows for the continuous expansion and evolution of identifiers and communication modes in the network layer. The complete functionality of the identifier extension mechanism is implemented through the development of a network topology and a physical testbed in commercial operators' networks based on TCP/IP. The experimental results of prototypes demonstrate that the proposed scheme achieves backward compatibility and forward evolution, with acceptance performance cost.

Anomalous Sound Detection (ASD) serves as a core technology in equipment health monitoring, its main challenge being the domain shift problem caused by variations in machine operating conditions. To address this issue, we propose an ASD framework that integrates dual-dimension data mixing and progressive parameter fine-tuning. Specifically, the designed dual-dimension data mixing comprises: (1) Conditional Mixing, which linearly interpolates samples sharing the same operating conditions to mitigate feature distribution shifts induced by condition variations; and (2) Batch Mixing, which employs a cross-domain random batch mixing strategy to construct a domain-invariant feature space. Furthermore, progressive parameter fine-tuning is applied, where lower-layer encoding layers are frozen to solidify domain-invariant features, while a hierarchical parameter unfreezing mechanism is adopted to enable dynamic adaptation of high-level features. Finally, our method is evaluated on the DCASE2022 Task 2 dataset, achieving a harmonic AUC score of 0.78, outperforming both the baseline and the Task 2 winner.

Image dehazing is regarded as a fundamental image processing task with a major impact on higher-level imaging tasks. Many existing haze removal methods are designed for homogeneous haze, but in real-world cases, the haze is normally non-homogeneous. Superpixels, which segment an image into a set of closely spaced regions, can be employed in real-world scenarios to deal with non-homogeneous haze. In our paper, an adaptive non-homogeneous image dehazing approach that utilizes the superpixel-guided algorithm is designed to segment different hazy regions. Considering that both ambient light and transmission map estimation have a significant impact on the results, our research focuses on the development of a variational dehazing model that takes into account non-uniform ambient light and non-uniform transmission maps to address varying levels of haze. A series of numerical results illustrate the superiority and efficacy of our method.

Information-Centric Networking (ICN) is a promising future network architecture that shifts the host-based network paradigm to a content-oriented one. Over the past decade, numerous ICN congestion control (CC) schemes have been proposed, tailored to address congestion issues based on ICN’s transmission characteristics. However, several key challenges still need to be addressed. One critical issue is that most existing CC studies for ICN do not consider the diverse Quality of Service (QoS) requirements of modern network applications. This limitation hinders their applicability across various applications with different network performance preferences. Another ongoing challenge lies in improving transmission performance, particularly considering how to appropriately coordinate congestion control participants to enhance content retrieval efficiency and ensure reasonable resource allocation, especially in multipath scenarios. To tackle these challenges, we propose QSCCP, a QoS-aware congestion control protocol built upon NDN (Named Data Networking), a well-known ICN architecture. In QSCCP, diverse QoS preferences of various traffic are supported within a collaborative congestion control framework. A novel multi-level, class-based scheduling and forwarding mechanism is designed to ensure varied and fine-grained QoS guarantees. A distributed congestion notification and precise feedback mechanism is also provided, which efficiently collaborates with an adaptive multipath forwarding strategy and consumer rate adjustment to rationally allocate network resources and improve transmission efficiency, particularly in multipath scenarios. Extensive experimental results demonstrate that QSCCP satisfies diverse QoS requirements while achieving outstanding transmission performance. It outperforms existing schemes in throughput, fairness, delay, and packet loss, with a rapid convergence rate and excellent stability.

Log-Structured Merge Tree (LSM-tree) is commonly used for building high-performance persistent key-value stores. However, they are known to suffer from severe I/O amplification. To mitigate this issue, structures like Fragmented Log-Structured Merge Trees (FLSM-tree) have been developed, which leverage tiering merge policies to reduce write amplification. Nevertheless, FLSM-tree fails to consider data access frequency, access times, and other heat-related information. Additionally, the Sorted String Table(SSTable) in each level may not necessarily be ordered, leading to diminished read and range query performance. To address these issues, we have proposed a high-performance key-value storage structure, tailored specifically for read/write-intensive workloads, called HIndex-FLSM. This structure employs a heat calculation mechanism for cold and hot data segregation and enhances read performance by indexing Guard. The compaction process has also been optimized by combining heat and ordered indexing. To evaluate the performance of HIndex-FLSM, we have implemented the prototype system of HIndex-FLSM, called HIndex-PebblesDB, based on the prototype system PebblesDB of FLSM-tree. Through benchmark testing of HIndex-PebblesDB, we observed an approximately 20% improvement in hot spot read and 57% in range query compared to PebblesDB.

