## 2 Survey of Related Work
The emerging healthcare applications are possible due to the development in Micro-Electro-Mechanical Systems (MEMS), which enable the integration of various devices like actuators, sensor nodes or mobiles [10; 11]. It is preferable that the devices used for monitoring purposes operate wirelessly [12], forming Wireless Sensor Networks (WSNs), which constitute the substantial part of IoT [13]. WSNs are widely used in healthcare applications due to their advantages and diversity. In [14], [PERSON] and [PERSON] propose WSN for monitoring patients' heart rate and oxygen saturation. [PERSON], [PERSON] and [PERSON] embed Electrocardiography (ECG) and blood pressure sensors into a cellular phone [15]. The wireless body area network is an example of a suitable approach to the IoT healthcare paradigm. [PERSON], [PERSON] and [PERSON] use Wi-Fi technology to transmit data about the blood pressure, heart rate, body temperature and oxygen saturation to the base station [16]. [PERSON] and [PERSON] apply Bluetooth technology and a smartphone for monitoring the patient's health parameters [17]. In IoT healthcare applications, one of the most frequently-monitored issues is the localization of patient or equipment. For this purpose, depending on the application, various methods and technologies are used. Numerous approaches are based on Received Signal Strength (RSS) [18]. [PERSON] uses RSS measurements from several known Wi-Fi access points assuring the localization accuracy at a four room-zone level on a single floor of a building. In order to localize an asset in the healthcare environment, the authors of [19] use the existing infrastructure of the Wireless Local Area Network (WLAN), extended just with six access point beacons. Based on Wi-Fi RSS measurements and small Wi-Fi tags, they are able to localize the assets like wheelchairs, beds, etc., with an accuracy of about 2 m in the hospital clinic environment of 63 m \(\times\) 46 m size. [PERSON] et al. use RFID RSS measurements of the reference and monitored tags to estimate the cost function consisting of the disparity and similarity of RSS between monitored and reference tags [20]. In this way, the three optimal reference tags are found, and the position of the monitored tag is determined as the center of mass of the triangle, which they form. The average localization error of a patient or asset in a 5 m \(\times\) 10 m healthcare environment is about 0.74 m. [PERSON] et al. propose the stigmergy approach combined with RSS measurements of Bluetooth Low Energy (BLE) [21]. Their approach results in a localization error of less than 1.8 m in 75% cases in a 6 m \(\times\) 6 m furnished office. [PERSON] et al. propose a healthcare dedicated indoor localization algorithm based on BLE RSS measurements and least squares-support vector machine, resulting at the four room-zone level localization accuracy [22]. The authors of [23] focus on patients' localization, tracking and monitoring in the nursing institute environment. They use RSS measurements of the ZigBee standard and a particle filter. As a result, they achieved an average localization error of less than 2 m in 80% of cases. Different algorithms and methods can be used to improve the localization accuracy. In [24], the authors use the Radio Frequency Identification (RFID) fingerprints method and the artificial neural network, which enables a 3D localization accuracy of about 70 cm within a room-sized environment. A different approach to the indoor localization problem is shown in [25] where the authors used fingerprints of Wi-Fi and barometric pressure to localize a target with the floor accuracy of a six-floorbuilding. Their Barometer-aided Wi-Fi (BarFi) floor localization approach detects the target's floor correctly in 96.3% of cases. An interesting solution of the Pedestrian Dead Reckoning (PDR) algorithm is presented by [PERSON] and [PERSON] in [PERSON] and [PERSON] in [26]. They use data from off-the-shelf three-axis gyroscope, magnetometer and accelerometer smartphone sensors in an in-building environment.