Consortium blockchain systems have been recognized as promising infrastructures for data sharing and collaboration among different organizations due to their trustless nature. However, performance bottlenecks have seriously hindered the applications of these systems in many practical scenarios, especially under high transaction volumes. This study identifies two critical factors affecting system performance: intra-block and interblock transaction conflicts. To address these issues, we present FabricTCA (Fabric Two-level Conflict Avoidance), a method that significantly improves system throughput by mitigating both transaction-level (intra-block) and block-level (inter-block) conflicts. To resolve intra-block transaction conflicts, we propose a scheduling strategy that detects and prevents conflicts by introducing a novel dependency chain data structure and defining the notion of dangerous structures. For inter-block transaction conflicts, we propose a conflicting transaction detection mechanism by constructing a cache during the early ordering service stage, in which detected conflicting transactions will be sent back to clients and resubmitted. Extensive experiments under multiple workload scenarios demonstrate that FabricTCA outperforms state-of-the-art consortium blockchains, such as Hyperledger Fabric (a.k.a. Fabric) and its multiple variants, in terms of throughput, transaction abort rate, transaction execution time, and space utilization. In particular, FabricTCA achieves a 9.51-fold increase in throughput and reduces space consumption by 88.27 % compared to Hyperledger Fabric, showcasing its superior performance. Additionally, FabricTCA demonstrates low storage occupation, strong robustness to attacks and high detection speed.

Sharding is a promising solution to enhance blockchain scalability. While deploying more shards of smaller sizes for a given network scale can significantly boost performance, it also heightens the risk of shard failures. In many existing sharding systems, the failure of a single shard can compromise the entire system. Therefore, to ensure safety, current systems often require each shard to contain hundreds of consensus nodes to prevent crashes, adversely affecting scalability. In this paper, we propose Levee, a blockchain sharding system capable of tolerating shard failures. When a shard malfunctions, Levee can swiftly detect, isolate, and autonomously recover the faulty shard, allowing other shards to operate without interruption. This fault-tolerance feature enables Levee to reduce shard sizes by 73.7% and increase the number of shards by 3.25 times, all without sacrificing security. When tested in a scenario with 7000 nodes, Levee demonstrated a 14.34 times increase in throughput and a 65% decrease in transaction latency compared to traditional non-fault-tolerant sharding systems.

As mixing services are increasingly being exploited by malicious actors for illicit transactions, mixing address association has emerged as a critical research task. A range of approaches have been explored, with graph-based models standing out for their ability to capture structural patterns in transaction networks. However, these approaches face two main challenges: label noise and label scarcity, leading to suboptimal performance and limited generalization. To address these, we propose HiLoMix, a graph-based learning framework specifically designed for mixing address association. First, we construct the Heterogeneous Attributed Mixing Interaction Graph (HAMIG) to enrich the topological structure. Second, we introduce frequency-aware graph contrastive learning that captures complementary structural signals from high- and low-frequency graph views. Third, we employ weak supervised learning that assigns confidence-based weights to noisy labels. Then, we jointly train high-pass and low-pass GNNs using both unsupervised contrastive signals and confidence-based supervision to learn robust node representations. Finally, we adopt a stacking framework to fuse predictions from multiple heterogeneous models, further improving generalization and robustness. Experimental results demonstrate that HiLoMix outperforms existing methods in mixing address association.

Ethereum 2.0 (ETH2) marks a pivotal shift in blockchain technology, transitioning from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism, with Gasper at its core. While this evolution promises enhanced scalability and energy efficiency, the performance of its block proposal stage is highly sensitive to network latency and system parameters, such as slot length. This sensitivity introduces a critical trade-off between throughput and security, measured by the probability of blockchain forking. This paper reveals that network latency is not just a passive risk but an exploitable attack surface. We introduce the "adaptive latency-driven equivocation attack", a novel adversarial strategy where an attacker deliberately creates forks while mimicking the behavior of a high-latency node, thus achieving plausible deniability. To formally analyze and quantify the impact of this threat, we develop a comprehensive theoretical model by using Markov chains to analyze the fork probability and throughput of the Gasper's block proposal mechanism under both honest and adversarial conditions. Through extensive simulations, we validate the accuracy of our model in both normal and bursty traffic conditions. Our findings provide a systematic methodology for optimizing system parameters to achieve a robust balance between performance and security, offering a foundational guide for configuring ETH2 networks against sophisticated, latency-based threats.

Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but the extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. While dynamic scheduling strategies can mitigate this issue, they introduce new trust concerns: how to verify that scheduling decisions are fair, and whether clients faithfully execute optimization instructions without disclosing private data? This paper proposes Nautilus, a verifiable and efficient federated learning framework. First, we design a multi-dimensional resource-aware scheduling algorithm that dynamically allocates compression ratios and training tasks based on vehicles bandwidth, latency, and computing power, significantly improving system training efficiency. Second, to address the trust deficit in the scheduling process, we introduce a Zero-Knowledge Proof (ZKP) mechanism that ensures the fairness of scheduling strategy generation and the compliance of client execution while preserving privacy. Experimental results demonstrate that the framework effectively reduces communication overhead and accelerates model convergence while maintaining system integrity.

The proliferation of Web3 and Internet of Things (IoT) applications generates unprecedented volumes of real-time data streams, demanding secure and efficient subscription mechanisms that uphold data sovereignty. While decentralized architectures are the logical paradigm to ensure this sovereignty, a prominent class of existing schemes suffers from critical vulnerabilities—notably revocation attacks and prohibitive communication overhead—that severely hinder their practical deployment in large-scale environments. This paper introduces SegSub, a novel decentralized data subscription scheme specifically designed to significantly enhance both security and efficiency. SegSub's core innovations include the Segmented Dual-Key Regression with Binary Hash Trees (SDKR-BHT) mechanism, which partitions key regression chains into isolated segments to effectively contain potential data leakage and optimize token management and a strategic user grouping policy that localizes key updates, thereby substantially reducing system-wide communication overhead during revocation events. We formally quantify security improvements using a proposed security index and demonstrate a configurable trade-off between security and efficiency. Theoretical analysis and extensive experimental results validate that SegSub's security index is inversely proportional to segment length while communication efficiency is directly proportional. Furthermore, our grouping policy significantly reduces communication costs in large-scale scenarios through optimal group sizing. SegSub offers a robust and adaptable foundation for sovereignty-preserving data subscription services in Web3, empowering system designers with precise control over the critical security-efficiency balance to meet diverse deployment requirements.

Blockchain systems aim to achieve security and high-performance. These characteristics depend on efficient data broadcast mechanisms. However, existing mechanisms often suffer from inefficiency, primarily because they fail to consider geographic proximity in network topology construction and rely on redundant gossip-based transmission. We introduce Mercator, a geography-aware structured data broadcast protocol. It enables fast and robust data propagation through two techniques: (i) region division strategy based on geographic proximity, and (ii) an efficient and robust broadcast method covering inter-region and intra-region. Extensive simulations show Mercator significantly outperforms current broadcast schemes, reducing propagation latency by up to 61.94% and message redundancy by up to 73.03%. Moreover, Mercator maintains robustness and low latency even in the presence of 30% malicious nodes.

The growing adoption of permissioned blockchains in trade, finance, logistics has led to a proliferation of isolated networks that increasingly function as data and value silos. Achieving secure cross-chain interoperability is critical to unlocking the full potential of these ecosystems. However, existing cross-chain protocols often assume transparent state access or rely on trust assumptions that are incompatible with permissioned blockchains, which enforce strict access control and prohibit public state visibility. In this paper, we propose Pistis, a secure and verifiable framework designed to enable interoperability across unobservable permissioned blockchains. Pistis introduces a novel architecture based on decentralized oracle committees and witness network. Specifically, we propose a dynamic oracle committee election method based on verifiable random function (VRF) and a secure and verifiable cross-chain transfer protocol based on independent vote-based consensus. To support sensitive information transfer, Pistis integrates end-to-end encryption, zero-knowledge proof (ZKP), and verifiable oracle attestation. We formally analyze the security properties of Pistis and prove that it achieves strong guarantees of availability, confidentiality, and verifiability under adversarial conditions. We also implement a prototype on Hyperledger Fabric and conduct extensive experiments, showing that Pistis maintains low processing latency and high success rates across diverse adversarial and sensitive information transfer scenarios, demonstrating its feasibility in practical applications.