Blockchain has now become a critical infrastructure in Metaverse for storing and managing the digital resources of users, bridging the real and virtual worlds. However, consensus protocols in blockchains constrain the performance of their applications. While existing voting-based consensus protocols such as HotStuff and other Byzantine Fault Tolerance (BFT) protocols have optimized efficiency and scalability, they simply adopt a one-person-one-vote rule that is not aligned with the human-centric values of most blockchain applications, including Metaverse. Therefore, we propose Stake Voting Protocol (SVP), a secure and scalable consensus protocol, whose design philosophy is to consider validators’ stakes in the BFT protocol and introduce flexibility through a sliding window. We also propose a certification rule within the pipelined two-chain consensus process to enhance security. Furthermore, our epoch change and incentive mechanisms ensure dynamics and liveness, respectively. Finally, our analytical and experimental results demonstrate that the proposed SVP satisfies correctness and can resist specific attacks with low latency and high throughput.

The evolution of communication technologies has driven the advancement of autonomous driving. However, infrastructure components based on the traditional IP network architecture are susceptible to network delay and traffic load, especially in the context of high vehicle mobility. As a promising future network architecture that combines multiple communication semantics and multiple network identifiers, Multi-Identifier Network (MIN) provides superior quality of experience and efficient data transmission in the V2X (Vehicle To Everything) environment. Nevertheless, resource-constrained vehicular nodes pose challenges in minimizing communication delay and resource loss when storing content copies in caching nodes. In order to address this challenge, we propose a cooperative caching scheme in MIN-V2X. The proposed solution uses a hierarchical network architecture to efficiently manage the caching of in-network resources, thereby reducing the latency of accessing content and network resource redundancy. It also filters content that needs to be cached and implements a cache replacement scheme better suited to mobile environments, making more efficient use of node cache space. Experimental results demonstrate that the proposed scheme makes significant improvements in terms of cache hit rate, content access delay, and average hop count for data fetching in the V2X environment.

Collaborative exploration in environments involving multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) represents a crucial research direction in multi-agent systems. However, there is still a lack of research in the areas of multi-target detection task assignment and swarm path planning, both of which play a vital role in enhancing the efficiency of environment exploration and reducing energy consumption. In this paper, we propose an air–ground collaborative multi-target detection task model based on Mixed Integer Linear Programming (MILP). In order to make the model more suitable for real situations, kinematic constraints of the UAVs and UGVs, dynamic collision avoidance constraints, task allocation constraints, and obstacle avoidance constraints are added to the model. We also establish an objective function that comprehensively considers time consumption, energy consumption, and trajectory smoothness to improve the authenticity of the model and achieve a more realistic purpose. Meanwhile, a Branch-and-Bound method combined with the Improved Genetic Algorithm (IGA-B&B) is proposed to solve the objective function, and the optimal task assignment and optimal path of air–ground collaborative multi-target detection can be obtained. A simulation environment with multi-agents, multi-obstacles, and multi-task points is established. The simulation results show that the proposed IGA-B&B algorithm can reduce the computation time cost by 30% compared to the traditional Branch-and-Bound (B&B) method. In addition, an experiment is carried out in an outdoor environment, which further validates the effectiveness and feasibility of the proposed method.

Message Queuing Telemetry Transport (MQTT) is a data transmission protocol based on the publish/subscribe model. Currently, MQTT is extensively employed in the Internet of Vehicles due to its lightweight, efficient, and reliable attributes. Existing MQTT protocols are mostly built on the traditional TCP/IP architecture. However, the singularity of the underlying communication semantics constrains their effectiveness in handling data forwarding within V2X (Vehicle to Everything) networks. Multi-Identifier Network (MIN) emerges as a promising future network architecture, which supports both push-based and pull-based semantics along with multiple network identifiers. It offers excellent mobility support and efficient data transmission capabilities. In this paper, we propose MIN-MQTT, a publish/subscribe approach based on MIN for the V2X environment. MIN-MQTT leverages network identifiers for efficient packet forwarding and provides seamless mobility support. It also ensures data integrity and traceability through node identity binding, signature verification, and other security mechanisms. Experimental results demonstrate that MIN-MQTT outperforms the existing schemes in terms of transmission performance and resource consumption in the V2X environment.

