Geomagnetic field fingerprint from wristband and smartphone clusterization for indoor localization

International Conference on SMART CITY Innovation 2018

Ahdiy, F.A.^1 ; Yulita, I.N.^1

Universitas Padjadjaran, Jl. Raya Bandung Sumedang KM.21, Hegarmanah, Jatinangor, Sumedang, West-Java

45363, Indonesia^1

关键词: Built-in sensors;    Clusterization;    Geomagnetic fields;    Hier-archical clustering;    Indoor localization;    Indoor localization systems;    Information collections;    UCI machine learning repository;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/248/1/012030/pdf
DOI  :  10.1088/1755-1315/248/1/012030

来源: IOP

【 摘 要 】

Nowadays, many wearable and smartphones have their built-in sensors that can be used to obtain the information about location to make an Indoor Localization Systems (ILS). It could be helpful on developing Smart City Systems, where ILS can handle the information collection about space and placement inside a room or building. In this research, we used the datasets from UCI Machine Learning Repository, which consists data of geomagnetic field fingerprint captured by both smartphone and smartwatch sensors from certain locations with timestamps provided, and then followed by clusterization processes to determine whether each device could effectively used for ILS based on radiation emitted in the same environment. For the methods, we used K-means (for k=2 to k=10) and Hierarchical Clustering. After several experiments, hierarchical clustering works best to cluster the data since the outliers problem doesn't occurred in this algorithm, and every instance get clustered with high homogenity for each member and every clusters differs enough in characteristics. Newer device with more sensor needed for next research.