Deep recurrent neural networks for wi-fi based indoor trajectory sensing

Hao Li*, Joseph K Y NG, Junxing Ke

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Wi-Fi based sensing becomes more and more popular in ubiquitous computing with prevalence of Wi-Fi devices. In this paper, we propose a new task, indoor trajectory sensing, based on Received Signal Strength Indicator (RSSI) of Wi-Fi. Traditional distance measure based methods, like Dynamic Time Warping (DTW) based Nearest-Neighbor (1NN) method, have poor performance in indoor trajectory sensing due to that RSSI of Wi-Fi in indoor environment is fluctuating, partially missing, time-varying and device-dependent. Recently, Recurrent Neural Networks (RNN) and its variants have strong abilities in learning the temporal dependency of sequence data since it can extract more meaningful features. Consequently, it is necessary to design an RNN model for the indoor trajectory sensing problem with relatively small size data. We adopt a passive way to collect Wi-Fi signals from the smart phone to ensure more data collected and generate multiple time series for each trajectory. For the recurrent neural network training, RNN and its variants are applied into our sequence data to find more meaningful patterns especially for different environment and devices. Series of real-world experiments have been conducted in our test bed and the results show that the deep based approach can achieve better performance than traditional methods with challenging environment and device factors.

Original languageEnglish
Title of host publicationAdvances in Networked-based Information Systems - The 22nd International Conference on Network-Based Information Systems, NBiS 2019
EditorsLeonard Barolli, Hiroaki Nishino, Tomoya Enokido, Makoto Takizawa
PublisherSpringer Verlag
Pages434-444
Number of pages11
ISBN (Print)9783030290283
DOIs
Publication statusPublished - 2020
Event22nd International Conference on Network-Based Information Systems, NBiS 2019 - Oita, Japan
Duration: 5 Sep 20197 Sep 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1036
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference22nd International Conference on Network-Based Information Systems, NBiS 2019
Country/TerritoryJapan
CityOita
Period5/09/197/09/19

Scopus Subject Areas

  • Control and Systems Engineering
  • Computer Science(all)

User-Defined Keywords

  • Indoor trajectory
  • Recurrent neural networks
  • Ubiquitous computing
  • Wi-Fi sensing

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