This paper proposes an android-based application to help the users to navigate in finding books in the library easily and interactively. This navigation application is connected to a Bluetooth Low Energy (BLE) device that will emit an RSSI signal received by the Smartphone user and show the desired distance to the bookshelf position. The method of triangulation and mean filter were used to eliminate noise in the test environment to make the position of the bookshelf can be found precisely based on the RSSI BLE Beacon value. The test results showed the largest RSSI value for LOS conditions at -48dBm and NLOS at -63 dBm; while the lowest RRSI values for LOS conditions was at -84dBm and NLOS was at -96dBm.

The Bluetooth Low Energy (BLE) MESH network technology gains popularity in low duty IoT systems. Its advantage is a low energy consumption that enables long lifetime of IoT systems. The paper proposes and evaluates new MRT management methods, i.e. exact and heuristic, that improves energy efficiency of BLE MESH network by minimizing the number of active relay nodes. The performed experiments confirm efficiency of the MRT methods resulting in significantly lower energy consumption of BLE MESH network.

In this paper, an autonomous wearable sensor node is developed for long-term continuous healthcare monitoring. This node is used to monitor the body temperature and heart rate of a human through a mobile application. Thus, it includes a temperature sensor, a heart pulse sensor, a low-power microcontroller, and a Bluetooth low energy (BLE) module. The power supply of the node is a lithium-ion rechargeable battery, but this battery has a limited lifetime. Therefore, a photovoltaic (PV) energy harvesting system is proposed to prolong the battery lifetime of the sensor node. The PV energy harvesting system consists of a flexible photovoltaic panel, and a charging controller. This PV energy harvesting system is practically tested outdoor under lighting intensity of 1000 W/m2. Experimentally, the overall power consumption of the node is 4.97 mW and its lifetime about 246 hours in active-sleep mode. Finally, the experimental results demonstrate long-term and sustainable operation for the wearable sensor node.