<i>WiMap:</i> Autonomous Wi-Fi Mapping for Device-free Tracking in Smart Homes

Tracking the locations of users and Wi-Fi-enabled devices, such as smart home appliances, can facilitate more intelligent home automation services. Typical Wi-Fi human tracking systems also depend on the known Wi-Fi device layout, highlighting the essential role of Wi-Fi device mapping. While progress has been made in autonomous Wi-Fi devices mapping, these methods generally rely on the unreasonable assumption that any pair of Wi-Fi devices can communicate with each other. It is impractical because the communication pattern in a home should be centered around Wi-Fi routers, i.e. , all Wi-Fi appliances should communicate with the router rather than the other Wi-Fi appliances. This paper proposes WiMap , an automatic Wi-Fi mapping system suitable for the router-centric communication pattern in smart homes. The basic idea is as follows: though cleaning robots cannot directly communicate with smart home appliances (except routers), their movement can passively affect ambient Wi-Fi signals similarly to humans. Consequently, the Wi-Fi mapping problem can be regarded as the inverse problem of device-free tracking. To resolve cumulative deviation and environmental interference, we propose a multi-trajectory fusion approach and the Velocity-to-AoA (V2A) Wi-Fi mapping model. The V2A model theoretically reveals the method to solve the angle of arrival from velocity without multiple antennas, enabling accurate Wi-Fi mapping. We implement the WiMap with commercial Wi-Fi devices, and the results demonstrate that our angle-based model achieves median localization errors of 0.10m.

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Publication Details

Journal
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Published
2025-12-02
DOI
https://doi.org/10.1145/3770700
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
Field-Weighted Citation Impact
0.00
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article

WiMap: Autonomous Wi-Fi Mapping for Device-free Tracking in Smart Homes

Xin Nan Xie, Xiulong Liu, Yichen Tian, Wenyu Qu et al.
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Indoor and Outdoor Localization Technologies
article

WiMap: Autonomous Wi-Fi Mapping for Device-free Tracking in Smart Homes

Xin Nan Xie, Xiulong Liu, Yichen Tian, Wenyu Qu, Xinyu Tong, Renrui Tan, Sheng Chen, T.S. Hong
article en

Abstract

Tracking the locations of users and Wi-Fi-enabled devices, such as smart home appliances, can facilitate more intelligent home automation services. Typical Wi-Fi human tracking systems also depend on the known Wi-Fi device layout, highlighting the essential role of Wi-Fi device mapping. While progress has been made in autonomous Wi-Fi devices mapping, these methods generally rely on the unreasonable assumption that any pair of Wi-Fi devices can communicate with each other. It is impractical because the communication pattern in a home should be centered around Wi-Fi routers, i.e. , all Wi-Fi appliances should communicate with the router rather than the other Wi-Fi appliances. This paper proposes WiMap , an automatic Wi-Fi mapping system suitable for the router-centric communication pattern in smart homes. The basic idea is as follows: though cleaning robots cannot directly communicate with smart home appliances (except routers), their movement can passively affect ambient Wi-Fi signals similarly to humans. Consequently, the Wi-Fi mapping problem can be regarded as the inverse problem of device-free tracking. To resolve cumulative deviation and environmental interference, we propose a multi-trajectory fusion approach and the Velocity-to-AoA (V2A) Wi-Fi mapping model. The V2A model theoretically reveals the method to solve the angle of arrival from velocity without multiple antennas, enabling accurate Wi-Fi mapping. We implement the WiMap with commercial Wi-Fi devices, and the results demonstrate that our angle-based model achieves median localization errors of 0.10m.

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous TechnologiesVol. 9(4)
Tianjin University (CN)
Openalex Percentile: Top 62%
Indoor and Outdoor Localization Technologies
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