Evaluation of unsupervised indoor localisation algorithm using real-environment data

Localisation is becoming increasingly important. It can be used in navigation, social media, and various economic activities. Outdoor localisation is well established through the usage of GPS. However, indoor localisation is still an active research area. Indoor localisation has become increasingly important over the years as more people tend to spend time in indoors. It is possible to train a location´s Wi-Fi data based on its density distribution. The trained location could further be used for recognition in future visits. This project aims to implement and modify DCCLA (Density-based Clustering Combined Localisation Algorithm) to allow indoor localisation in a public environment.