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Zee: Zero-effort Crowdsourcing for Indoor Localization
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| Venue: | In Proceedings of ACM International Conference on Mobile Computing and Networking (MobiCom |
| Citations: | 85 - 2 self |
BibTeX
@INPROCEEDINGS{Rai_zee:zero-effort,
author = {Anshul Rai and Krishna Kant Chintalapudi and Venkata N. Padmanabhan and Rijurekha Sen},
title = {Zee: Zero-effort Crowdsourcing for Indoor Localization},
booktitle = {In Proceedings of ACM International Conference on Mobile Computing and Networking (MobiCom},
year = {}
}
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Abstract
Radio Frequency (RF) fingerprinting, based on WiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee – a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user’s initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length, (c) back propagation to go back and improve the accuracy of localization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building.
Keyphrases
zero-effort crowdsourcing indoor localization user-specific knowledge site-specific input novel technique calibration effort user initial location zee surmounts sitespecific calibration significant challenge orientation estimation explicit effort stride length real world mobile device training data explicit user participation wifi measurement inertial sensor placement-independent step counting calibration zero-effort large office building radio frequency indoor environment user-specific walk characteristic phone placement popular approach wifi scan wifi-based particle initialization known location cellular signal
