1. IPSN19 杨景

2. Background
Success of Internet of Things (IoT) depend highly on reliable communication of billions devices
LoRa (and LPWAN) is at forefront of commercial interest
long coverage, simplifying deployment and management, etc.

3. Problem and Motivation
Performance of LoRa is intrinsically dependent on the environment
Channel models and empirical evidence are still largely lacking in the literature
Challenge
traverse multiple types of environments
spatial extension and sequence are highly variable in the large area

4. Goal
Model LoRa links and estimate their quality in a simple and low-cost way
use remote sensing techniques to obtain
environment-specific information in a low-cost manner
长距离 遥感 覆盖信息

5. Remote Sensing
exploiting the propagation and reflection properties of electromagnetic waves
the target scene is illuminated by a source of electromagnetic radiation
sensors mounted on satellites, airplanes or UAVs, measure the radiation reflected by the objects in the scene
enabling the automatic extraction of detailed information over a large- scale area

6. LoRa Link in Theory high sensitivity: −140 dBm
free-space path loss (FSPL) model
upper bounds km
in practice ≥30km
the range of LoRa links can vary by a factor of 100 or more
unique to low-power long-range links
Wifi -90dbm
10^5 weaker than lora
Factor 5

7. The Baseline: Open-space Scenarios
Prx expected received power (RSSI)
Ptx transmission power
Gtx , Grx are transmitting and receiving antenna gains
FSPL free-space path loss
Expected Signal Power (ESP) to obtain the energy of the signal
141 TTN gateways 每种颜色一个网关

8. The Baseline: Open-space Scenarios
not uncommon for LoRa to attain very wide coverages
at shorter distances (<50 km) the error is quite high

9. The Baseline: Open-space Scenarios
环境 衰减
高度 考量 不同网关高度不一样
the need to classify the attenuation of the surrounding environment
the need to use a model that considers height as an integral parameter of the propagation model

10. Assessing the Impact of Obstacles
A long “transitional region”
any distance under 6 km, we get a mix of link quality, from good to poor

11. Assessing the Impact of Obstacles
植被覆盖以及环境的影响
R2 3km 在$6解释
R1 through buildings
R2, R3, R4 through farming fields
R4 is bad as GA is placed indoors and near the NW side of the building(towards R1)

12. Extracting Land Cover
Images acquired via the Sentinel-2 satellite constellation, a last-generation remote sensing system of the European Space Agency (ESA).

13. high ( 92%) overall accuracy for all three tiles
Extracting Land Cover
high ( 92%) overall accuracy for all three tiles
提取质量 评估
Overall Accuracy (OA): percentage of test pixels correctly classified
Producer’s Accuracy (PA): the percentage of correctly classified pixels for the given class
User’s Accuracy (UA): the percentage of correctly classified pixels computed w.r.t. the overall number of pixels associated to the given class

14. Exploiting Land-cover Knowledge
Land Cover makes different

15. Exploiting Land-cover Knowledge
R2 3km处 为 0
the height of obstacles belonging to nlos classes plays a role in determining link quality

16. Model the Impact of Land Cover
A model that needs measurements
requires empirical data
log-normal shadowing model
d distance from transmitter
PL(d0) path loss at d0
n path loss exponent of the environment
σ is the standard deviation of a zero-mean Gaussian random variable X

17. A General Model A model that needs measurements
using the Okumura-Hata model
Hm, hb height of transmitter and receiver
d distance from transmitter
f transmission frequency
a(hm) depending on the environment
Urban model
Suburban model

18. Validation in the Wild
判断视距和非视距 全路线 靠近节点附近
node in LOS or NLOS

19. Estimation Accuracy of ESP
(a)(b) ≤ 9dB (c)(d) ≥ 32dB

20. Predicting the Gateway Coverage
the gateway coverage is significant variability in space depending on both the direction and the distance
Intersection performance more better

21. Contribution
analyze empirically the maximum range of LoRa in free space with a weather balloon, and use that evaluation as a baseline to gain insights about the negative effects of the environment at ground level
propose a dedicated processing toolchain to automatically analyze the types of environment traversed by LoRa links
analyze and compare models in the literature for LoRa
validate the combined use of the Okumura-Hata model and our toolchain on a large real-world dataset from ‘The Things Network’

22. Thanks~