Power management for long-term sensing applications with energy

Power Management for Long-­‐Term Sensing Applica8ons with Energy Harves8ng Philipp Sommer, Branislav Kusy, Raja Jurdak AUTONOMOUS SYSTEM LABORATORY | COMPUTATIONAL INFORMATICS Flying Foxes, Megabats, Fruitbats Suborder: Megachiroptera Family: Pteropodidae Size: 6 – 40 cm Wingspan: up to 1.7 m Weight: up to 1.6 kg Diet: Fruits, nectar 2 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Tracking Flying Foxes Disease Vector •  Hendra Virus •  Ebola in Asia/Africa •  Coronavirus in Persian Gulf Seed Dispersal Behaviour InteracKon •  Bio Security •  Not well understood •  Threatened species •  With other flying foxes •  With other animals 3 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Roos8ng Camps 4 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Con8nental-­‐Scale Tracking of Flying Foxes Long-­‐term tracking of flying foxes Discovery of new camps 5 | Camazotz: MulKmodal AcKvity-­‐based GPS Sampling | Philipp Sommer Popula8on of Flying Foxes in Australia 400+ camps assessed every 3 months Cairns Brisbane Sydney Melbourne 6 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Delay-­‐Tolerant Wireless Networking Individuals travel between different camps and other locaKons Store sensor/posiKon samples locally in flash Upload using short-­‐range radio to gateway (3G) at known camps Base station 1
Base station 2
Base station n
Server
7 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Node PlaSorm: Camazotz •  MulKmodal sensing plaform (GPS, inerKal, pressure, audio) •  CC430F5137 system-­‐on-­‐chip with 900 MHz radio Li-Ion
Battery
Li-Ion
Charger
Low
Power
Ublox
GPS6
Max
Power
Supplies
CC430F5137
Texas
System-onInstruments
Chip:
CC430F5137
MCU/Radio
Solar
Panels
3-D
ST Inertial
Micro
sensors
LSM303
I2C
ADC
Pressure
Bosch
sensor
BMP085
SPI
Serial
Atmel
Flash
AT25DF
Audio Mic
Microphone
R. Jurdak, P. Sommer, B. Kusy, et al. “MulKmodal AcKvity-­‐based GPS Sampling,” IPSN 2013. 8 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak PlaSorm Requirements •  Weight limit: 30-­‐50 g (max. 5% of body weight) •  Long term operaKon (months-­‐years) •  Short-­‐range radio communicaKon in camps •  Delay tolerant networking •  GPS tracking •  Context (inerKal, pressure, audio) 9 | Camazotz: MulKmodal AcKvity-­‐based GPS Sampling | Philipp Sommer Hardware Camazotz PlaSorm Camazotz: Bat god in Mayan mythology Camazotz: Hardware Architecture Low-­‐power architecture • TI CC430 System-­‐on-­‐Chip (MCU + Radio) • ConKki OS Power Supply • Li-­‐Ion bamery (300 mAh) + solar panels Data Storage •  64-­‐MBit flash chip Li-Ion
Battery
CC430F5137
Texas
System-onInstruments
Chip:
CC430F5137
MCU/Radio
Solar
Panels
Low
Power
Ublox
GPS6
Max
Power
Supplies
3-D
ST Inertial
Micro
sensors
LSM303
I2C
ADC
Pressure
Bosch
sensor
BMP085
SPI
Serial
Atmel
Flash
AT25DF
11 | Camazotz: MulKmodal AcKvity-­‐based GPS Sampling | Philipp Sommer Li-Ion
Charger
Audio Mic
Microphone
Camazotz: On-­‐Board Sensors Li-Ion
MulKmodal sensing plaform Battery
• GPS (u-­‐blox MAX6) • InerKal (accelerometer, magnetometer) Power
Supplies
• Pressure and temperature • Microphone CC430F5137
Texas
System-onInstruments
Chip:
CC430F5137
MCU/Radio
Li-Ion
Charger
Solar
Panels
Low
Power
Ublox
GPS6
Max
3-D
ST Inertial
Micro
sensors
LSM303
I2C
ADC
Pressure
Bosch
sensor
BMP085
SPI
Serial
Atmel
Flash
AT25DF
12 | Camazotz: MulKmodal AcKvity-­‐based GPS Sampling | Philipp Sommer Audio Mic
Microphone
Sampling Frequency Tracking – Current status Short-­‐term frequent sampling Long-­‐term sparse sampling DuraKon 13 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Sampling Frequency Tracking – Our goal Short-­‐term frequent sampling Long-­‐term frequent sampling Long-­‐term sparse sampling DuraKon 14 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Op8mal Scheduling of GPS Tracking 2. GPS Duty Cycling 1. Energy EsKmaKon GPS Scheduler 3. AcKvity DetecKon GPS Sampling Kmes and Frequencies 4. Mobility Models 15 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak BaYery State of Charge Es8ma8on •  Camazotz plaform has a single 300 mAh Li-­‐ion bamery •  Store energy harvested through solar panels during dayKme •  Spend energy for sensing tasks (GPS) during the night •  How to esKmate bamery state of charge (SOC)? 16 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Method 1: BaYery Voltage Measurement •  Camazotz can measure bamery voltage using ADC channel •  Bamery voltage inaccurate in mid-­‐range (3.7-­‐3.9V) •  RelaKvely accurate in extreme states (>3.9V and <3.6V) 4.1
Battery Voltage [V]
4.0
3.9
3.8
3.7
3.6
3.5
3.4
3.3
29
62
3.2
99
119
240
468
1000
3.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
State of Charge (SOC)
17 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak 0.0
of energy harvested and spent by the node, which
bination of hardware and software based methods.
