Recently, low-power wide area network (LPWAN) has attracted attention as a wireless network for long-range Internet of Things (IoT) services. IoT devices in an LPWAN are managed by a network server in the cloud. Various data are delivered from the IoT devices to the network server, and the role of the centralized network server in the cloud has become important for efficient data transfer over an LPWAN. However, control by the network server concentrates the control load on the network server and is vulnerable in terms of response time and bandwidth utilization.Therefore, this paper takes the approach of moving the control of the LPWAN to the edge cloud, which provides computing and storage environments at base stations. An LPWAN gateway is then integrated with the edge cloud, and LPWAN data are cached at the edge cloud in the gateway for LPWAN control. LPWAN control such as report interval control for data, transmission power control, and data aggregation is applied to improve the efficiency of data transmission. Network control is performed by learning using cached data in the edge cloud. Compared with the existing LPWAN control approach, the proposed approach exhibits improved performance for IoT data transmission. The simulation results show the proposed approach's efficiency. KEYWORDSedge cloud, IoT, LPWAN, mobile edge computing, network control INTRODUCTIONComputer systems for intelligent services are becoming convergent in various areas. Tiny computer systems collect and exchange information using a network. They are applied to things or objects; that is, everything is connected to a network and the collected information in the network is provided as services in convergent areas. This is known as the Internet of Things (IoT). The IoT creates various intelligent services in combination with cloud computing. The information generated in the IoT area is delivered to a server in the cloud; the cloud server performs computing for services.Then, users can exploit the results of the computing as a service. In the cloud, users do not consider physical resources on the server side. Cloud computing provides computing resources regardless of physical considerations, and it has an important place in modern IT services. [1][2][3][4][5] Several wireless technologies are used to collect information about things in the IoT area, such as wireless sensor network (WSN) and low-power wide area network (LPWAN). Both WSN and LPWAN are wireless access technologies. Objects (i.e., IoT devices) named sensor nodes gather information around them using sensors in the networks. The sensor nodes are small computer systems and operate with small batteries; they have insufficient resources and use a low data rate. WSN is a wireless technology with a short transmission range; it considers personal operating space (POS) within 10 m. Thus, it is suitable for gathering information about personal space, and its applications are focused on intelligent personal area services. 6-10 In contrast, LPWAN is a wireless technology with a long transmis...

Background Rheumatic mitral stenosis is a significant cause of valvular heart disease. Pulmonary arterial systolic pressure (PASP) reflects the hemodynamic consequences of mitral stenosis and is used to determine treatment strategies. However, PASP progression and expected outcomes based on PASP changes in patients with moderately severe mitral stenosis remain unclear. Methods and Results A total of 436 patients with moderately severe rheumatic mitral stenosis (valve area 1.0–1.5 cm 2 ) were enrolled. Composite outcomes included all‐cause mortality and hospitalization for heart failure. Data‐driven phenotyping identified 2 distinct trajectory groups based on PASP progression: rapid (8.7%) and slow (91.3%). Patients in the rapid progression group were older and had more diabetes and atrial fibrillation than those in the slow progression group (all P <0.05). The initial mean diastolic pressure gradient and PASP were higher in the rapid progression group than in the slow progression group (6.2±2.4 mm Hg versus 5.1±2.0 mm Hg [ P =0.001] and 42.3±13.3 mm Hg versus 33.0±9.2 mm Hg [ P <0.001], respectively). The rapid progression group had a poorer event‐free survival rate than the slow progression group (log‐rank P <0.001). Rapid PASP progression was a significant risk factor for composite outcomes even after adjusting for comorbidities (hazard ratio, 3.08 [95% CI, 1.68–5.64]; P <0.001). Multivariate regression analysis revealed that PASP >40 mm Hg was independently associated with allocation to the rapid progression group (odds ratio, 4.95 [95% CI, 2.08–11.99]; P <0.001). Conclusions Rapid PASP progression was associated with a higher risk of the composite outcomes. The main independent predictor for rapid progression group allocation was initial PASP >40 mm Hg.