Blockchain technology, renowned for its decentralized and secure nature, has gained substantial attention. Central to its functionality are consensus mechanisms, which are essential for validating transactions and upholding the integrity of the distributed ledger. However, the efficiency and scalability of blockchain are currently impeded by the resource limitations and excessive communication demands of existing consensus mechanisms. To address these challenges, we propose GT-BFT, a streamlined and lightweight blockchain consensus mechanism grounded in a global trust model. This model capitalizes on node behavior to form consensus groups and facilitate consensus achievement. GT-BFT integrates a novel approach of selective broadcasting along with a Byzantine threshold determination algorithm, significantly boosting both the efficiency and security of the network. Our extensive analysis and performance evaluation reveal that GT-BFT surpasses existing mechanisms in key areas such as security, system throughput, and transaction confirmation speed, marking a significant advancement in blockchain consensus technology.

The Ethereum ecosystem, which secures over $381 billion in assets, fundamentally relies on client APIs as the sole interface between users and the blockchain. However, these critical APIs suffer from widespread implementation inconsistencies, which can lead to financial discrepancies, degraded user experiences, and threats to network reliability. Despite this criticality, existing testing approaches remain manual and incomplete: they require extensive domain expertise, struggle to keep pace with Ethereum’s rapid evolution, and fail to distinguish genuine bugs from acceptable implementation variations. We present APIDiffer , the first specification-guided differential testing framework designed to automatically detect API inconsistencies across Ethereum’s diverse client ecosystem. APIDiffer transforms API specifications into comprehensive test suites through two key innovations: (1) specification-guided test input generation that creates both syntactically valid and invalid requests enriched with real-time blockchain data, and (2) specification-aware false positive filtering that leverages large language models to distinguish genuine bugs from acceptable variations. Our evaluation across all 11 major Ethereum clients reveals the pervasiveness of API bugs in production systems. APIDiffer uncovered 72 bugs, with 90.28% already confirmed or fixed by developers, including one critical error in the official specifications themselves. Beyond these raw numbers, APIDiffer achieves up to 89.67% higher code coverage than existing tools and reduces false positive rates by 37.38%. The Ethereum community’s response validates our impact: developers have integrated our test cases, expressed interest in adopting our methodology, and escalated one bug to the official Ethereum Project Management meeting. By making APIDiffer open-source, we enable continuous validation of Ethereum client API implementations, thereby strengthening the foundational integrity of the entire Ethereum ecosystem.

The Internet of Things (IoT) cloud model provides an efficient scheme for rapid collection, storage, processing, and analysis of massive node data, and its application has gradually expanded to key areas such as healthcare and transportation. However, the security issues of open channel transmission in IoT still persist. Researchers have proposed a lot of solutions, but the forward secrecy, session key security, and other aspects have not been effectively solved. This paper proposes a provably secure authenticated key agreement scheme, which constructs a secure channel between endpoint, gateway, and cloud server (CS). Compared with other schemes, this scheme has three characteristics: (1) According to the different computing resources of devices, gateways and CSs, a segmented differential authentication and secret key negotiation protocol is designed by using cryptographic primitives with different computing overheads; (2) after verification with the ProVerif tool, rigorous proof with the real‐or‐random (ROR) model, and informal analysis, the protocol has been proven to be secure, effectively guarding against typical threats; and (3) compared with the five most recent schemes, it can be seen that the protocol is at least 35% superior to other schemes in endpoint computational overhead, and it meets 10 security objectives, making it very suitable for application scenarios where endpoint resources are limited.

The inherent imaging properties of sensors result in two distinct differences between the data from the two modalities in RGB-T Salient Object Detection (SOD) tasks. Namely, differences in imaging effectiveness due to varying sensitivities to specific scenes and fundamental domain differences resulting from differences in reflecting scene characteristics. Existing methods primarily focus on pursuing unique cross-modal fusion designs to enhance model performance. However, not only do direct cross-modal fusion modes fail to improve the effectiveness of original features, but intricate cross-modal fusion designs also increase the domain differences between modalities, thereby resulting in suboptimal performance. Therefore, in this paper, we no longer insist on pursuing unique cross-modal fusion designs but instead contemplate how to enhance the effectiveness of original features within modalities (mitigating differences in imaging effectiveness) and utilize a concise cross-modal fusion mechanism (alleviating the impact of domain differences) to achieve satisfactory performance. In this spirit, we propose the Intra-modality Self-enhancement Mirror Network (ISMNet) for RGB-T salient object detection. The core of ISMNet is the proposed Intra-modality Cross-scale Self-enhancement Module (ICSM). The main insight of ICSM is to exploit saliency clues by modeling the correlation between intra-modality cross-scale features (which exhibit strong correlations and small domain differences), thereby enhancing the effectiveness of original multi-scale features within modalities. We employ the proposed novel paradigm to mirror-expand existing typical paradigms to obtain a more robust model architecture. Extensive experiments demonstrate that our proposed new architecture and the introduced universal Intra-modality Cross-scale Self-enhancement Module effectively improve the effectiveness of original features and promote the achievement of state-of-the-art performance.

