A Self-Evolving WiFi-based Indoor Navigation System Using Smartphones

Given a wide spectrum of demands for indoor location-based service, great research effort has been devoted to developing indoor navigation systems. Nevertheless, due to high engineering complexity and expensive infrastructure and labor cost, scalable indoor navigation is still an unsolved problem. In this paper, we present SWiN, a Self-evolving WiFi-based Indoor Navigation system. SWiN provides plug-and-play and light-weight indoor navigation in a sharing manner. To alleviate the impact of the environmental change and device diversity, SWiN extracts both the static and dynamic properties of WiFi signals including scanned AP list, variations of signal strength, and AP's relative strength order. SWiN exploits the leader-follower structure, navigating following users by tracking their motion patterns to provide real-time navigation guidance. In specific, during navigation, SWiN utilizes a light-weight synchronization algorithm to synchronize multi-dimensional WiFi measurements between leader and follower traces. Furthermore, a trace updating mechanism is developed to guarantee the long-term utility of SWiN by extracting useful information in followers’ traces. Consolidating these techniques, we implement SWiN on commodity smartphones, and evaluate its performance in a five-story office building and a newly opened two-story shopping mall with test areas over <inline-formula><tex-math notation="LaTeX">$8000\;\mathrm {m}^2$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>8000</mml:mn><mml:mspace width="0.277778em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href="he-ieq1-2915221.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$6000\;\mathrm {m}^2$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>6000</mml:mn><mml:mspace width="0.277778em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href="he-ieq2-2915221.gif"/></alternatives></inline-formula>, respectively. Our experimental results show that 95 percent of the tracking offsets during navigation are less than <inline-formula><tex-math notation="LaTeX">$2\;\mathrm {m}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mspace width="0.277778em"/><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="he-ieq3-2915221.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$3.2\;\mathrm {m}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:mn>2</mml:mn><mml:mspace width="0.277778em"/><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="he-ieq4-2915221.gif"/></alternatives></inline-formula> in these two environments.