Feihong Xu

The Internet of Medical Things represents a pivotal application of Internet of Things technology in Healthcare 4.0, offering substantial practical benefits in enhancing medical quality, reducing costs, and minimizing errors. In history, researchers have proposed numerous privacy-preserving authentication schemes to safeguard Internet of Medical Things applications. Nevertheless, due to design shortcomings, existing solutions still encounter significant security and performance challenges, rendering them impractical for real-world use. To resolve the issue, this work introduces a novel practical Internet of Medical Things-based smart healthcare system, leveraging a pairing-free certificateless signature scheme and hash-based message authentication code. Through formal security proofs under standard cryptographic assumptions, and performance analysis, our scheme demonstrates enhanced security while maintaining desirable computational and communication efficiency.

The large-scale deployment of Internet of Things (IoT) technology across various aspects of daily life has significantly propelled the intelligent development of society. Among them, the integration of IoT and named data networks (NDNs) reduces network complexity and provides practical directions for content-oriented network design. However, ensuring data integrity in NDN-IoT applications remains a challenging issue. Very recently, Wang et al. (Entropy, 27(5), 471(2025)) designed a certificateless aggregate signature (CLAS) scheme for NDN-IoT environments. Wang et al. stated that their construction was provably secure under various types of security attacks. Using theoretical analysis methods, in this work, we reveal that their CLAS design fails to meet unforgeability, a core security requirement for CLAS schemes. In particular, we demonstrate that their scheme is vulnerable to a malicious public-key replacement attack, enabling an adversary to produce authentic signatures for arbitrary fraudulent messages. Therefore, Wang et al.’s design cannot achieve its goal. To address the issue, we systematically examine the root causes behind the vulnerability and propose a security-enhanced CLAS construction for NDN-IoT environments. We prove the security of our improved design under the standard security assumption and also analyze its practical performance by comparing the computational and communication costs with several related works. The comparison results show the practicality of our design.

Safety helmets are essential in various indoor and outdoor workplaces, such as metallurgical high-temperature operations and high-rise building construction, to avoid injuries and ensure safety in production. However, manual supervision is costly and prone to lack of enforcement and interference from other human factors. Moreover, small target object detection frequently lacks precision. Improving safety helmets based on the helmet detection algorithm can address these issues and is a promising approach. In this study, we proposed a modified version of the YOLOv5s network, a lightweight deep learning-based object identification network model. The proposed model extends the YOLOv5s network model and enhances its performance by recalculating the prediction frames, utilizing the IoU metric for clustering, and modifying the anchor frames with the K-means++ method. The global attention mechanism (GAM) and the convolutional block attention module (CBAM) were added to the YOLOv5s network to improve its backbone and neck networks. By minimizing information feature loss and enhancing the representation of global interactions, these attention processes enhance deep learning neural networks’ capacity for feature extraction. Furthermore, the CBAM is integrated into the CSP module to improve target feature extraction while minimizing computation for model operation. In order to significantly increase the efficiency and precision of the prediction box regression, the proposed model additionally makes use of the most recent SIoU (SCYLLA-IoU LOSS) as the bounding box loss function. Based on the improved YOLOv5s model, knowledge distillation technology is leveraged to realize the light weight of the network model, thereby reducing the computational workload of the model and improving the detection speed to meet the needs of real-time monitoring. The experimental results demonstrate that the proposed model outperforms the original YOLOv5s network model in terms of accuracy (Precision), recall rate (Recall), and mean average precision (mAP). The proposed model may more effectively identify helmet use in low-light situations and at a variety of distances.