Cyber-Physical Systems for
Sustainability
Guoliang Xing
Assistant Professor
Department of Computer Science and Engineering
Michigan State University
Research Objective
Energy
Environment
Sustainability
Healthcare
Hazards
Address challenges of sustainability by advancing
interdisciplinary research
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Cyber-Physical Systems
• “Cyber-physical systems are engineered systems that
are built from and depend upon the synergy of
computational and physical components”1
• Many critical sustainability application domains
– Environment, smart grid, medical, auto, transportation…
• # 1 national priority for Networking and IT Research
and Development (NITRD)
–
NITRD Review report by President's Council of Advisors on Science and Technology (PCAST) titled
“Leadership Under Challenge: Information Technology R&D in a Competitive World”, 2007
1 NSF Cyber-physical systems solicitation13502
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Our CPS Projects
Tungurahua Volcano, Ecuador
Data Center Monitoring,
HPCC, MSU
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Harmful Algae Bloom in Lake
Mendota in Wisconsin, 1999
Volcano Monitoring Sensors
Data center thermal monitoring
Residential electricity usage profiling
Real-time volcano monitoring
Aquatic process profiling
Robotic fish, Smart Microsystems
Lab, MSU
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Motivation
• Data centers are critical computing infrastructure
– 509,147 data centers world wide, 285 million sq. ft.1
– 2.8M hours of downtime, 142 billions direct loss/year1
An aerial view of EMC's new data
center in Durham, North Carolina2
An EMC data center 2
• 23% server outages are heat-induced shutdowns
1Emerson
Network Power, State of the Data Centers 2011, 2http://www.datacenterknowledge.com/archives/2011/09/15/emc-opens-new-cloud-data-center-in-nc/.
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Motivation
• Many data centers are overcooled
– Low AC set-points, high server fan speeds
– Excessive cooling energy
• up to 50% or more of total power consumption
• Rapid increase of energy use in data centers
– From 2005 to 2010, electricity use in data centers
grew 36% (US) and 56% (world wide)1
– An estimated 2% of electricity budget of US1
1Jonathan
G. Koomey, “Grouth in data center electricity use 2005 to 2010”, Analytics Press, 2011.
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Temperature Forecasting
• Predict server temperature evolution
– Identify potential hot spots
– Enable high CRAC set-points for energy saving
• Temperature at inlets/outlets indicates hotspots
cool air
hot air
Inlets
Outlets
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Challenges
• Complex air and
thermal dynamics
Row 2
Server
exhaust
• Highly dynamic
workloads
Raised-floor
cold air
Row 1
• Physical failures
– ACs, servers, fans
12-day CPU utilization data of one rack (64 servers with 512 CPU cores)
in High Performance Computer Center at Michigan State University
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System Architecture
• CFD + Wireless Sensing + Data-driven Prediction
– Preserve realistic physical characteristics in training data
– Capture dynamics by in situ sensing and real-time prediction
Data Center
Sensing
(CPU, fan speed, temperature, airflow)
geometric model
(server/rack dimension
and placement)
CFD Modeling
Real-time Prediction
Calibration
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Data Center Experiment
• Testbed configuration
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Chained
Temp. sensor
5 racks, 229 servers, 2016 cores
4 in-row CRAC units
35 temperature sensors
4 airflow sensors
In-row
CRACs
• Dynamic CPU utilization
In-row
CRACs
Temperature
sensor
Airflow
sensor
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Experiment Results
• 12-day experiment
Outlet
Inlet
10-minute temperature prediction
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Outline
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Data center thermal monitoring
Residential electricity usage profiling
Real-time volcano monitoring
Aquatic process profiling
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Residential Electricity in U.S.
• Residential electricity
– Largest sector
Residential Industrial
25.5%
36.7%
Others
Commercial
34.2%
• Rising cost
– Increase by 75% in 10 years
• Understanding usage
– Real-time power readings
– Fine-grained usage info
Electricity retail sales in
U.S. 2011
[US EIA-861, EIA-923]
Appl.
Joul %
When?
Bed light
5%
7pm-11pm
Fridge
8%
Every 1h
Space
heater
30%
Jan 1 …
….
….
….
