Accurate and efficient road damage detection is critical for maintaining urban infrastructure and ensuring public safety in Intelligent Internet of Things (IIoT) systems. There remains a significant challenge to achieving a balance between detection accuracy and real-time inference on resource-constrained edge devices despite advances in deep learning. This paper addresses this gap by enhancing the YOLOv9c object detection framework with two distinct backbone architectures: MobileNet V3-Small, which is a lightweight convolutional neural network optimized for edge deployment, and Swin Transformer, which is a hierarchical vision transformer that captures rich contextual features. We present a systematic, dual-backbone performance benchmark that quantifies the critical trade-off between computational efficiency and detection precision, which is essential for guiding IIoT deployment strategies. We conducted experiments on the Street View Road Damage Detection (SVRDD) dataset to evaluate detection accuracy, computational efficiency, and latency. The MobileNet backbone achieves the highest mean Average Precision ([email protected]) of 74.0% (a 1.5% gain over baseline) and recall of 68.8% (a 7.3% gain over baseline), demonstrating improved accuracy while maintaining a low inference time on a baseline GPU, indicating its suitability for deployment on IoT edge devices. Importantly, the MobileNet variant reduces the parameter count from 25.6 M to 2.54 M and the Giga Floating-point Operations Per Second (GFLOPs) from 102.3 to 0.49, making it more efficient for IIoT edge devices. Both backbones performed better than the YOLOv9c baseline model in terms of accuracy, thus providing scalable and practical solutions for real-time infrastructure monitoring. These findings contribute to the development of intelligent, efficient, and scalable object detection systems tailored for smart city and IIoT environments.

Federated learning (FL) enables privacy-preserving analytics at the medical IoT (IoMT) edge but is vulnerable to model poisoning and distribution shift. We presentATTA-FL-Lite, a lightweight aggregation rule that admits a client update only when three tests are jointly satisfied: (i) scale conformity via a median–absolute–deviation (MAD)z-score, (ii) directional alignment via cosine similarity to a coordinate-wise median reference, and (iii) non-degradation via a validation-lossz-score computed on a small, centrally held clean set. If no update passes, a coordinate-wise median fallback is used. We provide sub-Gaussian tail bounds for the loss test and an expected one-step descent bound forL-smooth objectives under benign mean-alignment and an accepted-set composition assumption; the analysis does not require a positive cosine threshold. Experiments on MNIST, Fashion-MNIST, and PathMNIST, under IID and non-IID partitions with up to 40% adversaries across four attack families, show that ATTA-FL-Lite maintains accuracy representatively ≈ 0.78–0.98 across Tiny/Small/Medium CNNs, reliably filters magnitude/noise attacks, and remains competitive against sign-flip. Server runtime scales approximately linearly with the number of participating clients at fixed model and validation sizes. These results indicate that ATTA-FL-Lite offers practical robustness for FL in resource-constrained IoMT deployments without cryptographic overhead or trusted root data beyond a small validation set.

Coverage optimization in Wireless Sensor Networks is a fundamental yet NP-hard problem that directly affects monitoring quality and efficiency. Existing solutions mainly rely on meta-heuristic algorithms that use fitness-based evaluations, which often incur high computational overhead, slow convergence, and limited scalability, particularly in real-time or high-precision monitoring scenarios. In this paper, we examine the relationship between effective coverage area and redundant distances in an analytical manner. We then propose reformulating WSN coverage optimization as a Distance-Gradient based convex optimization problem, which can be subsequently solved using the first-order Gradient Descent algorithm or the second-order quasi-Newton algorithm. Extensive comparative experiments against five representative meta-heuristic methods, the Virtual Force Algorithm (VFA) and a general convex optimization algorithm (CVX), demonstrate that our approach achieves near-optimal coverage while preserving network connectivity within milliseconds, highlighting its advantages over existing methods for WSNs coverage optimization.

