Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic images across medical institutions is usually prohibited due to patients’ privacy concerns. This causes the issue of insufficient data sets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received local model updates trained by clients without exchanging clients’ local data. Nevertheless, the default setting of federated learning introduces a huge communication cost of transferring model updates and can hardly ensure model performance when severe data heterogeneity of clients exists. To improve communication efficiency and model performance, in this article, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyze medical diagnostic images. Furthermore, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion based on participating clients’ training time. In addition, we summarize a category of medical diagnostic image data sets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency, and fault tolerance.

Edge computing provides high-class intelligent services and computing capabilities at the edge of the networks. The aim is to ease the backhaul impacts and offer an improved user experience. However, the edge artificial intelligence exacerbates the security of the cloud computing environment due to the dissociation of data, access control, and service stages. In order to prevent users from carrying out lateral movement attacks in an edge-cloud computing environment, in this paper we propose a real-time lateral movement detection method, named CloudSEC, based on an evidence reasoning network for the edge-cloud environment. First, the concept of vulnerability correlation is introduced. Based on the vulnerability knowledge and environmental information of the network system, the evidence reasoning network is constructed, and the lateral movement reasoning ability provided by the evidence reasoning network is then used. The experiment results show that CloudSEC provides a strong guarantee for the rapid and effective evidence investigation, as well as real-time attack detection.

The advent of 5G technologies enables highly anticipated prospect of the internet of things in smart cities. Smart terminals become a necessity with increasing number of applications and tasks. The limited computational resources and constrained battery energy at terminals compel tasks to be offloaded for execution sometimes. For time-sensitive and safety-critical tasks, fog computing is the preferred option instead of cloud computing, since fog computing brings the computing resources to the edge of networks and thus has relatively shorter response latency. In this context, Vehicular fog computing (VFC) that supplements fog computing by contributing vehicular computing resources has been attracting increasing attention in recent years. However, a few of challenging issues regarding task offloading in VFC still need to be addressed. For example, decision making for task allocation and response latency optimization are not straightforward, since on one hand the connection between vehicles and end users is usually short or intermittent; on the other hand the multi-hop inter-vehicle task forwarding is time-consuming and thus prone to packet loss. To address the latency related issue, we in this paper propose a three-layer architecture of VFC to schedule the tasks offloaded from MDs. RSU is endowed with computing facilities and storing resources such that it can act as not only a gateway to the remote cloud center but also a centralized infrastructure responsible for task scheduling among vehicles. A greedy heuristic based scheduling strategy and algorithms are proposed in the next. Last, an extensive series of experiments are carried out to investigate the proposed scheduling strategy. The results show that the proposed scheduling strategy outperforms other well known heuristics based scheduling strategies such as Min-Min and Max-Min and thus has wide prospects.

An experience of finding a vacant parking slot can be very stressful in densely populated areas, especially in peak hours. Such parking process takes a long time, wastes significant gasoline, and emits extra vehicle exhaust that harms the environment. Smart parking, aiming to assist drivers in finding desirable parking slots more efficiently through information and communication technologies such as vehicle ad hoc networks (VANETs), has received extensive attention recently. Current VANETs-based parking slot allocations cannot provide a fully satisfactory solution, because vehicle communication devices-onboard units-and roadside units lack computational capabilities to perform humanized and accurate service provisioning, such as real-time parking slots information and probabilistic prediction on future parking slots. Therefore, we, in this paper, propose a fog computing-based smart parking architecture to improve smart parking in real time. Fog nodes deployed at parking lots, cooperating with each other, enable realtime parking slot information provisioning as well as parking requests processing. The cloud center can further enhance smart parking capability by enforcing global optimization on parking requests allocation. The experimental results of our approaches show higher efficiency compared with other parking strategies. The proposed fog computing-based smart parking can lower the average parking cost and minimize gasoline wastes and vehicle exhaust emission.

This article presents a novel extended reality (XR) and deep-learning-based Internet-of-Medical-Things (IoMT) solution for the COVID-19 telemedicine diagnostic, which systematically combines virtual reality/augmented reality (AR) remote surgical plan/rehearse hardware, customized 5G cloud computing and deep learning algorithms to provide real-time COVID-19 treatment scheme clues. Compared to existing perception therapy techniques, our new technique can significantly improve performance and security. The system collected 25 clinic data from the 347 positive and 2270 negative COVID-19 patients in the Red Zone by 5G transmission. After that, a novel auxiliary classifier generative adversarial network-based intelligent prediction algorithm is conducted to train the new COVID-19 prediction model. Furthermore, The Copycat network is employed for the model stealing and attack for the IoMT to improve the security performance. To simplify the user interface and achieve an excellent user experience, we combined the Red Zone’s guiding images with the Green Zone’s view through the AR navigate clue by using 5G. The XR surgical plan/rehearse framework is designed, including all COVID-19 surgical requisite details that were developed with a real-time response guaranteed. The accuracy, recall, F ₁ -score, and area under the ROC curve (AUC) area of our new IoMT were 0.92, 0.98, 0.95, and 0.98, respectively, which outperforms the existing perception techniques with significantly higher accuracy performance. The model stealing also has excellent performance, with the AUC area of 0.90 in Copycat slightly lower than the original model. This study suggests a new framework in the COVID-19 diagnostic integration and opens the new research about the integration of XR and deep learning for IoMT implementation.

A digital twin-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier generative adversarial network (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon’s helmet in the application layer using the digital twin-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92, and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for digital twin-based surgical implementation.