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Supero
Smart meter
100W
Base station
‘+1’
Event-Appliance
Association
Event clustering
Light and acoustic sensors
Light + acoustic captures
90% power consumption
Event Correlation
(remove false alarm)
Light/acoustic event Power reading
14 / 23
Implementation & Deployments
TelosB (light)
Iris (acoustic)
Kill-A-Watt
Apartment-1 deployment
• System
– TelosB/Iris + TED5000 + KAW ground truth meters
• Five deployments
– Three apartments (40~150 m2), two houses
– 9 ~ 22 sensors
15 / 23
10-day Results
Appliance
Supero
Oracle
Baseline
kWh
Error (%)
kWh
Error (%)
kWh
Error (%)
Light 1
4.17
0.5
4.11
0.9
4.11
0.9
Light 2
4.96
0.1
4.92
0.8
4.92
0.8
Light 3
6.24
1.4
6.25
1.7
6.25
1.7
Light 4
1.45
0.1
1.45
0.1
1.48
1.7
Light 5
0.39
0.2
0.39
0.7
0.41
5.5
Water boiler
0.48
0.5
0.48
0.5
0
100
Tower fan
0.21
50
0.17
17.9
0.24
66.2
Rice cooker
0.98
2.2
1.01
1.2
1.01
0.8
Hair dryer
0.07
19.2
0.09
0.4
0.02
73.2
Fridge
11.8
3.7
11.8
3.2
11.8
3.2
Bath fan
0.12
N/A
0.17
N/A
0
N/A
Router
2.03
4.3
3.04
43.3
3.04
43.3
Average error
7.5
6.5
27.0
• Supero
– All 146 light events detected, no false alarm, no miss
– Comparable to Oracle
16 / 23
Outline
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Data center thermal monitoring
Residential electricity usage profiling
Real-time volcano monitoring
Aquatic process profiling
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Volcano Hazards
Eruption in Chile, 6/4, 2011
$68 M instant damage, $2.4 B future relief.
www.boston.com/bigpicture/2011/06/volcano_erupts_in_chile.html
Eruptions in Iceland 2010
A week-long airspace closure
[Wikipedia]
• 7% world population live near active volcanoes
• 20 - 30 explosive eruptions/year
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Volcano Monitoring
• Seismic activity monitoring
– Earthquake localization, tomography, early warning etc.
• Traditional seismometer
– Expensive (~$10K/unit), difficult to install & retrieve
– Only ~10 nodes installed for most threatening volcanoes!
Photo credit: USGS, http://volcanoes.usgs.gov/activity/methods/
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VolcanoSRI Project
• Large-scale, long-term deployment
– Up to 500 nodes on an active volcano in Ecuador
– Sampling@100Hz, several month lifetime
• Collaborative in-network processing
– Detection, timing, localization
– 4D tomography computation
The tentative deployment map at Ecuador
(Photo credits: Prof. Jonathan Lees)
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Current Work
• Smartphone-based sensing platform
• Distributed earthquake detection/timing algorithms
• Field deployment in 2012 in Tungurahua, Ecuador
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Aquatic Environment Monitoring
• Monitoring aquatic ecosystems is critical for
urban planning, clean water, etc.
• Traditional approaches
– Boats, sea sliders, etc.
• Our approach
– Robotic fish, collaborative sensing and actuation
HABs in a lake
Boat sensing
Robotic fish
photo credits: Prof. E. Litchman and Prof. Xiaobo Tan
Representative Publications
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Nemo: A High-fidelity Noninvasive Power Meter System for Wireless Sensor Networks, The 12th ACM/IEEE Conference on
Information Processing in Sensor Networks (IPSN), acceptance ratio: 24/115=21%, SPOTS Best Paper Award.
Supero: A Sensor System for Unsupervised Residential Power Usage Monitoring, 11th IEEE International Conference on Pervasive
Computing and Communications (PerCom), 2013, acceptance ratio: 18/170 = 10.6%, Best Paper Award Runner-up.
Beyond Co-existence: Exploiting WiFi White Space for ZigBee Performance Assurance, The 18th IEEE International Conference on Network
Protocols (ICNP), Kyoto, Japan, October 5-8, 2010, acceptance ratio: 31/170 = 18.2%, Best Paper Award.
Passive Interference Measurement in Wireless Sensor Networks, The 18th IEEE International Conference on Network Protocols (ICNP), Kyoto,
Japan, October 5-8, 2010, acceptance ratio: 31/170 = 18.2%, Best Paper Candidate (6 out of 170 submissions).
Volcanic Earthquake Timing using Wireless Sensor Networks, The 12th ACM/IEEE Conference on Information Processing in Sensor Networks
(IPSN), acceptance ratio: 24/115=21%.
Quality-driven Volcanic Earthquake Detection using Wireless Sensor Networks, The 31st IEEE Real-Time Systems Symposium (RTSS),
November 30 - December 3, 2010, San Diego, CA, USA.
Fidelity-Aware Utilization Control for Cyber-Physical Surveillance Systems, The 31st IEEE Real-Time Systems Symposium (RTSS), November 30
- December 3, 2010, San Diego, CA, USA.
ZiFi: Wireless LAN Discovery via ZigBee Interference Signatures, The 16th Annual International Conference on Mobile Computing and
Networking (MobiCom), Chicago, USA, September 2010, acceptance ratio: 33/233=14.2%.
Negotiate Power and Performance in the Reality of RFID Systems, The 8th Annual IEEE International Conference on Pervasive Computing and
Communications (PerCom), 2010, acceptance ratio: 27/227=12%, Best Paper Candidate (3 out of 227 submissions) .
Adaptive Calibration for Fusion-based Wireless Sensor Networks, The 29th Conference on Computer Communications (INFOCOM), March 1519, 2010, San Diego, CA, USA, acceptance ratio: 276/1575=17.5%.
Total number of citations since 2003: 3,800