Although cryptocurrency trend classification is more reliable than direct price prediction, it still faces notable challenges under extreme market conditions. These markets typically display high volatility, strong noise, and sudden structural changes, which often cause existing models to overfit or generalize poorly. To address these challenges, we propose RocketFormer, a robust framework for cryptocurrency trend classification. RocketFormer integrates large-scale random convolutional kernels with a Transformer encoder to learn more stable local features and capture global dependencies, enabling the model to better handle noise disturbances and structural changes in extreme markets. It further introduces a volatility-aware two-stage mechanism to enhance the stability of both training and inference. Experiments on minute-level real data from four major cryptocurrencies show that RocketFormer achieves the best overall performance among eleven state-of-the-art classifiers, outperforming the strongest baseline by 2.6

Cross-chain swaps are a crucial application that facilitates the transfer of digital assets across different blockchains, thereby enhancing the flexibility and availability of asset circulation. Ensuring atomicity is a fundamental objective for cross-chain swaps. The Hashed TimeLock Contract (HTLC) protocol is one of the primary solutions for cross-chain swaps. It ensures atomicity by statically partitioning execution actions for swap submission and asset refund along the time dimension. Such designs rely on a predictable upper bound on transaction confirmation time. However, this assumption does not hold in practical blockchain environments, leading to atomicity violations. To address this limitation, we propose Chuchu, a hashlock group protocol for cross-chain swaps. Chuchu abandons static partitioning along the time dimension and instead distinguishes swap submission and asset refund through explicitly defined asset locking states and their dynamic transitions. To cope with divergent execution progress across blockchains, Chuchu bounds execution progress divergence and introduces an exit-proof mechanism, ensuring that locked assets always have well-defined and consistent unlocking paths under all execution scenarios. The effectiveness and feasibility of Chuchu are demonstrated through theoretical analysis and formal verification using TLA+. Meanwhile, experiment results show that Chuchu reduces execution time by over 98% compared to the HTLC protocol in the case of swap rollbacks.

Effective visual analysis of underwater environments for applications in geoscience and remote sensing is fundamentally constrained by two principal factors: severe image degradation inherent to the aquatic medium and the extreme geometric diversity of instances. Existing methods often struggle as they rely on separate enhancement modules or complex architectures that are not optimized for these conditions. To overcome these limitations, we propose Clarity-Former, a novel one-stage, end-to-end Transformer framework designed with a synergistic architecture to overcome both challenges simultaneously. The core of our framework is a self-adaptive masked attention (SAMA) Transformer backbone, which uses an adaptive temperature and a diagonal masking strategy to extract robust local features directly from noisy, low-contrast inputs. Building on this robust feature foundation, a Transformer encoder-decoder module with a sparse attention sampling mechanism efficiently models long-range, multiscale dependencies to handle vast variations in instance scale and shape. Finally, a variance-guided spatial enhancement (VGSE) module intelligently fuses and refines multiscale features to ensure the generation of high-quality segmentation masks. Extensive experiments on the UIIS, USIS10K, and USIS16 datasets demonstrate that Clarity-Former achieves an optimal tradeoff between efficiency and precision. Specifically, our small variant surpasses the previous best method WaterMask by 2.8%, 5.8%, and 2.5% mean average precision (mAP). Furthermore, even with the minimal parameter (45 M), our tiny variant consistently outperforms existing baselines on all three datasets. This positions Clarity-Former as a highly efficient framework for automated underwater visual analysis.