l is to estimate the net energy flow EBatt into the
which is the total harvested energy Einput less the
Eoutput consumed by the system within a specific
erval:Approach: EsKmate net energy flow into the bamery Method 2: Energy Es8ma8on EBatt = Einput
Eoutput
(1)
olarInput: Energy
Harvesting of solar panel voltage and charge current Measurement h mobility of nodes in our tracking application reOutput: Sosware Bookkeeping ased on scheduled tasks a highly
dynamic
solar energy
input, due to b
frequent
in location
(up to 600 km in a 24 hour period), so Current [mA]
l orientation, weather, and landscape. This mobility
with seasonal di↵erences in solar radiation makes
on of energy intake difficult. Thus, our hardware
1.0
20
0.8 us to directly measure the15voltage of the som allows
InerKal Radio 0.6
Baseline 10
ls using
the
Analog-Digital
Converter
(ADC)
of
the
0.4
5
0.2
ntroller.
Furthermore, the Torex 0charger chip pro0.0
charge0 current
sense
pin15 which
5
5 track
0 to
10
20 allows
10 15 the
20 25 30
Time [s]
Time [s]
of energy harvested
through the solar panels.
Power Profiling
50
40
30
20
10
0
18 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak loy software-based
bookkeeping to track the power
GPS Hot Start 0
5
10
15
20
Time [s]
25
30
35
Instantaneous error Comparison of Methods Bamery Voltage ConflaKon -­‐
based approach Energy EsKmaKon Long-­‐term error 19 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak ltagevoltage- and energy-based SOC estimators is that the voltagemiddle
based estimator does not depend on a previous SOC esrating
timate,
while the230µA
energy-based
estimatortask
does.running
However,
consumption
is roughly
(left), inertial
for
3.7 V
deriving the SOC from the battery voltage has a larger unenter), and
GPS hotstart with 30 seconds of tracking (right).
certainty when the battery is not nearly fully charged or
empty (see Figure 2), and is a↵ected by variations in the
load resistance and ambient temperature.
SOC Es8ma8on through Confla8on DE
de
Energy Estimation
SOC
Thus, instead of representing SOC estimators as a single
value, we represent them as normal distributions given by
their mean m and variance 2 . We d
use a mathematical
we
SOC
method called conflation [7] to combine data from two noisy
sources estimating the same quantity (SOC) and calculate
wv
a new normal distribution. Conceptually, the distribution
with the smaller uncertainty has a larger
d vinfluence on the
Voltage Measurement
SOC
mean value of the resulting distribution. Therefore, we can
calculate the weighting factors we and wv accordingly:
Figure 3. Conflating Distributions
(Green curve is the conflation of red curves. Note that the mean of the conflati
to the mean of the input distribution with smaller variance, i.e. with greater
At first glance it may seem counterintuitive that the conflation of two relatively broa
distributions can be a much narrower one (Figure 3). However, if both measurement
assumed equally valid, then with relatively high probability the true value should lie
overlap region between the two distributions. Looking at it statistically, if one lab m
measurements and another lab makes 100, then the standard deviations of their resu
distributions will usually be different. If the labs' methods are also different, with dif
systematic errors, or their methods rely on different fundamental constants with diff
uncertainties, then the means will likely be different too. But the bottom line is that t
150 valid measurements is substantially greater than either lab's data set, so the stand
deviation should indeed be smaller.
Figure 3. Model for estimation
of state of charge
(SOC).
2
2
erent
we =
v
2
e
+
,
2
v
wv =
e
2
e
+
2
v
(5)
3. Properties of Conflation
Conflation has several basic mathematical properties with significant practical advan
describe these properties succinctly, it will be assumed throughout this section that
The weighting factors we and
wfor determine
the influence
T. P. Hill and J. Miller. How to combine independent data sets v
the same quanKty. arXiv:1005.4978, 2010. 2
[ are calculated
d
Finally,
the
mean
m
and
variance
ofestimation
SOC
of
each
separate
estimator
on
the
final
of SOC
SOC
as follows:
time step. One important difference between the
ne thein each
2
2
nstantvoltage- and energy-based
· mv estimators
· v2 voltageis e2that
2
v · me + eSOC
are independent normal random variables with means m1 , m2 and standard deviations
respectively. That is, for i 1, 2 ,
20 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Experimental Results 5
4
3
2
1
0
GPS
Data Download
0
1
2
3
4
5
6
7
8
9
10
11 12
Day
13
14
15
16
17
18
19
20
21
22
23
10
5
[mA]
15
0
4.4
1.0
4.0
0.5
3.6
0.0
3.2
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23
Day
21 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Voltage
Current [mA]
Based on empirical voltage/charge measurements from free-­‐living flying foxes Outlook Ongoing work: •  Progressively longer field trials – from 30 to 150 then 1000 nodes •  Deploy up to 50 base staKons in camps ConKnuous and near-­‐perpetual tracking based on acKviKes, mobility models and remaining energy budget: •  ImplementaKon of conflaKon-­‐based method on nodes for extended field trials •  Improve measurement of harvested energy 22 | Power Management for Long-­‐Term Sensing ApplicaKons with Energy HarvesKng | Philipp Sommer, Branislav Kusy, Raja Jurdak Thank you Philipp Sommer Postdoctoral Fellow t +61 7 3327 4076 e philipp.sommer@csiro.au w www.csiro.au AUTONOMOUS SYSTEMS LABORATORY | COMPUTATIONAL INFORMATICS