Currently, the main challenges of the frameworks for healthcare monitoring are as follows: minimising latency, especially for delay-sensitive diseases such as sudden heart disease; identifying health situation in a timely and accurate manner when correlating physiological indicators and context information; reducing the risk of exposure because health data are highly private. In response to the above, this paper proposes a framework for 5G-secure-smart healthcare monitoring (5GSS) to achieve the following goals: fast and accurate identification of context-aware health situation, blockchain-based secure data sharing, and low-latency services for emergent patients. The framework consists of a data acquisition layer, a diagnosis and security layer (edge cloud), and a health service layer. The proposed framework adopts the following key technologies: a 5G-IPv6 communication network, context-aware health situation identification-based similarity measure, and blockchain-based secure data sharing mechanism. Finally, a prototype system has been implemented to monitor hypertensive heart disease, confirming its effectiveness with respect to a real scenario. Combined with the data of 45 patients, the prototype system can identify health situations with an accuracy of 96.34% at a sensitivity of 92.46% and a specificity of 93.62%, while significantly reducing the latency and improving the data sharing security.

The new generation healthcare monitoring system combines technologies of wireless body sensor network, cloud computing, and Bigdata, and there are still limitations in protocol security, response delay, and prediction of potential severity disease. In response to the above situation, an Internet Protocol Version 6 (IPv6)-based framework for fog-assisted healthcare monitoring is proposed. This framework is composite of body-sensing layer, fog layer, and cloud layer. The body-sensing layer generates physiological data, and fog computing node in fog layer collects and analyses time-sensitive data. Fog layer sends physiological data to cloud computing node in cloud layer for further processing. Mobile intelligent device connects fog computing node and helps individuals to predict the potential disease with its level of severity. The proposed framework uses advanced techniques such as IPv6-based network architecture, cloud–fog resource scheduling algorithm based on time threshold, and classification model of chronic diseases based on cascaded deep learning and so on. In order to determine the validity of the framework, health data were systematically generated from 45 patients for 30 days. Results depict that the proposed classification model of chronic diseases has high accuracy in determining the level of severity of potential disease. Moreover, response delay is much lower than Internet Protocol Version 4 (IPv4)-based cloud-assisted environment.

Network pruning facilitates the deployment of convolutional neural networks in resource-limited environments by reducing redundant parameters. However, most of the existing methods ignore the differences in the contributions of the output feature maps. In response to the above, we propose a novel neural network pruning method based on the channel attention mechanism. In this paper, we firstly utilise the principal component analysis algorithm to reduce the influence of noisy data on feature maps. Then, we propose an improved Leaky-Squeeze-and-Excitation block to evaluate the contribution of each output feature map using the channel attention mechanism. Finally, we effectively remove lower contribution channels without reducing the model performance as much as possible. Extensive experimental results show that our proposed method achieves significant improvements over the state-of-the-art in terms of FLOPs and parameters reduction with similar accuracies. For example, with VGG-16-baseline, our proposed method reduces parameters by 83.3% and FLOPs by 66.3%, with only a loss of 0.13% in top-5 accuracy. Furthermore, it effectively balances pruning efficiency and prediction accuracy.

With the rapid development of intelligent transportation and vehicular edge computing (VEC), efficient, fair, and interpretable task offloading has become a key challenge in dynamic and resource-constrained environments. Nonorthogonal multiple access (NOMA) can enhance connectivity and spectrum efficiency. However, conventional resource allocation strategies typically rely solely on channel gain ordering while overlooking spatial factors and fairness. In addition, the lack of transparency in multiagent deep reinforcement learning (MADRL) decision-making raises concerns regarding transparency and trustworthiness. To address these challenges, we propose a NOMA-based task offloading framework that integrates distance and channel-aware (DACA) resource allocation, and we design a distributed multiagent decision-making algorithm based on potential games (DACA-MAD4PG), further incorporating Shapley additive explanations (SHAP) to improve interpretability. The proposed framework is significantly different from existing NOMA-based task offloading approaches in the following three aspects. First, it introduces a DACA joint resource allocation mechanism to enhance both efficiency and fairness in VEC. Second, an exact potential game is incorporated to guarantee system stability and the existence of Nash equilibria. Last, SHAP is integrated to provide posthoc interpretability, thereby improving transparency in multiagent decision-making. Experiments based on real-world DiDi trajectory data demonstrate that the proposed approach significantly reduces task latency, improves service success rate, cumulative reward (CR), and fairness, and outperforms several baselines, thereby providing a stable and interpretable solution for VEC task offloading.

A new generation of home healthcare monitoring system strives to have such characteristics as low-cost, low-power and low-volume etc., and furthermore, it needs to quickly adapt to the development of national medical information based Cloud computing in China. In response to this trend, a Cloud-assisted home health monitoring system was designed in this paper. Sensors can be selectively configured to monitor the respective physiological signal depending on the diagnostic demands of a patient's disease. This system uses smart phones to receive physiological signals and cloud platforms (such as Xiamen Health Cloud) to storage physiological monitoring data. Combined with these data, health cloud provides risk assessment of chronic disease. Clinical application of the system shows that it has positive significance for patients with hypertension and diabetes who can enjoy health monitoring and the services of health could at home.

In order to tackle some challenges in ubiquitous healthcare monitoring such as mobility, scalability, and network latency, a framework for Fog-assisted healthcare monitoring is proposed in this paper. This framework is composite of body-sensing layer, Fog layer (Fog-assisted gateway), Cloud layer (health Cloud). And then, this paper makes an intensive study in some key technologies of the pro posed framework such as an IPv6-based network architecture, intelligent warning model based on subband energy feature, security framework of HL7 RIM-based data exchange, health risk assessment based on fusion of grey model and Markov model. Finally, results of experiment depict that the proposed intelligent warning model can make immediate distinction abnormal signals. Moreover, the proposed health assessment model confirms its effectiveness with respect to 245 patients in Xiamen District of Jimei.