Push the limit of WiFi based localization for smartphones

By Hongbo Liu, Yu Gan, Jie Yang, Simon Sidhom, Yan Wang, Yingying Chen, Fan Ye. ACM/IEEE International Conference on Mobile Computing and Networking, 2012

443 citations recorded by the source corpus.

Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., $6\sim8m$) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as $2m$ and $1m$, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

Subfields: Electrical and Electronic Engineering, Ocean Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9809fb48fe5f8889b533db23007113953c0a1cb8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Shiwen Mao
2. Xuyu Wang
3. Lingjun Gao
4. Yunhao Liu
5. Zheng Yang
6. Zimu Zhou
7. Lionel Ming-shuan Ni
8. Dina Katabi

In Ocean Engineering

1. Shiwen Mao
2. Xuyu Wang
3. Lingjun Gao
4. Zimu Zhou
5. Lionel Ming-shuan Ni
6. Zheng Yang
7. S. Chan
8. Suining He

In Signal Processing

1. Shiwen Mao
2. Xuyu Wang
3. Lingjun Gao
4. S. Chan
5. Suining He
6. Zimu Zhou
7. Yunhao Liu
8. Zheng Yang

Citations in the corpus

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• From RSSI to CSI (2013, methodology): “…al. 2007; Yang et al. 2011], ultrasound [Harter et al. 1999; Priyantha et al. 2000], FM [Chen et al. 2012], infrared This work is supported in part by the NSFC under grant 61171067, the NSFC Major Program under grant 61190110, and the NSFC Distinguished Young Scholars Program under grant 61125202.”
• SpotFi: Decimeter Level Localization Using WiFi (2015, background): “…idea of these approaches is that one would collect fingerprints such as the vector of RSSIs from a specific location to all the APs in range, and if in the future a target were at the same location, it would exhibit the same fingerprint, enabling us to localize [14, 15, 16, 17, 18, 19, 20, 21, 22].”
• E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures (2014, background): “Furthermore, they need the support of wireless infrastructure, either lightweight devices such as re-use multiple access points if available [7, 11, 22] or costly specialized devices, such as antenna arrays [14, 37].”
• CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach (2016, background): “proposed a peer-assisted localization technique based on smartphones to compute accurate distance estimation among peer smartphones with acoustic ranging [40].”
• A Survey on Wireless Indoor Localization from the Device Perspective (2016, background): “Liu et al. [2013] propose an accurate acoustic smartphone-based indoor localization system.”
• Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, background): “These relative locations serve as additional constraints in location estimation and improve the localization accuracy [18], [32], [130].”
• Accurate indoor localization with zero start-up cost (2014, background): “), or, 2) it needs exhaustive fingerprinting of the environment to learn the spatial distribution of signal strength, either in a training phase [28, 18] or via crowdsourcing [26, 5] to achieve meter accuracy at best.”
• Precise Power Delay Profiling with Commodity Wi-Fi (2015, background): “[6], [7], [8], [9], [10]—multipath channel dynamics can be unveiled from consecutive measures of the power delay profile, e.”
• Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, background): “wave [23]).”