Wifi Indoor Positioning with Genetic and Machine Learning Autonomous War-Driving Scheme

Wifi Fingerprinting is a widely used method for indoor positioning due to its proven accuracy. However, the offline phase of the method requires collecting a large quantity of data which costs a lot of time and effort. Furthermore, interior changes in the environment can have impact on system accuracy. This paper addresses the issue by proposing a new data collecting procedure in the offline phase that only needs to collect some data points (Wi-fi reference point). To have a sufficient amount of data for the offline phase, we proposed a genetic algorithm and machine learning model to generate labeled data from unlabeled user data. The experiment was carried out using real Wi-fi data collected from our testing site and the simulated motion data. Results have shown that using the proposed method and only 8 Wi-fi reference points, labeled data can be generated from user’s live data with a positioning error of 1.23 meters in the worst case when motion error is 30%. In the online phase, we achieved a positioning error of 1.89 meters when using the Support Vector Machine model at 30% motion error.

@article{Tinh2022,
  title     = {Wifi Indoor Positioning with Genetic and Machine Learning Autonomous War-Driving Scheme},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Pham Doan Tinh and Bui Huy Hoang},
  doi       = {10.14569/IJACSA.2022.0130279},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130279}
}