ACROSS Colloquium
Combined power management methods
in wireless networks of energy-hungry
sensors
Vana Jeličić, dipl.ing.
January 18, 2013
Content

Research area
WSNs – distributed event detection
 Communication energy  Wake-up radio
 Energy-hungry sensors

 Video
surveillance and smart gas monitoring
 Hierarchical, adaptive, event-driven sensing


Motivation and challenges
Problem approach
To-date results
 Future research


Activities of AIG
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Research area



Wireless sensor networks
Wireless sensor node
Energy-efficiency


Communication!
Distributed sensing systems
Event detection, alarm generation
 Video surveillance, gas monitoring



Energy-hungry sensors
Power management
Comm. unit (RX, TX, idle state – cca 20 mA!)
 Sensing unit

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Power management

Duty-cycling (D)
 Reducing
activity: sensors & radio
Maximal reaction time
Critical event arrival worst case
D = tactive / T
ENERGY
tactive <<
T >>
LATENCY
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Eliminating radio idle time


“Classical” WSN problem!
One-channel wake-up radio
Radio (WOR) – radio periodically wakes up from sleep
mode and listens for incoming packets without MCU interaction.
 Wake-On

TI CC1000, CC1101, CC1100E, CC2500, CC430
 MAC:

B-MAC, S-MAC, X-MAC...
Optimization
 delay
– energy trade-off
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Wake-up receiver (WURx)

Two-channel wake-up radio
 Ultra-low-power;
Continuously monitoring
 No idle listening on main radio

Lin, Rabaey and Wolisz; “Power-efficient rendez-vous schemes for dense
WSNs”, 2004, <50 uW to outperform one-channel radios!

Trade-offs



wake-up range vs. energy consumption
wake-up range vs. delay (multihops)
in-band vs. out-of-band wake-up radio
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WURx prototypes
Jelicic et al. Analytic Comparison of Wake-up receivers for WSNs and Benefits over the Wake-on Radio Scheme.
PM2HW2N 2012.
Author
Year
f [GHz]
Rate [kbps]
S [dBm]
d [m] P [uW]
AD
l [ms]
Implement.
Le Huy
2008
2,4
50
-50
NA
20
Y
NA
simulation
Yu
2008
2,4
100
-75
NA
53
N
NA
simulation
Langevelde
2009
0,868
45
-89
NA
2400
N
1,36
130 nm
Pletcher
2009
2
100
-72
NA
52
N
NA
90 nm
Durante
2009
2,4
100
-53
NA
12,5
Y, FPGA
NA
120 nm
Gamm
2010
0,868
NA
-52
40
2,78
Y
13
120 nm
Drago
2010
2,4
250
-87
NA
415
N
NA
65 nm
500
-82
NA
Fraunhofer
2010
0,868
1
-60
30
33
Y
32
180 nm
Huang
2010
2,4
100
-64
NA
51
N
NA
90 nm
0,915
100
-75
NA
Huang
2011
0,915
10
-86
NA
123
N
NA
90 nm
Marinkovic
2011
0,433
5.5
-51
10
0,270
N, (MCU)
9
OTS SMD
Shih
2011
0,9165
0.370
-122
1000
1153
Y
NA
OTS
Hambeck
2011
0,868
100
-71
304
2,4
Y
40-110
130 nm
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WURx prototypes (2)

Commercially available (LF, 125 kHz)




Austriamicrosystems
Atmel
Addressing required
Addressing not required
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WURx applications

Applications with WURx – proposals



Building automation 1, 3, 4
Healthcare 2
No energy vs. latency trade-off!
Still not used in
WSNs!
very promising!
1) Zhang et al. Improving Energy-Efficiency in Building Automation with Event-Driven Radio. WCSP 2011.
2) Marinkovic et al. Power Efficient Networking Using a Novel Wake-up Radio. PervasiveHealth 2011.
3) Gamm et al. Low Power Wireless Sensor Node for use in building automation. WAMICON 2011.
4) Gamm et al. Smart Metering Using Distributed Wake-up Receivers. I2MTC 2012.
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Sensing power management


Fixed duty cycle  energy wasting
Adaptive duty cycle
 Wake-up



latency: ton ≥ twakeup + tacquire
Event-driven
Context-awareness
Energy-awareness
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Heterogeneous WSNs for event detection



Different sensing modalities
Hierarchy
Applications
 Video
surveillance: Camera + PIR
 Gas monitoring: Gas sensor + PIR

High-consuming sensors
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Smart video surveillance




Reducing transmitted data size
Hierarchical, multi-tier, multimodal
Pyroelectric InfraRed (PIR) sensor
Energy-aware decisions
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Image transmission

Transmission of large amount of data







Only when really necessary
Increasing tactive
ZigBee not intended to that  Stack modificaton 1
Image fragmentation – maximal frame filling
Disabled MAC acknowledgment  APL layer control
Today – low power WiFi modules
Avoiding transmitting large amounts of data  only event
1) Jelicic et al. Reducing Power Consumption of Image Transmission over IEEE802.15.4/ZigBee Sensor Network. I2MTC
2010.
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Existing work – multimodal video networks

PIR sensor mounted on the camera board 1, 2, 3
 Same FOV; Dynamically changed sensitivity

