Small object detection in remote sensing imagery is a challenging task due to the small size of targets, complex background, and low contrast, which makes achieving high precision difficult. To enhance the accuracy of detection, this study proposes a novel oriented object detection model with three significant innovations: Firstly, a lightweight feature extraction network is designed to achieve efficient feature representation at a reduced computational cost, which is particularly effective for the recognition of small targets in remote sensing imagery. Secondly, a Feature-Focused Channel Attention (FFCA) is introduced that enhances the model’s ability to focus on small target areas by combining spatial and channel attention, enhancing the model’s capacity to capture and represent features more effectively. Lastly, an attention-guided multi-scale feature fusion module is proposed to integrate features from different levels, which substantially boosts the model’s ability to accurately detect small-scale objects, especially in remote sensing scenarios with vast fields of view and complex backgrounds. The experimental outcomes validate that our model achieves the best detection performance on two benchmark public datasets for remote sensing imagery, confirming its effectiveness and practicality in remote small object detection tasks.

A large-scale Internet of Things (IoT) system based on sharded blockchain technology faces challenges, such as excessively high Cross-Shard Transaction (CST) ratios and imbalanced internal consensus. Current solutions include designing sharding protocols and consensus algorithms. However, these previous approaches solely focus on the outcomes of sharding, neglecting the intricate consensus costs incurred during the sharding process. This seriously affects the transaction latency of the system. Therefore, in this paper, we propose a latency optimization scheme. This scheme includes the sharding algorithm based on Weighting in K-means (W-K-means) clustering (namely WK-shard) and the Best Stable Committee (BSC) algorithm, aimed at addressing challenges in committee formation latency and internal consensus latency during the sharding process. Specifically, the WK-shard algorithm utilizes W-K-means clustering to balance the relationship between CSTs and intra-shard computility allocation. This ensures load balancing across shards while reducing CSTs, providing a strong basis for user nodes to select appropriate shards. Meanwhile, the BSC algorithm utilizes Markov chains to solve for the steady-state committee. The optimal utility of problem is explored through the dynamic state transitions of the schemes selected by different committees. A good selection scheme can reduce the total interval time of the system, effectively resolve the problem of laggards in internal consensus. We analyze the transaction lantency and validity degree of the latency optimization scheme through experiments, and compare it with other algorithms. The experimental results show that the proposed WK-shard algorithm reduces the committee formation latency by 15%, and the average validity degree of the BSC algorithm increases by 0.4 Transaction Per Second (TPS).

Due to their versatility and ease of movement, Unmanned Aerial Vehicles (UAVs) have become crucial tools in data collection for Wireless Sensor Networks (WSNs). While numerous UAV-based solutions exist, the focus often needs to be on optimizing flight trajectories and managing energy use, sometimes neglecting key factors affecting channel quality. In this article, we introduce a collaborative design framework designed to alleviate channel quality degradation caused by UAV flight distance in three-dimensional spaces. Our approach jointly optimizes UAV power schemes, positions, and flight trajectories. Firstly, we start by introducing a novel enhancing power model developed explicitly for rotary-wing UAVs gathering data, utilizing an alternating optimization method to achieve locally optimal solutions. Next, we frame an optimization problem aimed at maximizing the total average collection rate while achieving approximate optimal position relationships among UAVs. Additionally, we propose a new trajectory optimization model based on the Steiner Minimal Tree (SMT) concept, which is called the Circumcircle Steiner Minimal Tree Problem with Neighborhood (CSMTPN). Finally, we confirm our theoretical insights and numerical outcomes through extensive simulations demonstrating our framework’s effectiveness.

Finding more specific subcategories within a larger category is the goal of fine-grained image classification (FGIC), and the key is to find local discriminative regions of visual features. Most existing methods use traditional convolutional operations to achieve fine-grained image classification. However, traditional convolution cannot extract multi-scale features of an image and existing methods are susceptible to interference from image background information. Therefore, to address the above problems, this paper proposes an FGIC model (Attention-PCNN) based on hybrid attention mechanism and pyramidal convolution. The model feeds the multi-scale features extracted by the pyramidal convolutional neural network into two branches capturing global and local information respectively. In particular, a hybrid attention mechanism is added to the branch capturing global information in order to reduce the interference of image background information and make the model pay more attention to the target region with fine-grained features. In addition, the mutual-channel loss (MC-LOSS) is introduced in the local information branch to capture fine-grained features. We evaluated the model on three publicly available datasets CUB-200-2011, Stanford Cars, FGVC-Aircraft, etc. Compared to the state-of-the-art methods, the results show that Attention-PCNN performs better.

As one of the essential steps to secure government data sharing, Identity Authentication (IA) plays a vital role in the processing of large data. However, the centralized IA scheme based on a trusted third party presents problems of information leakage and single point of failure, and those related to key escrow. Therefore, herein, an effective IA model based on multiattribute centers is designed. First, a private key of each attribute of a data requester is generated by the attribute authorization center. After obtaining the private key of attribute, the data requester generates a personal private key. Second, a dynamic key generation algorithm is proposed, which combines blockchain and smart contracts to periodically update the key of a data requester to prevent theft by external attackers, ensure the traceability of IA, and reduce the risk of privacy leakage. Third, the combination of blockchain and interplanetary file systems is used to store attribute field information of the data requester to further reduce the cost of blockchain information storage and improve the effectiveness of information storage. Experimental results show that the proposed model ensures the privacy and security of identity information and outperforms similar authentication models in terms of computational and communication costs.