Navigation in complex indoor environments is often difficult, and many of the current navigation applications on the market are not yet mature enough for indoor use. To address this issue, this project developed an application based on Unity's ARCore extension for AR Foundation and Google Cloud Anchor Service, combined with the real-time database, to identify, record the location of key points indoors, and to provide self-localization, path planning and navigation functions for users. The application is divided into two sections: Administrator and User. In the administrator interface, the device camera scans the environment to record the posture of key points and the characteristics of the area where they are located, generates anchor points, and uploads them to the cloud platform database. In the user interface, the user can choose to download the data, after which the environmental features scanned by the camera will be matched with the anchor point features in the database, and the anchor point in the current environment will be identified and displayed on the screen for the purpose of self-localization, after the user selects the destination, the path planning algorithm will be invoked and the planned navigation route will be displayed on the screen.

With the rapid popularization of mobile devices and the wide application of various sensors, scene perception methods applied to mobile devices occupy an important position in location-based services such as navigation and augmented reality (AR). The development of deep learning technologies has greatly improved the visual perception ability of machines to scenes. The basic framework of scene visual per-ception, related technologies and the specific process applied to AR navigation are introduced, and future technology development is pro-posed. An application (APP) is designed to improve the application effect of AR navigation. The APP includes three modules: navigation map generation, cloud navigation algorithm, and client design. The navigation map generation tool works offline. The cloud saves the navigation map and provides navigation algorithms for the terminal. The terminal realizes local real-time positioning and AR path rendering.

With the constant evolution of emerging applications and changing user demands, the limitations of traditional TCP/IP network architecture are gradually exposed, such as mobility and security concerns. The Multi-Identifier Network (MIN) is a promising future network architecture designed to handle these challenges. This paper focuses on the transport layer of MIN networks. We propose a secure and efficient transport protocol (SETP) for MIN. SETP offers extensible encoded packet format, identity-based encrypted handshake, process identifier-based network address multiplexing, non-blocking stream multiplexing, and seamless connection migration. The experimental results demonstrate that the proposed protocol has distinct advantages over existing solutions, particularly regarding enhanced resource utilization, tolerance to head-of-line blocking, and connection migration performance.

As a popular and innovative technology in fields including digital currencies and the Internet of Things, blockchain provides decentralized and immutable trust to multiple parties through an append-only chained structure and redundant replicas. However, the increasing length of the blockchain leads to extreme storage costs. While some lightweight storage schemes compress block data on-chain, most of them suffer from poor generality, limited compression capabilities, and imperfect read performance. To alleviate these issues, we propose a cooperative storage scheme as a lightweight solution for resource-limited blockchains. It incorporates a history-based node assignment mechanism and a timeline-based adaptive read mechanism to reduce the decoding probability when accessing transactions. The former maps chunks of erasure codes to nodes based on their history scores, while the latter redundantly caches blocks at certain nodes for easy access. Experiments demonstrate that the proposed scheme actively mitigates the influence of anomalous nodes on read requests. In contrast to typical schemes, it achieves extremely low read time with less storage cost, which is especially valuable in the long term.

As part of vehicle to everything (V2X) environments, intelligent connected vehicles (ICVs) generate a large amount of data, which can be exploited securely and effectively through decentralized techniques such as federated learning (FL). Existing FL systems, however, are vulnerable to attacks and barely meet the security requirements for real-world applications. If malicious or compromised ICVs upload inaccurate or low-quality local model updates to the central aggregator, they may reduce the accuracy of the global model, thereby reducing drivers safety and efficiency. This paper aims to alleviate these concerns by presenting BV-ICVs, a blockchain-enabled and privacy-preserving FL framework for ICVs in an edge-envisioned V2X environment. This system uses Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zkSNARKs) verification that is compiled as smart contracts to prevent malicious, compromised or even rational ICVs from uploading unreliable, erroneous or low-quality model updates. The verification process is embedded within the consensus of the underlying permissioned blockchain, which maximizes both the efficiency of the process and the utilization of computer resources. As demonstrated by discussions, security analysis, and numerical results, BV-ICVs reduced data poisoning attacks and increased the privacy protection and accuracy of FL.

