Automated estimation of link quality for Lora: A remote sensing approach

Silvia Demetri, Marco Zúñiga, Gian Pietro Picco, Fernando Kuipers, Lorenzo Bruzzone, Thomas Telkamp

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

13 Citations (Scopus)
517 Downloads (Pure)


Many research and industrial communities are betting on LoRa to provide reliable, long-range communication for the Internet of Things. This new radio technology, however, provides widely heterogeneous coverage; a LoRa link may span hundreds of meters or tens of kilometers, depending on the surrounding environment. This high variability is not captured by popular channel models for LoRa, and on-site measurementsÐa common alternativeÐare impractical due to the large geographical areas involved. We propose a novel, automated approach to estimate the coverage of LoRa gateways prior to deployment and without on-site measurements. We achieve this goal by combining free, readily-available multispectral images from remote sensing with the right channel model. Our processing toolchain automatically classifies the type of environment (e.g., buildings, trees, or open fields) traversed by a link, with high accuracy (>90%) and spatial resolution (10×10m2). We use this information to explain the attenuation observed in experiments. As signal attenuation is not well captured by popular channel models, we focus on the Okumura-Hata empirical model, hitherto largely unexplored for LoRa, and show that i) it yields estimates very close to our observations, and ii) we can use our toolchain to automatically select and configure its parameters. A validation on 8,000+ samples from a real dataset shows that our automated approach predicts the expected signal power within a ∼10dBm error, against the 20ś40dBm of popular channel models.

Original languageEnglish
Title of host publicationIPSN'19
Subtitle of host publicationProceedings of the 2019 Information Processing in Sensor Networks
EditorsRasit Eskicioglu
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages12
ISBN (Print)978-1-4503-6284-9
Publication statusPublished - 2019
Event18th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2019 - Montreal, Canada
Duration: 16 Apr 201918 Apr 2019


Conference18th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2019


  • Link quality
  • LoRa
  • Multispectral images
  • Remote sensing

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