The influenza problem has always been an important global issue. It not only affects people’s health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control method for flu epidemics. It can effectively predict the influenza-like illness morbidity, and provide reliable information to the relevant facilities. For social facilities, it is possible to strengthen epidemic prevention and care for highly sick groups. It can also be used as a reminder for the public. This study collects information on the influenza-like illness emergency department visits to the Taiwan Centers for Disease Control, and the PM_2.5 open-source data from the Taiwan Environmental Protection Administration's air quality monitoring network. By using deep learning techniques, the relevance of short-term estimates and the outbreak calculation method can be determined. The techniques are published by the WHO to determine whether the influenza-like illness situation is still in a stage of reasonable control. Finally, historical data and future forecasted data are integrated on the web page for visual presentation, to show the actual regional air quality situation and influenza-like illness data and to predict whether there is an outbreak of influenza in the region.

In the past decade, Internet of Things (IoT) technology has been widely used in various applications in daily life. Currently, IoT applications primarily depend on powerful cloud data centers as computing and storage centers. However, with such cloud-centric frameworks, numerous data are transferred between end devices and remote cloud data centers via a long wide-area network, which will result in intolerable latency and a lot of energy consumption. The edge computing paradigm is exploited to sink the cloud computing capability from the network core to network edges in proximity to end devices to enable computation-intensive and latency-critical edge intelligence applications to be executed in a real-time manner to alleviate this problem. With the increasing number of edge devices, it is essential to obtain the status of devices in real time to realize the overall resources of heterogeneous edge devices. Thus, constructing a system that can monitor each device’s status and performance is important. This study implements a cluster-based heterogeneous edge computing system by integrating the Docker, Kubernetes, Prometheus, Grafana and Node Exporter technologies for resource monitoring and performance evaluation. In the experiment, three deep learning models for object detection evaluate the performance of the implemented system. Through the constructed resource monitoring platform, the resource usage status of various edge devices can be monitored easily. In addition, the overall system performance can also be evaluated effectively.