# An IoT System for Remote Monitoring of Patients at Home
## 1 Introduction  
The concept of IoT (Internet of Things) [1; 2], which allows smart objects to communicate with each other or with a user, has become increasingly popular. With an IoT, we can obtain much more information more easily than ever before, and control objects much more seamlessly. The IoT concept can be applied in many areas, including metering, traffic control, smart homes, and building management. The objects can be sensors, home equipment, meters, and other similar devices. The remote monitoring of patients has become a highly active area of research in recent years [3; 4; 5; 6; 7; 8; 9]. However, there has not been much research on remote monitoring that employs the IoT concept. Application areas that utilize the concept of IoT can be broadened to healthcare or remote monitoring areas. Personal Healthcare Devices (PHDs) are portable electronic healthcare devices that sense and measure users' biomedical signals. Activity monitors, medication dispensers, pulse oximeters, ECG monitors, blood pressure monitors and falling detectors are representative examples of PHDs [10; 11; 12; 13; 14; 15]. PHDs could be objects in an IoT system for health care, and healthcare workers could enjoy most of the advantages of an IoT system by monitoring patients at home via IoT systems. In this paper, an IoT system for the remote monitoring of patients at home is proposed. With the help of the IoT system for monitoring patients at home, medical staffs can monitor their patients at home much more easily than ever before by receiving diverse biomedical data from PHDs with which the patients are connected. PHDs are considered as objects in the IoT system in this paper, and the system will enable medical staffs to be informed when their patients experience emergency conditionsmore quickly than ever before. In this study, the IoT monitoring system is proposed and constructed based on the oneM2M (one Machine-to-Machine) communication protocol [16; 17; 18], an international communication protocol standard for IoT systems. To make the patient at home monitoring system more desirable and operable on ordinary IoT networks, we consider the following issues in this paper. * Multiclass data scheduling/communication: Because many diverse PHDs and their biomedical data are transmitted to medical staffs, each piece of data needs to be classified based on the urgency of its delivery. More urgent of data deliveries are assigned higher classes. For example, falling detector data have to be delivered more urgently than medication dispenser data, and thus falling detector data are assigned to the higher class. Alarm conditions should be assigned to the highest class. For example, abnormal ECG data are assigned to a higher class than normal ECG data, and should be delivered more quickly. * Communication protocol conversion: To use PHDs as objects in an IoT system based on the oneM2M protocol, a communication protocol conversion process is needed, as PHDs and IoT systems use different standard communication protocols. In other words, the ISO/IEEE 11073 protocol [10; 11] is an international standard for PHD communication, while the oneM2M protocol is an international standard for the IoT system considered in this paper. * Strict authorization: Because the biomedical data obtained by a PHD may involve a patient's privacy, the data need to be handled in a manner that prevents its exposure to unauthorized persons. In this study, two security schemes are proposed. First, a patient's biomedical data obtained by a PHD are not stored as a single unit, but stored in parts in the IoT server. The data are partitioned into several parts (two parts in this study) and stored separately in the IoT server. Furthermore, the separation information is not stored in the IoT server, but in the IoT authentication server. This means that anyone who wants to access the data has to access both the IoT server and the IoT authentication server at the same time.