Multi-tier Multimodal WSNs 4, 5, 6, 7, 8, 9
1) Magno et al. A Solar-powered Video Sensor Node for Energy Efficient Multimodal Surveillance. DSD 2008.
2) Magno et al. Adaptive Power Control for Solar Harvesting Multimodal Wireless Smart Camera. ICDSC 2009.
3) Magno et al. Multimodal abandoned/removed object detection for low power video surveillance systems. AVSS 2009.
4) Kulkarni et al. SensEye: A Multi–tier camera sensor network. ACM Multimedia 2005.
5) Prati et al. An Integrated MultiModal Sensor Network for Video Surveillance. VSSN 2005.
6) He et al. Vigilnet: An integrated sensor network system for energy efficient surveillance. ACM Trans. Sen. Netw. 2006.
7) Lopes et al. On the Development of a Multi-tier, Multimodal Wireless Sensor Network for Wild Life Monitoring. IFIP
Wireless Days 2008.
8) Magno et al. Energy Efficient Cooperative Multimodal Ambient Monitoring. EuroSSC 2010.
9) Jelicic et al. An energy efficient multimodal wireless video sensor network with eZ430-RF2500 modules. ICPCA 2010.
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Heterogeneous WVSN
HOMOGENEOUS NWK
HETEROGENEOUS NWK
Camera + PIR onboard
Further reducing
radio activities
Further reducing
cameras’ activities
Tier 2
Camera nodes
Coordinator
wakeup
Tier 1
PIR nodes
Two-tier network
WOR  duty-cycling!
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Two-tier network
WURx  NO duty-cycling
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Smart gas monitoring

Metal Oxide Semiconductor (MOX)
Small form factor
 Fast response
 Power-efficient


Heater Resistance change

Fabrication field

System-level field
TWO SEPARATED AREAS BY NOW!

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Related work

Fabrication field 1, 2





Pulse mode (duty-cycling)
Temperature dependence
Wake-up latency
9 mW
System-level application 3, 4, 5


Duty cycle
Still high energy consumption
1) Sayhan et al. Discontinuously operated metal oxide gas sensors for flexible tag microlab applications. IEEE Sensors J.
2008.
2) Rastrello et al. Thermal Transient Measurements of an Ultra-Low-Power MOX Sensor. J. of Sensors 2010.
3) Ivanov et al. Distributed smart sensor system for indoor climate monitoring. KONNEX Sci. Conf. 2002.
4) Postolache et al. Smart Sensors Network for Air Quality Monitoring applications. IEEE Trans. on Instrum. and Meas.
2009.
5) Choi et al. Micro sensor node for air pollutant monitoring: HW and SW issues. Sensors 2009.
6) De Vito et al. Wireless Sensor Networks for Distributed Chemical Sensing: Addressing Power Consumption Limits With
On-Board Intelligence. IEEE Sensors J. 2011.
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System-level application – our solution
1) Jelicic et al. Design, Characterization and Management of a WSN for Smart Gas Detection. IWASI 2011.
2) Jelicic et al. Context-Adaptive Multimodal WSN for Energy-Efficient Gas Monitoring. IEEE Sensors J. 2012.
Network


Energy consumption reduction on 3 levels:
Sensor level
duty-cycling gas sensor
 early detection of safe conditions
Node
Sensor


Node level
ultra low sleep current (8 uA)
 duty-cycling sensor node
 people presence detection (modifying duty cycle)


Network level

messages from neighbor nodes (modifying duty cycle)
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Early detection of safe conditions
R [kΩ]
Stable difference
between clean air and
contaminated air signals
1x103
Clean air – after long inactive time
Clean air – after short inactive time
Contaminated air – after short inactive time
1x102
1x103
1x101
1x102
A
threshold
1x101
1x100
B
1x100
1x10-1
0 47
1x10-1
0
1000
2000
3000
4000
200
5000
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6000
7000
8000
9000
10000
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time [ms]
Adaptive sampling rate (t_ON = 1s)
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
Quality ratio: node lifetime / worst case reaction time
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Motivation and challenge

Policies to reduce
Communication energy
 Sensing energy


Combined methods

Reducing amount of wirelessly transmitted data


Reducing radio idle consumption


adaptive sampling, event detection
Wake-up receiver
Goal
Context- and energy-awareness
 Good QoS

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To-date work and results

Proposed energy saving policies in WVSN & WGSN 1, 2, 5


multimodal (PIR nodes); adaptive duty-cycling
Reducing communication energy 3, 6

Extensive study and comparison of WURx solutions 4
1) Jelicic et al. Design, Characterization and Management of a WSN for Smart Gas Detection. IWASI 2011.
2) Jelicic et al. Context-Adaptive Multimodal WSN for Energy-Efficient Gas Monitoring. IEEE Sensors J. 2012.
3) Jelicic et al. Reducing Power Consumption of Image Transmission over IEEE 802.15.4/ZigBee Sensor Network. I2MTC 2010.
4) Jelicic et al. Analytic Comparison of Wake-up Receivers for WSNs and Benefits over the Wake-on Radio Scheme.
PM2HW2N 2012.
5) Jelicic et al. An energy efficient multimodal wireless video sensor network with eZ430-RF2500 modules. ICPCA 2010.
6) Jelicic et al. MasliNET – A Wireless Sensor Network based Environmental Monitoring System. MIPRO 2011.
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AIG – WSN activities
ACTIVITIES

APPLICATIONS
Sensors and sensor interfaces
HW (PCB) design
 Measurements




Embedded systems
Wheeze detection
 Air quality monitoring

Microcontrollers
 FPGA
 Firmware


Environmental
monitoring
Asthma monitoring


Berth monitoring
Fall detection
Wireless sensor networks
Power management
 Signal processing

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Hvala na pažnji!
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