Machine Learning Based Indoor Localization Using a Representative k-Nearest-Neighbor Classifier on a Low-Cost IoT-Hardware

Online Offline Learning for Sound-Based Indoor Localization Using Low-Cost Hardware

“…We already demonstrated the applicability for static room setups [4] in 2018, where we extend a nature-inspired method [5] without need for high spatial accuracy or big microphone arrays which is inspired by [6] and [7] and uses K-Means generated representative prototypes in a KNN model. With this model, an 88 % success rate can be achieved in distinguishing 16 squares with a side length of 15 cm on a 60 cm × 60 cm table top.…”

“…They evaluate current approaches based on audible sound with an accuracy in the range of meters as inaccurate. First positioning experiments by evaluation of the RIR provide promising results in the decimeter range [4]. Other presented methods allow more precise positioning, but require a high amount of resources in the form of high-quality hardware or complex algorithms.…”

“…which represents the raw data of a recording [4]. After the data transfer is complete, the IoT-Kit sends a command in topic convert to trigger the create.csv node that runs a Python script to merge the 20 generated plain text files into a valid.csv file to convert the received data into a suitable format for further processing.…”

“…In this paper, we present an alternative technology that promises to yield outstanding results with costeffective hardware aiming at replacing existing technologies in the long run, which often require high maintenance and acquisition costs while at the same time offering medium quality services. Regarding the required hardware, we only use a single $2 electronic microphone, a single ordinary loudspeaker and the WiFi-capable ESP8266 based IoT-Kit Octopus [4] and improve localization performance by combining efficient Machine Learning (ML) algorithms like K-Nearest-Neighbor (KNN), K-Means and Learning Vector Quantization (LVQ) in a distributed cloud application. We apply these algorithms to room impulse response (RIR) records, which depend on the position of the microphone, the room geometry and the setup of the room.…”

“…As the microphone moves away from the loudspeaker, the time required FIGURE 1. Schematic illustration of the room impulse response, the direct sound (red) reaches the microphones earlier with a higher amplitude than the first reflections (green and blue) due to signal propagation theory [4]. for the signal to reach the microphone increases and the measured intensity decreases.…”