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 (CSTs) 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 (HMM). HMMDShard leverages fine-grained blockchain sharding and fully embraces the dynamic assemblage characteristic of IoT collaborative sensing. By integrating the HMM, we achieve adaptive dynamic incremental updating of blockchain shards, effectively reducing CSTs 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 CSTs 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, called CrowdHB . Unlike other blockchain-based crowdsensing systems, CrowdHB adopts 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 proposed CrowdHB outperforms the other crowdsensing systems in terms of task success rate and performance for a large number of mobile users and tasks.

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.

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.

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 .