Blockchain systems expose a large number of tunable parameters that significantly influence system performance. However, in practice, a single parameter configuration is often applied across different workloads, leaving substantial unexploited performance potential. To address this, we propose EchoFlow, a blockchain parameter tuning framework that adaptively adjusts parameter configurations based on workload characteristics, enabling continuous performance optimization. EchoFlow employs a distributed reinforcement learning approach in which multiple actors perform parallel sampling to mitigate the substantial time required for sample generation in blockchain environments. To further accelerate convergence, we introduce a genetic algorithm during the initial phase of training to generate high-quality samples. Extensive experimental evaluations demonstrate that EchoFlow consistently outperforms existing methods across diverse workload scenarios while also reducing training time, highlighting its effectiveness and practical value.

In digital asset trading, blockchain oracles play a crucial role. This paper proposes a novel oracle price feed framework to address the significant price deviations and high maintenance costs associated with oracles in digital asset trading. This framework employs off-chain data to cleanse on-chain exchange price information, effectively preventing abnormal price deviations of assets. Simultaneously, by increasing the deviation threshold for passive price feeds, the frequency of passive feeds is reduced, lowering the cost of uploading off-chain data to the blockchain and thereby decreasing the maintenance costs of the oracle.

BlockNetSys promotes a network-aware co-design of consensus, dissemination, and verifiable networking. With 22 submissions, 10 were accepted ( 45.5%), spanning multicast propagation, eBPF/SGX-and SDN-based auditing, Bayesian/game-theoretic DoS defense, and ledger-driven decisions for edge/vehicular and extreme IoT. Results indicate that topology-aware propagation shortens confirmation tails and reduces traffic, tiered evidence (rich off-chain telemetry with on-chain commitments) enables line-rate, complianceready accountability, security posture must be tuned against liveness/finality, and edge learning gains from blockchain-backed provenance and incentives. We group contributions into four areas: Network-aware Consensus and Dissemination; On-/Off-chain Auditing and SDN Accountability; Security Modeling and DoS Defense; and Ledger-driven Edge/IoT Offloading and Coordination.

The widespread adoption of WiFi has made throughput efficiency a critical concern in wireless networks. While Full-Duplex (FD) technology promises to double network capacity by enabling simultaneous transmission and reception, existing FD-WiFi designs focus on the data transmission phase, leaving the fundamental inefficiencies in channel contention unaddressed. This paper presents CollFree, a novel WiFi protocol that exploits FD capabilities during both contention and data transmission phases. At its core, CollFree introduces a Slotwise Arbitration (SA) mechanism that enables each node to simultaneously transmit contention signals and sense channel status in each contention slot. This dual-mode operation significantly reduces contention time and facilitates collision- free data transmissions through a unique winner-determination process. We then develop theoretical models to analyze CollFree's contention performance and throughput efficiency under both perfect and imperfect Clear Channel Assessment (CCA) conditions, providing guidelines for parameter optimization in practical deployments. Extensive simulations demonstrate that CollFree enhances throughput efficiency by over 20% compared to state-of-the-art FD-WiFi systems while maintaining distributed control and compatibility with current WiFi standards. These results suggest that it represents a significant step toward realizing the full potential of FD technology in next-generation WiFi networks.

Blockchain systems, characterized by decentralization, irreversibility, and traceability, have attracted widespread adoption across security-critical domains. However, the discrepancy between claimed and observed performance metrics poses significant challenges for system selection and reliability assurance. Particularly, in multi-layer blockchain architectures with complex inter-node interactions, pinpointing abnormal nodes or malfunctioning stages remains a non-trivial task. While macroscopic metrics, such as transactions per second (TPS) and average latency are essential for measuring system capacity, they lack the granularity to capture fine-grained operational anomalies. To complement these metrics and provide a new diagnostic dimension, we propose a novel metric system grounded in the spatio-temporal transition of transaction states, introducing three complementary indicators: overall spatio-temporal transition cost, spatial transition cost, and temporal transition cost. These metrics characterize system-wide behavior and enable anomaly detection at both the node and stage levels. We further develop a log-driven analysis framework that leverages these metrics for anomaly localization and root cause inference. Our system is implemented atop ChainMaker, deployed over 16 containerized nodes using Docker and Kubernetes. Experimental results demonstrate that our proposed metrics exhibit strong stability, sensitivity, and precision in detecting abnormal behaviors under varied execution conditions. These results validate the effectiveness of our methodology in providing fine-grained insights into the performance reliability of blockchain systems.

