Demo: Scalable and Sustainable Asset Tracking with NextG Cellular Signals

This demonstration presents LiTEfoot, an ultra-low power localization system leveraging ambient cellular signals. To address the limitations of traditional GPS-based tracking systems in terms of power consumption and latency, LiTEfoot employs a non-linear transformation of the cellular spectrum to achieve efficient self-localization. Our design uses a simple envelope detector to realize spectrum folding, enabling the identification of multiple active base stations. The LiTEfoot prototype shows a median localization error of 22 meters in urban areas and 50 meters in rural areas, consuming only 40 μJoules of energy per localization update.

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Publication Details

Published
2024-12-04
DOI
https://doi.org/10.1145/3636534.3698837
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

Demo: Scalable and Sustainable Asset Tracking with NextG Cellular Signals

Nakul Garg, Nirupam Roy, Aritrik Ghosh
Modular Robots and Swarm Intelligence
article

Demo: Scalable and Sustainable Asset Tracking with NextG Cellular Signals

Nakul Garg, Nirupam Roy, Aritrik Ghosh
article en

Abstract

This demonstration presents LiTEfoot, an ultra-low power localization system leveraging ambient cellular signals. To address the limitations of traditional GPS-based tracking systems in terms of power consumption and latency, LiTEfoot employs a non-linear transformation of the cellular spectrum to achieve efficient self-localization. Our design uses a simple envelope detector to realize spectrum folding, enabling the identification of multiple active base stations. The LiTEfoot prototype shows a median localization error of 22 meters in urban areas and 50 meters in rural areas, consuming only 40 μJoules of energy per localization update.

University of Maryland, College Park (US)
Openalex Percentile: Top 78%
Modular Robots and Swarm Intelligence
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