# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 2 Related Work
### Locating Systems and Applications Based on Bluetooth Low Energy (BLE)  
There are a number of related researches in the field of BLE-based locating systems and applications in the literature. [PERSON] et al. used BLE beacons to estimate the occupancy of areas inside a building for managing an emergency situation such as fire or terrorist attack [13]. The mobile application installed on the occupant's mobile device keeps sending the received beacon IDs and their Received Signal Strength Indicator (RSSI) values to a remote control server, by which the server can estimate the location of the occupant. [PERSON] et al. aim at solving the problem of an overcrowded emergency room in which patients waiting to be treated may move around in the hospital. Their solution is a mobile indoor positioning system based on iBeacon to estimate patients' real-time locations [14], so doctors and nurses can find their patients conveniently. [PERSON] et al. used iBeacon for warehouse management that improves the performance of the order-picking and stock-taking process with high accuracy [15]. Specifically, the items kept in the warehouse are attached with beacon transmitters, and the beacon receivers are deployed in the warehouse. When searching for a specific item, the warehouse management system can locate the item by RSSI fingerprint matching. [PERSON] and [PERSON] [16] explored fingerprint positioning using BLE beacons. Their experimental result showed that with relatively sparse deployment of beacons, using BLE has better positioning accuracy compared with using Wi-Fi. [PERSON] et al. also studied the topic of indoor positioning using BLE beacons [17]. Specifically, they tried to minimize the error between real distance and estimation by using fuzzy logic. At several different distances, the average positioning errors of using fuzzy logic are lower than using direct RSSI readings and median values. [PERSON] et al. proposed a light-weightindoor positioning algorithm based on RSSI fingerprint matching [18]. To accelerate the process of fingerprint matching, they used a reduced-degree matrix composed of only 3-tuples (average, count, and standard deviation of the non-zero signal readings), and the experimental results showed good positioning accuracy. [PERSON] et al. improved the accuracy of inertial sensor-based indoor localization with the help of iBeacon devices [19]. In their design, when a user enters the vicinity of iBeacon device clusters, the accumulated errors of the inertial sensor-based indoor localization can be corrected by iBeacon localization. Experimental results show that 90% of the localization errors are within 3.5 m in large open areas. [PERSON] et al. focused on investigating the accuracy of distance estimation using iBeacon [20]. They also designed a mobile user interface (UI) for users to adjust the beacon settings (TX power and frequency) in order to witness how the settings can affect the distance estimates. To further improve the accuracy of indoor localization, [PERSON] et al. proposed a novel technique called _INTRI_, which employs trilateration in fingerprint-based environment [21]. First, in the environment the signal strengths from each access point are measured in order to form distinct contours of the equal signal strengths in the fingerprint region. Then, given the user's received signal vector, the user's location can be correlated with a number of contours, and accurately located by the concept of trilateration. Their proposed technique is a general approach applicable to various fingerprint signals such as Wi-Fi, ZigBee, RFID, and Bluetooth.