Zee: zero-effort crowdsourcing for indoor localization

By Anshul Rai, Krishna Chintalapudi, V. Padmanabhan, Rijurekha Sen. ACM/IEEE International Conference on Mobile Computing and Networking, 2012

1,181 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/5b2c81e10064fc1cb148532e42d7b8db6096f186

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

In Ocean Engineering

Citations in the corpus

Walk detection and step counting on unconstrained smartphones (2013, methodology): “We applied the algorithms to a more complex trace that involved a greater deal of activities chosen to repeat those in [47]—i.”
A Survey of Indoor Inertial Positioning Systems for Pedestrians (2013, methodology): “The Zee system proposes positioning primarily through fingerprinting, but with a radio map that is crowd-sourced from the subset of users with SHScapable smartphones [23].”
Avoiding multipath to revive inbuilding WiFi localization (2013, background): “Our main observation is that natural human mobility, when combined with PHY layer information, can help in accurately estimating the angle and distance of a mobile device from an wireless access point (AP).”
Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications (2017, methodology): “Here, we mention the works of ‎[80] exploiting human motions, ‎[81] using smartphone-based crowdsourcing, and ‎[82] adopting ray tracing tool.”
Multi-Person Localization via RF Body Reflections (2015, background): “Over the past decade, the networking community has made major advances in RF-based indoor localization [5, 34, 21, 26, 38, 17, 21, 11, 22], which led to systems that can localize a wireless device with centimeter-scale accuracy.”
Fusing Similarity-Based Sequence and Dead Reckoning for Indoor Positioning Without Training (2017, background): “[12] proposed Zee to achieve crowdsourcing of the training data using a smartphone without any participation of users.”
A Survey on Wireless Indoor Localization from the Device Perspective (2016, background): “Currently, some works have been done by leveraging crowdsourcing to generate the indoor radio map [Rai et al. 2012].”
A Survey of Selected Indoor Positioning Methods for Smartphones (2017, methodology): “In most cases steps are detected and counted using accelerometers [75]–[83] and, in some cases, gyroscopes [84].”
A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation (2018, background): “…Zee enables training data for the radiomap to be crowdsourced without any explicit effort on the part of users by leveraging the inertial sensors present in the mobile devices carried by users, to track them as they traverse an indoor environment, while simultaneously performing Wi-Fi scans [100].”