Device-independent Quantum Fingerprinting for Large Scale Localization
- URL: http://arxiv.org/abs/2206.10838v1
- Date: Wed, 22 Jun 2022 04:35:17 GMT
- Title: Device-independent Quantum Fingerprinting for Large Scale Localization
- Authors: Ahmed Shokry and Moustafa Youssef
- Abstract summary: We present QHFP, a device-independent quantum fingerprint matching algorithm.
In particular, we present a quantum algorithm with a complexity that is exponentially better than the classical techniques.
Results confirm the ability of QHFP to obtain the correct estimated location with an exponential improvement in space and running time.
- Score: 6.141741864834815
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Although RF fingerprinting is one of the most commonly used techniques for
localization, deploying it in a ubiquitous manner requires addressing the
challenge of supporting a large number of heterogeneous devices and their
variations. We present QHFP, a device-independent quantum fingerprint matching
algorithm that addresses two of the issues for realizing worldwide ubiquitous
large-scale location tracking systems: storage space and running time as well
as devices heterogeneity. In particular, we present a quantum algorithm with a
complexity that is exponentially better than the classical techniques, both in
space and running time. QHFP also has provisions for handling the inherent
localization error due to building the large-scale fingerprint using
heterogeneous devices. We give the details of the entire system starting from
extracting device-independent features from the raw RSS, mapping the classical
feature vectors to their quantum counterparts, and showing a quantum cosine
similarity algorithm for fingerprint matching.
We have implemented our quantum algorithm and deployed it in a real testbed
using the IBM Quantum machine simulator. Results confirm the ability of QHFP to
obtain the correct estimated location with an exponential improvement in space
and running time compared to the traditional classical counterparts. In
addition, the proposed device-independent features lead to more than 20% better
accuracy in median error. This highlights the promise of our algorithm for
future ubiquitous large-scale worldwide device-independent fingerprinting
localization systems.
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