In the Internet of Things (IoT) scenario, the device diversity and data sparsity present a significant challenge for malicious traffic detection, notably the “small sample problem” where insufficient data hampers the performance of the deep learning methods that depend on large volumes of labeled data for training. Transfer learning (TL) has the capability to transfer knowledge from a label-rich but heterogeneous domain to a label-sparse domain, making it a powerful tool for addressing challenges in IoT malicious traffic detection. To address these challenges, we introduce the EMTD-SSC model, a novel enhanced malicious traffic detection model that leverages TL under small sample conditions in IoT environments. Initially, our approach includes a comprehensive labeled data set that merges a small-scale IoT intrusion detection domain with the traditional intrusion detection domain to enrich semantic information transfer from the source to target domains. The EMTD-SSC model employs dual residual convolutional autoencoders for robust feature extraction and transfer, incorporating skip connections to expedite the model convergence and minimize information loss. Furthermore, to optimize transfer efficiency, we minimize the multilayer multi kernel maximum mean discrepancy (MLMK-MMD) across corresponding network layers, facilitating effective domain adaptation. Through unsupervised training and subsequent fine tuning on the target domain data, the model significantly enhances anomaly detection capabilities. Extensive experiments on the two well-known public data sets demonstrate that the EMTD-SSC model’s effectiveness, achieving an impressive 94.8% accuracy in the binary classification tasks.

Digital systems are growing in importance and computing hardware is growing more heterogeneous. Hardware design, however, remains laborious and expensive, in part due to the limitations of conventional hardware description languages (HDLs) like VHDL and Verilog. A longstanding research goal has been programming hardware like software, with high-level languages that can generate efficient hardware designs. This paper describes Kanagawa, a language that takes a new approach to combine the programmer productivity benefits of traditional High-Level Synthesis (HLS) approaches with the expressibility and hardware efficiency of Register-Transfer Level (RTL) design. The language’s concise syntax, matched with a hardware design-friendly execution model, permits a relatively simple toolchain to map high-level code into efficient hardware implementations.

Visual-based methods for rail surface defect inspection (RSDI) effectively improve the limitations of manual inspection, as they can intuitively display the locations and segmented areas of sensitive defects. The RGB-D RSDI task, which leverages the complementarity between RGB and depth (D) image information to enhance detection performance, has attracted widespread attention and achieved significant development. However, existing methods primarily depend on fully supervised training strategies that necessitate a substantial number of manually annotated pixel-level labels to supervise model training. Undoubtedly, extensive manual annotation is exceedingly time-consuming and labor-intensive, particularly considering the irregular shapes and textures of surface defects on rails, further compounding the burden of manual labeling. Therefore, in this paper, we aim to introduce the semi-supervised learning paradigm into this task. Towards the semi-supervised RGB-D RSDI task, a specific semi-supervised network for this task and an effective cross-modal fusion module are crucial to ensuring detection performance under the constraints of limited labeled samples. Thus, we propose a Depth-assisted Semi-Supervised RGB-D RSDI network (DSSNet) to simultaneously alleviate the annotation burden and achieve satisfactory detection performance. Specifically, adhering to the consistency training paradigm, we construct a semi-supervised RGB-D RSDI architecture for this task by optimizing structures, perturbation mechanisms, loss settings, etc. Furthermore, we propose a Depth-assisted Multi-scale Cross-modal Fusion Module (DMCFM) that conducts multi-scale exploration and cross-modal complementary fusion with the assistance of depth. Comprehensive experiments demonstrate that, compared to the latest 14 state-of-the-art fully supervised methods, the proposed DSSNet achieves highly competitive results while effectively alleviating an 80$\%$annotation burden.

