Enabling IoT Self-Localization Using Ambient 5G Signals


Suraj Jog, Junfeng Guan, and Sohrab Madani, University of Illinois at Urbana Champaign; Ruochen Lu, University of Texas at Austin; Songbin Gong, Deepak Vasisht, and Haitham Hassanieh, University of Illinois at Urbana Champaign


This paper presents ISLA, a system that enables low power IoT nodes to self-localize using ambient 5G signals without any coordination with the base stations. ISLA operates by simply overhearing transmitted 5G packets and leverages the large bandwidth used in 5G to compute high-resolution time of flight of the signals. Capturing large 5G bandwidth consumes a lot of power. To address this, ISLA leverages recent advances in MEMS acoustic resonators to design a RF filter that can stretch the effective localization bandwidth to 100 MHz while using 6.25 MHz receivers, improving ranging resolution by 16x. We implement and evaluate ISLA in three large outdoors testbeds and show high localization accuracy that is comparable with having the full 100 MHz bandwidth.

NSDI '22 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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This content is available to:

@inproceedings {278318,
author = {Suraj Jog and Junfeng Guan and Sohrab Madani and Ruochen Lu and Songbin Gong and Deepak Vasisht and Haitham Hassanieh},
title = {Enabling {IoT} {Self-Localization} Using Ambient 5G Signals},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {1011--1026},
url = {https://www.usenix.org/conference/nsdi22/presentation/jog},
publisher = {USENIX Association},
month = apr,

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