Bronchitis and pneumonia are the common respiratory diseases, of which pneumonia is the leading cause of mortality in pediatric patients worldwide and impose intense pressure on health care systems. This study aims to classify bronchitis and pneumonia in children by analyzing cough sounds. We propose a Classification Framework based on Cough Sounds (CFCS) to identify bronchitis and pneumonia in children. Our dataset includes cough sounds from 173 outpatients at the West China Second University Hospital, Sichuan University, Chengdu, China. We adopt aggregation operation to obtain patients’ disease features because some cough chunks carry the disease information while others do not. In the stage of classification in our framework, we adopt Support Vector Machine (SVM) to classify the diseases due to the small scale of our dataset. Furthermore, we apply data augmentation to our dataset to enlarge the number of samples and then adopt Long Short-Term Memory Network (LSTM) to classify. After 45 random tests on RAW dataset, SVM achieves the best classification accuracy of 86.04% and standard deviation of 4.7%. The precision of bronchitis and pneumonia is 93.75% and 87.5%, and their recall is 88.24% and 93.33%. The AUC of SVM and LSTM classification models on the dataset with pitch-shifting data augmentation reach 0.92 and 0.93, respectively. Extensive experimental results show that CFCS can effectively classify children into bronchitis and pneumonia.

In 6G mobile networks, vehicular networks will significantly benefit from extremely high network throughput and capacity. For Internet of Things (IoT) within a vehicular network, the sensor data as an update is delivered from each sensor source to a nearby gateway, by Vehicle-to-Vehicle (V2V) and Vehicle to Roadside unit (V2R) communications. The mobility of the vehicles is not only affected by the vehicle itself, but also by external means, such as the signal operations of traffic lights. The red light stops the vehicles at the intersection, which will increase the time it takes for updates carried by the vehicle to be delivered. On the other hand, the red light can also increase the opportunities of vehicles moving behind to catch up with the waiting vehicles in forwarding updates. This is termed as the traffic hole problem by the traffic lights in vehicular networks. In this paper, we investigate the influence of traffic lights in vehicular networks using the metric of Age of Information (AoI). We discuss the optimal generation rate at the source by considering the trade-off between AoI and transmission cost. We propose a Total Average Cost aware generation rate Algorithm (TACA) for the generation interval time at the sensor source. Our intensive simulations verify the proposed algorithm and evaluate the influence of the traffic lights on AoI.