There is no doubt that the rational and effective use of visible and thermal infrared image data information to achieve cross-modal complementary fusion is the key to improving the performance of RGB-T salient object detection (SOD). A meticulous analysis of the RGB-T SOD data reveals that it mainly consists of three scenarios in which both modalities (RGB and T) have a significant foreground and only a single modality (RGB or T) is disturbed. However, existing methods are obsessed with pursuing more effective cross-modal fusion based on treating both modalities equally. Obviously, the subjective use of equivalence has two significant limitations. Firstly, it does not allow for practical discrimination of which modality makes the dominant contribution to performance. While both modalities may have visually significant foregrounds, differences in their imaging properties will result in distinct performance contributions. Secondly, in a specific acquisition scenario, a pair of images with two modalities will contribute differently to the final detection performance due to their varying sensitivity to the same background interference. Intelligibly, for the RGB-T saliency detection task, it would be more reasonable to generate exclusive weights for the two modalities and select specific fusion mechanisms based on different weight configurations to perform cross-modal complementary integration. Consequently, we propose a weighted guided optional fusion network (WGOFNet) for RGB-T SOD. Specifically, a feature refinement module is first used to perform an initial refinement of the extracted multilevel features. Subsequently, a weight generation module (WGM) will generate exclusive network performance contribution weights for each of the two modalities, and an optional fusion module (OFM) will rely on this weight to perform particular integration of cross-modal information. Simple cross-level fusion is finally utilized to obtain the final saliency prediction map. Comprehensive experiments on three publicly available benchmark datasets demonstrate the proposed WGOFNet achieves superior performance compared with the state-of-the-art RGB-T SOD methods. The source code is available at: https://github.com/WJ-CV/WGOFNet .

Sharded blockchain offers scalability, decentralization, immutability, and linear improvement, making it a promising solution for addressing the trust problem in large-scale collaborative IoT. However, a high proportion of cross-shard transactions can severely limit the performance of decentralized blockchain. Furthermore, the dynamic assemblage characteristic of collaborative sensing in sharded blockchain is often ignored. To overcome these limitations, we propose HMMDShard, a dynamic blockchain sharding scheme based on the Hidden Markov Model. HMMDShard leverages fine-grained blockchain sharding and fully embraces the dynamic assemblage characteristic of IoT collaborative sensing. By integrating the Hidden Markov Model, we achieve adaptive dynamic incremental updating of blockchain shards, effectively reducing cross-shard transactions across all shards. We conduct a comprehensive analysis of the security issues and properties of HMMDShard, and evaluate its performance through the implementation of a system prototype. The results demonstrate that HMMDShard significantly reduces the proportion of cross-shard transactions and outperforms other baselines in terms of system throughput and transaction confirmation latency.

With the advent of the Internet of Things (IoT), crowdsensing, as a new emerging application of the IoT that employs ubiquitous mobile users with smartphones for data collection and processing, has further deepened our knowledge. However, the problems of the current crowdsensing systems regarding system security, user privacy, and user payment (UP) raise serious privacy and security concerns, which affect participants’ adoption of the system. The Blockchain technology allows for nondeterministic multiple parties to interact with each other anonymously in a network that is not fully trusted. In this article, we propose a new decentralized crowdsensing system, calledCrowdHB. Unlike other blockchain-based crowdsensing systems,CrowdHBadopts a hybrid blockchain architecture and uses smart contracts to achieve location privacy preservation and ensure data quality while improving the system performance. Furthermore, to optimize task assignments to mobile users, we propose a location privacy-preserving optimization mechanism (LPPOM) and the approach of consistency optimization (ACO) to achieve a tradeoff between user privacy and system performance. The extensive experimental results show that the proposedCrowdHBoutperforms the other crowdsensing systems in terms of task success rate and performance for a large number of mobile users and tasks.

With the popularization of intelligent terminals, especially current trends, such as “Industrie 4.0” and the Internet of Things, mobile crowdsensing is becoming one of the promising applications built on smart devices in mobile networks. However, the existing mobile crowdsensing models are mostly based on a centralized platform, which is not fully trusted in reality and results in the existence of fraud and other security problems. Furthermore, the data quality collected through crowdsensing is varied, and the location privacy is difficult to guarantee, especially at the worker selection stage. To solve these two problems, an effective blockchain-based location-privacy-preserving crowdsensing model, CrowdBLPS, is proposed in this article. First, the idea of a blockchain is introduced into this model. The decentralized structure and the consensus approach are applied to realize the nonrepudiation and nontampering of information. Second, to improve the data sensing quality and protect worker privacy, a two-stage approach, including the preregistration stage and the final selection stage, is proposed. Finally, we further implement a prototype on the Ethereum public testing network, and the experimental results show the feasibility, availability, and reliability of CrowdBLPS.

In the research of location privacy protection, the existing methods are mostly based on the traditional anonymization, fuzzy and cryptography technology, and little success in the big data environment, for example, the sensor networks contain sensitive information, which is compulsory to be appropriately protected. Current trends, such as “Industrie 4.0” and Internet of Things (IoT), generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, we propose a location privacy protection method that satisfies differential privacy constraint to protect location data privacy and maximizes the utility of data and algorithm in Industrial IoT. In view of the high value and low density of location data, we combine the utility with the privacy and build a multilevel location information tree model. Furthermore, the index mechanism of differential privacy is used to select data according to the tree node accessing frequency. Finally, the Laplace scheme is used to add noises to accessing frequency of the selecting data. As is shown in the theoretical analysis and the experimental results, the proposed strategy can achieve significant improvements in terms of security, privacy, and applicability.

