TY - GEN
T1 - Automated estimation of link quality for Lora
T2 - 18th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2019
AU - Demetri, Silvia
AU - Zúñiga, Marco
AU - Picco, Gian Pietro
AU - Kuipers, Fernando
AU - Bruzzone, Lorenzo
AU - Telkamp, Thomas
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Link quality
KW - LoRa
KW - LPWAN
KW - Multispectral images
KW - Remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85066635881&partnerID=8YFLogxK
U2 - 10.1145/3302506.3310396
DO - 10.1145/3302506.3310396
M3 - Conference contribution
SN - 978-1-4503-6284-9
T3 - IPSN 2019 - Proceedings of the 2019 Information Processing in Sensor Networks
SP - 145
EP - 156
BT - IPSN 2019 - Proceedings of the 2019 Information Processing in Sensor Networks
A2 - Eskicioglu, Rasit
PB - ACM
CY - New York, NY, USA
Y2 - 16 April 2019 through 18 April 2019
ER -