Train Once, Locate Anytime for Anyone: Adversarial Learning based Wireless Localization
Danyang Li, Jingao Xu, Zheng Yang, Yumeng Lu, Qian Zhang, Xinglin Zhang
Abstract
Among numerous indoor localization systems, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. To push forward this approach for wide deployment, three crucial goals on delightful deployment ubiquity, high localization accuracy, and low maintenance cost are desirable. However, due to severe challenges about signal variation, device heterogeneity, and database degradation root in environmental dynamics, pioneer works usually make a trade-off among them. In this paper, we propose iToLoc, a deep learning based localization system that achieves all three goals simultaneously. Once trained, iToLoc will provide accurate localization service for everyone using different devices and under diverse network conditions, and automatically update itself to maintain reliable performance anytime. iToLoc is purely based on WiFi fingerprints without relying on specific infrastructures. The core components of iToLoc are a domain adversarial neural network and a co-training based semi-supervised learning framework. Extensive experiments across 7 months with 8 different devices demonstrate that iToLoc achieves remarkable performance with an accuracy of 1.92m and > 95% localization success rate. Even 7 months after the original fingerprint database was established, the rate still maintains > 90%, which significantly outperforms previous works.