Decentralized cryptocurrency systems, known as blockchains, have shown promise as an infrastructure for mutually distrustful parties to securely agree on transactions. Nevertheless, blockchain systems are constrained by the CAP Trilemma. Due to performance degradation, it is impossible to address this issue by improving simply the consensus layer or the network layer. To alleviate the CAP constraint in consortium blockchains, we propose a topological construction method to optimize the physical layer based on multi-dimensional hypercubes with excellent partition tolerance in probability. The basic topology has the advantage of solving the mismatch problem between the overlay network and the underlying network. It is further extended to hierarchical recursive topologies with more intermediate links or short links to balance the reliability requirement with the cost of building the physical network. We prove that the proposed hypercube topology has better partition tolerance than the regular rooted tree and ring lattice topologies, and effectively fits the upper-layer protocols at the consensus and network layers. As a result, combined with suitable transmission and consensus protocols that satisfy strong consistency and availability, the proposed topology-constructed blockchain can reach the CAP guarantee bound.

One of the fascinating aspects of sports rivalry is that anything can happen. The significant difficulty is that computer-aided systems must address how to record and analyze many game events, and fractal AI plays an essential role in dealing with complex structures, allowing effective solutions. In table tennis, we primarily concentrate on two issues: ball tracking and trajectory prediction. Based on these two components, we can get ball parameters such as velocity and spin, perform data analysis, and even create a ping-pong robot application based on fractals. However, most existing systems rely on a traditional method based on physical analysis and a non-machine learning tracking algorithm, which can be complex and inflexible. As mentioned earlier, to overcome the problem, we proposed an automatic table tennis-aided system based on fractal AI that allows solving complex issues and high structural complexity of object tracking and trajectory prediction. For object tracking, our proposed algorithm is based on structured output Convolutional Neural Network (CNN) based on deep learning approaches and a trajectory prediction model based on Long Short-Term Memory (LSTM) and Mixture Density Networks (MDN). These models are intuitive and straightforward and can be optimized by training iteratively on a large amount of data. Moreover, we construct a table tennis auxiliary system based on these models currently in practice.

With the rapid development of the network, the IP network have exposed many issues, such as the exhausted IP addresses, security risks and inapplicability to the streaming media. To solve these issues, Multi-Identifier Network (MIN) is proposed as a novel network architecture with excellent features. In the MIN architecture, content can be retrieved from multiple sources and multiple paths. To fit the nature of MIN, congestion control protocols should consider the impact of in-network caching, multi-path forwarding. In this paper, we propose a practical congestion control protocol, which detects congestion based on CoDel algorithm and then notices consumers by explicitly marking data packets, so that consumers can adjust their interest sending rate. Meanwhile, it employs a caching strategy based on content popularity to reduce interest retransmission rate. Our simulation results show that the proposed scheme achieves a higher total throughput than existing work.

Background Exploring correspondences across multiview images is the basis of various computer vision tasks. However, most existing methods have limited accuracy under challenging conditions. Method To learn more robust and accurate correspondences, we propose DSD-MatchingNet for local feature matching in this study. First,we develop a deformable feature extraction module to obtain multilevel feature maps, which harvest contextual information from dynamic receptive fields. The dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust correspondence. Second, we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching, which enables our method to produce more accurate correspondences. Result Experiments show that our proposed DSD-Matching Net achieves a better performance on the image matching benchmark, as well as on the visual localization benchmark.Specifically, our method achieved 91.3% mean matching accuracy on the HPatches dataset and 99.3% visual localization recalls on the Aachen Day-Night dataset.

Some Byzantine fault tolerant (BFT) consensus algorithm in consortium blockchains have lots of message broadcast to encroach bandwidth, which greatly affect the efficiency of consensus execution. Although reducing the number of global consensus nodes can help, it is also at the cost of reduced security. Based on the idea of Mimic Defense, this paper proposes a consensus framework for BFT consensus and takes the Parallel Proof of Vote (PPoV) algorithm as an example to design a randomized node sharding and role partitioning scheme to improve security of the consensus process without affecting efficiency much. In addition, the multimode adjudication group composed of the shard leaders can replace the single leader to complete voting statistics to improve the reliability of consensus results. Through theoretical analysis, it is known that the BFT consensus framework based on mimic security, taking PPoV as an example, has a strong defense effect against eclipse attacks and selfish mining. A simulation experiment is also designed to prove that through efficient iteration and dynamic threshold design, the adjudication group greatly improves the reliability of the results and the robustness of the algorithm.