## 1 Introduction  
The aging and growth of the world's population has caused traditional health information systems to become more complicated. Today, addressing the spread of the modern healthcare information systems to cover peripheral areas, while making accurate predictions of major diseases, are critical challenges. The rapid development of mobile computing, wireless communication, sensor networks, and embedded systems has also resulted in health information systems approaching the era of pervasive computing. This has led to a constant growth of applications and devices (i.e., wearable devices) for providing new pervasive health services [1], which are rapidly being adopted worldwide [2]. On the other hand, the Internet of Things (IoT) [3] provides computer power and Internet connections to everyday objects, capable of processing, communicating, and storing the data captured from the physical world. Most of the applications of IoT are pervasive in nature [4]. More specifically,in the healthcare domain, IoT applications have led to the concept of the Internet of Medical Things (IoMT) [5] (also known as healthcare IoT). This refers to a global infrastructure composed of medical devices and applications interconnected through the Internet. In this context, sensors for physiological parameters (i.e., ECG, blood pressure, blood oxygen saturation, etc. ), behavior (i.e., pedometers, gyroscopes, GPS, etc.) and patient context (i.e., relative air humidity, temperature, etc.) enable continuous observation and monitoring of health-related parameters. As a consequence, healthcare professionals can offer more accurate and lower-cost services without intervening in the patients' daily routine. IoMT-based platforms can empower the patient in the sense that they become self-aware of their health status [6]. In many cases, they even avoid or minimize the intervention of healthcare professionals. For example, [PERSON] and [PERSON] [7] proposed a cloud-centric IoT-based framework for monitoring disease diagnosing which automatically predicts potential diseases and their level of severity without healthcare professionals participating. Moreover, these platforms empower the physicians with technologies that simplify the healthcare process in addition to providing them with tools to support clinical decisions. This has made IoMT widely used to enhance healthcare and is now considered to be a pillar in new pervasive health services [8]. However, the number of IoMT devices is expected to grow considerably in the coming years and the heterogeneity present in different IoMT components (network interfaces, communication protocols, data structure, data semantics) will impose interoperability and privacy related challenges [9]. In this sense, a pervasive healthcare platform must be flexible enough to handle all these concepts. Therefore, the integration of IoMT solutions in an interoperable environment and the development of tools for the storage, processing, and widespread dissemination of IoMT data have become relevant. They have also required overcoming technological barriers that hamper the design of new solutions. The following open issues are related to the heterogeneity of devices and possible data representations, as well as the volume and complexity of the collected data:  
1. Standardization in the context of pervasive healthcare still does not guarantee interoperability among IoMT solutions [10], which is a general problem in IoT. Each manufacturer uses its own definitions of devices, data format, and communication protocols, which hampers integration with larger systems. Moreover, many solutions are closed and do not implement functionalities for data interchange;
2. Several communication protocols have been used in IoT/IoMT and their performances depend on the functionalities they offer. Depending on the application context, one protocol may be more suitable than another (i.e., a publish/subscribe-based for monitoring or a client/server-based protocol for data aggregation).