IoMT : A Medical Resource Management System Using Edge Empowered Blockchain Federated Learning

Publisher Copyright: IEEEAs data sharing on the Internet of Medical Things (IoMT) become more complicated, the problems of divergent interests, unregulated policies, privacy and security, and the resource constraints of data owners have drawn the attention of researchers. To address the problems, this paper provides resource management in the IoMT using a proposed edge-empowered blockchain federated learning system. Also, an improved linear regressor model is proposed as the global learning model for the federated learning system. Gradient parameters are encrypted using Paillier encryption on the federated server side before they are shared by the federated clients. Blockchain is deployed to provide new security features for IoMT and edge computing. Moreover, all transactions of IoMT and edge devices are stored on the blockchain for secure cataloguing and auditing. Edge computing is employed to handle complex computing tasks on behalf of IoMT devices. Extensive simulations are conducted to validate the efficacy of the proposed systemmodel. The results show that computing costs are minimized while still achieving the benefits of security and privacy in the proposed system. Furthermore, security analysis shows that the proposed system is protected from security attacks.Peer reviewe

Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey

Semantic segmentation in 3D meshes is the classification of its constituent element(s) into specific classes or categories. Using the powerful feature extraction abilities of deep neural networks (DNNs), significant results have been obtained in the semantic segmentation of various remotely sensed data formats. With the increased utilization of DNNs to segment remotely sensed data, there have been commensurate in-depth reviews and surveys summarizing the various learning-based techniques and methodologies that entail these methods. However, most of these surveys focused on methods that involve popular data formats like LiDAR point clouds, synthetic aperture radar (SAR) images, and hyperspectral images (HSI) while 3D meshes hardly received any attention. In this paper, to our best knowledge, we present the first comprehensive and contemporary survey of recent advances in utilizing deep learning techniques for the semantic segmentation of urban-scale 3D meshes. We first describe the different approaches employed by mesh-based learning methods to generalize and implement learning techniques on the mesh surface, and then describe how the element-wise classification tasks are achieved through these methods. We also provide an in-depth discussion and comparative analysis of the surveyed methods followed by a summary of the benchmark large-scale mesh datasets accompanied with the evaluation metrics for assessing the segmentation performance of the methods. Finally, we summarize some of the contemporary problems of the field and provide future research directions that may help researchers in the community