Energy storage power station faces problems such as frequent charging and discharging switching, high energy loss, and poor economic benefits in dealing with the deviation of renewable energy power prediction and generation independently. This paper proposes an optimal operation method for the capacity sharing of multiple energy storage power stations based on a consortium blockchain. Firstly, capacity sharing profit distribution strategy considering opportunity cost and other contribution factors is proposed for energy storage stations. Then, a storage capacity sharing consortium chain, is constructed with capacity sharing smart contracts, which achieve trustworthy sharing of operational data among various energy storage power stations. Finally, optimization model is established to find the optimal operation schedule of energy storage power stations, including constraints of dealing prediction deviation, charging and discharging limitation, and power balance of grid are set to minimize operating costs, and variables of real-time service relationship and reference charging and discharging power. The simulation results show that compared with the independent operation method, the proposed method can reduce operation costs by an average of 45.93 yuan/kWh annually. At least 23.50% of the peak shaving losses caused by the prediction deviation of renewable energy power output in energy storage power stations could be recovered.

In Web 3.0, to achieve the continuous release of the value of data elements, we start with technologies related to data circulation and conduct research and analysis on the current data element market. We find that in the traditional model of data element circulation, the sharing and exchange of multi-source heterogeneous data are disconnected from the circulation and release of data element value. This results in poor data reusability, unclear data requirements, small market size, short lifecycle of data elements and data markets, making it difficult to motivate sellers to actively participate in building the data element market, and thus hard to achieve a positive cycle of data element value release. To address this, we have conducted a strategic analysis of the value release of data elements and designed a universal reference model, DataR2E, for the entire process of value release in the data elements market. Within the DataR2E model, we introduce the concept of data element bounty production, utilizing Web3 technology and data production tools to encourage sellers to actively provide the data elements needed by buyers. We envision using data production tools to build a bridge between buyers and sellers in the data elements market, promoting the sustainable development of the data element market, overcoming the mismatch in data elements expected by buyers and sellers, thereby resolving the issues of market presence without pricing and pricing without market presence, achieving sustainable development of the data elements market, and maximizing the potential of data element value release.

The rapid growth of blockchain ecosystems has given rise to Complex Cross-chain Networks (CCN), where interoperability is required not only for simple pairwise connections but also for more advanced architectures such as Direct-Link-Based, Single-Relay-Based, and Multi-Relay-Based networks. However, most existing cross-chain protocols are tailored to a single network architecture, which limits their effectiveness in heterogeneous cross-chain environments and restricts the development of broader cross-chain applications. To overcome these limitations, we present Honeycomb, a unified route-aware interoperability framework that supports complex cross-chain network architecture. The first challenge of Honeycomb is how to abstract the routing functions of complex cross-chain network architectures into a single layer. For that, we introduce the Cross-chain Route Layer (CRL) into the traditional layered interoperability framework. The second challenge is how to efficiently and securely discover trustworthy paths within CCN. To solve it, we propose a two-phase strategy: the first phase uses Forward-Update and Post-Probe strategies for scalable off-chain pathfinding, while the second phase employs a round-trip on-chain verification mechanism to ensure the trustworthiness of the selected paths. We implement Honeycomb on Ethereum-like blockchains and conduct comprehensive evaluations. The results demonstrate that CRL introduces only minimal overhead with an additional gas cost of just 0.6%-2.5% compared to traditional protocols.