Federated learning (FL) has emerged as a privacy-preserving paradigm for collaborative intrusion detection in networked environments. However, it remains vulnerable to Poisoning Attacks (PA) wherein malicious clients can corrupt the global model through deceptive updates. To address this, we propose PoisonShield-FL-NIDS, a robust FL-based intrusion detection system that integrates client-side anomaly filtering with trust-aware aggregation to defend against poisoned contributions. Experimental evaluation under varying levels of adversarial influence demonstrates that PoisonShield-FL-NIDS achieves superior performance across key metrics, attaining 93% accuracy, 91% precision, 94% recall, and an AUC of 0.96, while maintaining a low robustness index RI < 0.05 even with 30% compromised clients. Compared to baseline FL models such as FL-CNN and FedACNN, our framework demonstrates faster convergence and higher resilience with only a marginal increase in communication overhead.

Long-tailed data are a special type of multiclass imbalanced data with a very large amount of minority/tail classes that have a very significant combined influence. Long-tailed learning (LTL) aims to build high-performance models on datasets with long-tailed distributions that can identify all the classes with high accuracy, in particular the minority/tail classes. It is a cutting-edge research direction that has attracted a remarkable amount of research effort in the past few years. In this article, we present a comprehensive survey of the latest advances in long-tailed visual learning. We first propose a new taxonomy for LTL, which consists of eight different dimensions, including data balancing, neural architecture, feature enrichment, logits adjustment, loss function, bells and whistles, network optimization, and posthoc processing techniques. Based on our proposed taxonomy, we present a systematic review of LTL methods, discussing their commonalities and alignable differences. We also analyze the differences between imbalance learning and LTL. Finally, we discuss prospects and future directions in this field.

Data scarcity is a very common real-world problem that poses a major challenge to data-driven analytics. Although a lot of data-balancing approaches have been proposed to mitigate this problem, they may drop some useful information or fall into the overfitting problem. Generative Adversarial Network (GAN) based data synthesis methods can alleviate such a problem but lack of quality control over the generated samples. Moreover, the latent associations between the attribute set and the class labels in a relational data cannot be easily captured by a vanilla GAN. In light of this, we introduce an end-to-end self-training scheme (namely, Quality-Aware Self-Training) for rare relational data synthesis, which generates labeled synthetic data via pseudo labeling on GAN-based synthesis. We design a semantic pseudo labeling module to first control the quality of the generated features/samples, then calibrate their semantic labels via a classifier committee consisting of multiple pre-trained shallow classifiers. The high-confident generated samples with calibrated pseudo labels are then fed into a semantic classification network as augmented samples for self-training. We conduct extensive experiments on 20 benchmark datasets of different domains, including 14 industrial datasets. The results show that our method significantly outperforms state-of-the-art methods, including two recent GAN-based data synthesis schemes. Codes are available at https://github.com/yaxinhou/QAST.

In recent years, deep learning based techniques have been successfully applied to medical image segmentation, which plays an important role in intelligent lesion analysis and disease diagnosis. At present, the mainstream segmentation models are primarily based on the U-Net model for extracting local features through multi-layer convolution, which lacks global information and the multi-scale semantic information interaction between the Encoder and Decoder process, leading to sub-optimal segmentation performance. To address such issues, in this work we propose a new medical image segmentation network, namely SACA-UNet, which improves the U-Net model via the self-attention and cross atrous spatial pyramid pooling (Cross-ASPP) mechanisms. In specific, SACA-UNet first utilizes the self- attention mechanism to capture the global feature, it next devises a Cross-ASPP module to extract and fuse features of varying reception fields to prompt multi-scale semantic interaction. We evaluate the segmentation performance of our proposed model on four benchmark datasets including the ISIC2018, BUSI, CVC- ClinicDB, and COVID-19 datasets, in terms of both the Dice coefficient and IoU metrics. Experimental results demonstrate that SACA-UNet remarkably outperforms the baseline methods.