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OrchLoc: In-Orchard Localization via a Single LoRa Gateway and Generative Diffusion Model-based Fingerprinting

Published: 04 June 2024 Publication History

Abstract

In orchards, tree-level localization of robots is critical for smart agriculture applications like precision disease management and targeted nutrient dispensing. However, prior solutions cannot provide adequate accuracy. We develop our system, a fingerprinting-based localization system that can provide tree-level accuracy with only one LoRa gateway. We extract channel state information (CSI) measured over eight channels as the fingerprint. To avoid labor-intensive site surveys for building and updating the fingerprint database, we design a CSI Generative Model (CGM) that learns the relationship between CSIs and their corresponding locations. The CGM is fine-tuned using CSIs from static LoRa sensor nodes to build and update the fingerprint database. Extensive experiments in two orchards validate our system's effectiveness in achieving tree-level localization with minimal overhead and enhancing robot navigation accuracy.

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Cited By

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  • (2024)StarAngle: User Orientation Sensing with Beacon Phase Measurements of Multiple Starlink SatellitesProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699367(689-703)Online publication date: 4-Nov-2024
  • (2024)FDLoRa: Tackling Downlink-Uplink Asymmetry with Full-duplex LoRa GatewaysProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699338(281-294)Online publication date: 4-Nov-2024
  • (2024)A Low-Density Parity-Check Coding Scheme for LoRa NetworkingACM Transactions on Sensor Networks10.1145/366592820:4(1-29)Online publication date: 8-Jul-2024
  • Show More Cited By

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cover image ACM Conferences
MOBISYS '24: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services
June 2024
778 pages
ISBN:9798400705816
DOI:10.1145/3643832
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Publication History

Published: 04 June 2024

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Author Tags

  1. in-orchard localization
  2. LoRaWAN
  3. fingerprinting
  4. generative diffusion model

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  • Research-article

Funding Sources

  • Economic Development Administration, Farms Food Future
  • NSF
  • UC Merced Fall 2023 Climate Action Seed Competition grant
  • UC Merced Spring 2023 Climate Action Seed Competition grant

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MOBISYS '24
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Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

View all
  • (2024)StarAngle: User Orientation Sensing with Beacon Phase Measurements of Multiple Starlink SatellitesProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699367(689-703)Online publication date: 4-Nov-2024
  • (2024)FDLoRa: Tackling Downlink-Uplink Asymmetry with Full-duplex LoRa GatewaysProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699338(281-294)Online publication date: 4-Nov-2024
  • (2024)A Low-Density Parity-Check Coding Scheme for LoRa NetworkingACM Transactions on Sensor Networks10.1145/366592820:4(1-29)Online publication date: 8-Jul-2024
  • (2024)Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the FieldACM Transactions on Sensor Networks10.1145/366218220:4(1-34)Online publication date: 8-Jul-2024
  • (2024)RALoRa: Rateless-Enabled Link Adaptation for LoRa NetworkingIEEE/ACM Transactions on Networking10.1109/TNET.2024.339234232:4(3392-3407)Online publication date: Aug-2024

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