Summary The accumulation of massive data generates the new concept of big data. The relationships hidden in big data can bring great benefits, which have attracted public attentions. Meanwhile, the challenges of big data security are also more serious than ever. Privacy disclosure is one of the most concerned problems, and the privacy protection of big data is more difficult than traditional information protection. The technology of data publishing anonymous protection can provide privacy protection with the respect of data releasing. K‐anonymity and L‐diversity are two kinds of anonymity model. Their main idea is to generalize the value of quasi‐identifier and make the data accord with the model. In this paper, we propose the improved model which integrate K‐anonymity with L‐diversity and can solve the problem of imbalanced sensitive attribute distribution. K‐member clustering algorithm can translate the problem of anonymity into the problem of clustering and find a set of equivalence classes in which the records will be generalized to the same value. We utilize K‐member clustering algorithm to realize the improved anonymity model which can reduce the algorithm execution time and information loss. The integration of anonymity model and clustering algorithm makes the generalization process more efficient, which is particularly important for big data. Copyright © 2016 John Wiley & Sons, Ltd.

This paper presents a system-level Network-on-Chip simulation platform integrating the transaction-level performance model of NoC components and their architecture-level energy models. The transaction-level model written in SystemC enables fast simulation speed and the architectural energy model estimates communication energy, including both dynamic and leakage, dissipating on routers and links through the transaction-level simulation. This power model supports temporal power profiling for each NoC component and spatial power snapshots for the whole NoC, making it easy to inspect the power implications under application workloads. Applying this energy model on 8 deep sub-micron CMOS processes from 180nm to 45nm, we reveal an average 2.8X leakage power increase for each technology evolution. With temporal and spatial profiling for burst-mode applications, the power hungry portions in both time- and space-domains can be identified, and in turn it provides useful information for the energy-aware NoC design space exploration for the future nanoscale IC technologies.This paper presents a system-level Network-on-Chip simulation platform integrating the transaction-level performance model of NoC components and their architecture-level energy models. The transaction-level model written in SystemC enables fast simulation speed and the architectural energy model estimates communication energy, including both dynamic and leakage, dissipating on routers and links through the transaction-level simulation. This power model supports temporal power profiling for each NoC component and spatial power snapshots for the whole NoC, making it easy to inspect the power implications under application workloads. Applying this energy model on 8 deep sub-micron CMOS processes from 180nm to 45nm, we reveal an average 2.8X leakage power increase for each technology evolution. With temporal and spatial profiling for burst-mode applications, the power hungry portions in both time- and space-domains can be identified, and in turn it provides useful information for the energy-aware NoC design space exploration for the future nanoscale IC technologies.This paper presents a system-level Network-on-Chip simulation platform integrating the transaction-level performance model of NoC components and their architecture-level energy models. The transaction-level model written in SystemC enables fast simulation speed and the architectural energy model estimates communication energy, including both dynamic and leakage, dissipating on routers and links through the transaction-level simulation. This power model supports temporal power profiling for each NoC component and spatial power snapshots for the whole NoC, making it easy to inspect the power implications under application workloads. Applying this energy model on 8 deep sub-micron CMOS processes from 180nm to 45nm, we reveal an average 2.8X leakage power increase for each technology evolution. With temporal and spatial profiling for burst-mode applications, the power hungry portions in both time- and space-domains can be identified, and in turn it provides useful information for the energy-aware NoC design space exploration for the future nanoscale IC technologies.

This paper presents a system-level Network-on-Chip modeling framework that integrates transaction-level model and analytical wire model for design space exploration. It enables the analysis of influence of physical wire properties on the system performance and power dissipation in early design stages. SystemC provides the infrastructure to integrate transaction-level model and low-level models. By utilizing approximate timing, different temporal granularity can be used, leading to fast simulation speed. Six deep-submicron CMOS processes from 180 nm to 45 nm are used to evaluate the performance/power of NoC. Additionally, temporal and spatial NoC power analysis under different traffic conditions provides an effective basis for power/thermal optimization and design space exploration in early design stages.

We propose the architecture of a novel distributed SAT solver, which is composed of a control unit (CU) and multiple implication units (IU). In this model, CU handles the control-intensive tasks such as clause partitioning, decision and backtracking, and IUs process implications, which are computation-intensive. This model has been modeled with SystemC successfully and simulation results show that it has the potential to get >35 speedup compared to software solvers, and moreover, it does not need to re-compile implication circuits for different instances, in contrast to other hardware SAT solvers.