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"Thoreau: An Experimental, Low-Power Wireless Underground Sensor Network For Soil Sensing"
Xufeng Zhang, Argonne National LabArseniy Andreyev, U ChicagoMonisha Ghosh, U Chicago ([email protected])Supratik Guha, U Chicago & Argonne National Lab
Acknowledgements:
Topics
• The agriculture IoT use case.• Wireless needs of agriculture.• Thoreau: the man, the experiment.• Challenges, solutions, next steps.
Agriculture: unique challenges for IoT
• Enormous potential to benefit from the huge advances in computing, communication and sensing.
• Requirements are quite different from other IoT environments.
• Zone of interest: root zone, 6” – 12” underground.• Fields are heterogeneous: sensing needs to be spatially dense.• Installations cannot interfere with normal field management
practices: no antennae sticking out of the ground, low-power.
• Tremendous savings (2x – 5X) possible in water, pesticide and fertilizer use, with important consequences for the environment.
Agriculture Vision: Cyber Physical System
• Continuous soil monitoring using dense sensors, combine with weather and crop data in the cloud and close the loop with recommendations.
Thoreau: the man, the experiment
• Henry David Thoreau• Essayist, naturalist, environmentalist. Believed in recording
the environment to better understand it.
• The experiment:• http://thoreau.uchicago.edu• First functioning buried wireless sensor network on an
university campus that collects and curates time and geotagged data on an open platform on the cloud.
• Completely buried sensors, radio and antenna. Wireless channel
Sensor hardware
Wireless challenges
• Transmission through soil depends on:• Frequency (lower is better, but antenna size increases).• Soil composition and bulk density.• Volumetric water content: wetter is worse.
• Sigfox Approach• In the 900 MHz unlicensed bands.• Frequency hopped, narrow-band, with combining in the back
end• Low data rate: 12 Byte packets• Simple protocols: mostly uplink.• Designed for long range
Wireless Network Architecture
Nodes Base station Backend UsersSensors
Sensor
Sensor
Sensor
Sensor
……
GPIO
MCU Radio
Power Management
Battery
Antenna
I2C
Analog
UART
SDI
Filter
Amp
Internet
Backend
Base station
Use
r Int
erfa
ce
Callback API
Sensor Node
Sensor
Sensor
Sensor
Sensor
……
GPIO
MCU Radio
Power Management
Battery
Antenna
I2C
Analog
UART
SDI
Various sensor types and data protocols
Filter Amp.
Internet
Sigfox backend
Base station
User interface
Sensors:• Temperature• Soil moisture• Soil conductivity• Water potential
5-year operation
Sensor Types
Decagon MPS-6: • Temperature: -40 to 60 oC (res. 0.1 oC)• Water potential: -9 to -100,000 kPa (res. 0.1 kPa)
Decagon GS3:• Temperature: -40 to 60 oC (res. 0.1 oC)• Electric conductivity: 0 to 23 dS/m (res. 0.001 dS/m)• Soil moisture: 0 to 100% (res. 0.2%)
DS18B20:Temperature: -55 to 125 oC (res. 0.5 oC)
Sensor Power Management
Power consumption:• MCU: active 0.29 mA, sleep 2 uA. Negligible.• Sensors: 35 mA active (0.15 sec), 0.33 mA standby. Relatively small.• Radio: TX 110 mA (1 sec). With our hardware power management solution standby
current~ 60 uA. Small
6 x 3 AA batteries (@4.5 V; ~6000 mAh considering discharge effect
5-year operation
Sensor Installation
6” underground• 1 antenna on top of ERC building• 27 sensor units in service all over
the campus. Locations:• Logan center (Clay. 2 units)• Mansueto library (sand)• Alumni house (mixed)• Haper quad (mixed)• Ida Noyes court yard (mixed)• Graham School (mixed)• Kenwood (mixed)
• Two sensors on each unit, measuring 4 soil properties:
• soil temperature• soil moisture• electric conductivity• water potential
Wireless Transmission Through Soil
L1 L2
h1
h2
d2
d1 𝜃𝜃1
𝜃𝜃2
Total channel loss:
Underground:Aboveground:Interface:
Determined by soil properties
* ρbk: bulk density** soil composition in wt%
Soil samples
ResultsID Location Soil type Underground
DepthRSSI
(dBm)SNR (dB)
Delay (s) Rep.
5784 Logan center 1 Clay 6” -129.4 12.3 1.6 1.06
1737 Logan center 2 Clay 10” -135.4 8.2 1.52 1.00
3064 Alumni house Mixed 8” -97.96 27.0 2.06 2.91
7317 Mansueto library Sand 6” -108.4 20.7 1.55 2.67
5465 Haper quad Mixed 7” -103.1 26.2 1.59 2.80
2675 Ida Noyes Mixed 8” -118.6 16.3 1.6 2.11
2583 Graham School Mixed 8” -116.7 20.7 1.38 2.16
2670 Kenwood Mixed 6” -131.8 11.1 1.69 1.07
• Minimum measured signal: SNR 2.46 dB, RSSI -139 dBm (0.6 mile, 10” underneath dense clay)• Farthest measure result: 1.4 miles away, antenna on the ground, RSSI -117 dBm• Sigfox: Uplink TX power 26 dBm, typical link budget around 160 dB
Logan W Logan E Kenwood Graham Ida Noyes Alumni Harper Mansueto0
20
40
60
80
100
120
140
160
Path
loss
(dB)
Discrepency Calc. path loss
Preliminary Sensor Data
10/14/2016
10/16/2016
10/18/2016
10/20/2016
10/22/201630
31
32
33
34
35
36
Volu
met
ric w
ater
con
tent
(%)
Time
10/14/2016
10/16/2016
10/18/2016
10/20/2016
10/22/201610
12
14
16
18
20
Tem
pera
ture
(deg
ree
C)
Time
Sprinkler on
10/4/2016
10/6/2016
10/8/2016
10/10/2016
10/12/2016
10/14/2016-100
-95
-90
-85
-80
Sig
nal s
treng
th (d
Bm
)
Time10/4/2016
10/6/2016
10/8/2016
10/10/2016
10/12/2016
10/14/201612
14
16
18
20
Tem
pera
ture
(deg
ree
C)
Time
period: 1 dayRaining
Heat Maps
Signal strength map Signal-noise ratio map Temperature map
Soil moisture mapElectric conductivity map Water potential map
Packet Error Rate Performance
Ricean fading is thebest fit for the compositechannel: undergroundand over the air
Challenges and solutions
• Challenge: Packet loss rate rises with increasing depth• Potential Solutions:
• Increase transmit power• Diversity combine at receiver• Use lower frequency: TVWS a possibility• Improve antenna type and orientation
• Future Work• Add more soil sensors: nitrates, biological, pH etc.• Deploy LoRa, for comparison. IEEE 802.11ah is also a possibility• Large scale experimental deployment under discussion with the
Morton Arboretum and Chicago Tollway.
Conclusions
• Proof of concept demonstration of fully buried, dense sensor network at 900 MHz, from soil to cloud.
• http://thoreau.uchicago.edu
Cn
Low-Power
PM2.5 Sensors
sensorswireless
CloudCuration+analytics+physics
Power Data rate
Range
SoilSensing
