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.

The rapid expansion of the internet-enabled toys, informally referred to as the Internet of Toys (IoToys), has created new educational and communication opportunities, and at the same time, increased privacy and security threats to children. This study presents a fuzzy system-driven scoring system that will provide a list of privacy and security maturity of IoToys through the interpretation of the diverse legal, technical, and usability signals to an understandable moral trust evaluation. The system proposed can integrate five fundamental inputs using a Mamdani-based fuzzy rule block and centroid defuzzification to generate a limited 0-100 trust score in the form of seven linguistic variables. The suggested system comprises a thorough structure of membership functions, building of rule base of five inputs, and performance of the defuzzification workflow. As far as case study assessments reveal, insufficient parent permission and an imperfect data storage process greatly reduce the trust scores. Nevertheless, improved levels of communication integrity and the use of secure codes make the devices enter the highest levels of safety. The suggested system provides caregivers, regulators, and manufacturers with a strong prioritization of security enhancement mechanisms, a design comparative mechanism, and a premarket risk labeling mechanism.

Federated Learning (FL) enables privacy-preserving collaborative model training across distributed and decentralized clients without sharing raw data; however, its reliability is severely challenged by the joint presence of non-independent and identically distributed (non-IID) data heterogeneity and adversarial client behavior. Existing robust FL approaches often address these issues in isolation, leading to degraded performance when heterogeneous data distributions amplify the impact of adversarial attacks. In this work, we systematically investigate how non-IID data characteristics exacerbate vulnerabilities to Byzantine failures, model poisoning, backdoor insertion, and gradient inversion attacks in federated learning systems. Extensive experiments on CIFAR-10, MNIST, and FEMNIST demonstrate that data heterogeneity can increase adversarial attack success rates by up to 47% compared to IID settings. To mitigate these challenges, we propose an adaptive adversarial defense framework that integrates Jensen–Shannon divergence-based client clustering, hybrid robust aggregation, and multi-stage differential privacy mechanisms. The proposed approach achieves up to 94.3% model accuracy while tolerating up to 40% malicious clients, outperforming FedAvg, Krum, and Trimmed Mean by 12– 23% under diverse adversarial scenarios. These results highlight the importance of jointly addressing data heterogeneity and adversarial robustness, and provide practical insights for designing dependable federated learning systems in emerging distributed computing environments.

In IoT-enabled smart infrastructure, accurate and real-time road damage detection is crucial for enhancing road safety and optimizing maintenance processes. However, detecting road damage in complex and dynamic environments presents significant challenges, such as varying lighting conditions, diverse damage types, and the need for fast processing to enable real-time decision-making. This study introduces an advanced approach utilizing the YOLOv9s-Fusion model to overcome these challenges. Leveraging the RDD2022 dataset, which comprises 1976 annotated images of road damage from China, we employ comprehensive data preprocessing to create optimal conditions for model training. The YOLOv9s-Fusion model integrates innovative features, including a Transformer-based auxiliary module and enhanced feature extraction layers, specifically designed to detect fine-grained damage patterns accurately. Experimental results demonstrate that the model outperforms existing approaches, achieving notable improvements in mean average precision (mAP) and F1-score. Ablation studies further validate the impact of our modifications, highlighting the model’s robustness in real-time detection across diverse conditions. This IoT-centric approach sets a new standard for autonomous road damage detection, significantly advancing vehicle navigation and smart infrastructure management capabilities.

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision-making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 dataset collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors; Faster R-CNN ResNet101 and one-stage detectors; SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mAP of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance.