With the intent of developing a user-context-aware application using IoT devices, I began the study with open inquiry into indoor location technology. Using Bluetooth beacons in the UNM testbed, I tested two methods—triangulation and ranging. I found that these models for encapsulating indoor location were insufficient: triangulation requires added infrastructure, limiting scalability; ranging lacks precision, thus limiting usability. This necessitated the creation of a different model for encapsulating location, which ended up being the focus of the study.

I decided to explore the idea of applying fingerprinting, explored in previous work on RFID and WiFi signals, to received BLE signal strength indicators (RSSI). After familiarizing myself with prior work, I developed a testing environment. The testbed consisted of an Android phone, Estimote iBeacons, and Philips Hue smart lights acting as IoT devices. After developing an understanding of the behavior of BLE signals, I then developed a simple proof-of-concept application which can store and naively match fingerprints, triggering smart lights when a match is found.