Overview of Cyber-Physical Systems
Research
Guoliang Xing
Associate Professor
Department of Computer Science and Engineering
Michigan State University
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
2
Our CPS Projects
Tungurahua Volcano, Ecuador
Data Center Monitoring,
HPCC, MSU
•
•
•
•
•
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
Smartphone-based data-intensive CPS
Robotic fish, Smart Microsystems
Lab, MSU
3
Core Technical Capabilities
• 10+ years of experience of system research
– End-to-end CPS design, system integration
– Collaboration with experts from multiple domains
• Energy, environment, natural hazards, smart grid….
• Large-scale real-world CPS deployments
– Volcano, data center, Great Lakes…
• Multi-discipline technical expertise
– Hardware/software sensor system, signal
processing, predictive analytics, machine learning,
feed-back control, real-time
4
Honors & Awards
• 9 NSF Awards, total 3 million US dollars
• Faculty Early Career Development (CAREER) Award, National Science
Foundation, 2010
• Withrow Distinguished Junior Faculty Award, Michigan State University,
2014
• Best Paper Award, SPOTS Track, ACM/IEEE Conference on Information
Processing in Sensor Networks (IPSN), 2012
• Best Paper Award, IEEE International Conference on Network Protocols
(ICNP), 2010
• Best Paper Finalist, IPSN 2014, PerCom 2013, ICNP 2010, PerCom 2010,
SECOM 2014
• Third Best Mobile App, “iSleep: Unobtrusive Sleep Quality Monitoring”,
“iBreath: Breath Monitoring during Running”, Annual International
Conference on Mobile Computing and Networking (MobiCom), 2013, 2014
5
Outline
•
•
•
•
•
Data center thermal monitoring
Residential electricity usage profiling
Real-time volcano monitoring
Aquatic process profiling
Smartphone-based data-intensive CPS
6
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/.
7
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.
8
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)
Computational
Fluid Dynamics
Modeling
Real-time Prediction
Calibration
9
Data Center Experiment
• Testbed configuration
–
–
–
–
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
10
Experiment Results
• 12-day experiment
Outlet
Inlet
10-minute temperature prediction
11
Outline
•
•
•
•
•
Data center thermal monitoring
Residential electricity usage profiling
Real-time volcano monitoring
Aquatic process profiling
Smartphone-based data-intensive CPS
12
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 …
….
….
….
13 / 23
Our Solution: 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
• Baseline: False alarms caused by hair dryer and bath fan
16 / 23
Outline
•
•
•
•
•
Data center thermal monitoring
Residential electricity usage profiling
Real-time volcano/earthquake monitoring
Aquatic process profiling
Smartphone-based data-intensive CPS
17
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
18
Volcano/Earthquake 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/
19
System Architecture
Node Architecture
GPS
Antenna
XBee
Antenna
GPS
Receiver
SDCard
Arduino
Due
Processor
Board
XBee
Radio
24 Bit
ADC
Seismic
Amplifier
Seismic
Sensor
Deployments
• Ecuador - June 2013
– Detected event 20Km from Tungurahua Volcano
• Chile – January/March 2015
– 16 nodes plus base station
22
Outline
•
•
•
•
•
Data center thermal monitoring
Residential electricity usage profiling
Real-time volcano/earthquake monitoring
Aquatic process profiling
Smartphone-based data-intensive CPS
23
Aquatic Environment Monitoring
• Monitoring aquatic ecosystems is critical for
urban planning, public safety 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
Outline
•
•
•
•
•
Data center thermal monitoring
Residential electricity usage profiling
Real-time volcano/earthquake monitoring
Aquatic process profiling
Smartphone-based data-intensive CPS
25
Data-intensive Sensing Applications
Volcano Seismic Imaging
MSU news:
http://www.cse.msu.edu/About/Notable.php?Nid=423
Cloud-based Robotic Vision
26
Data-intensive Sensing Applications
4D Volcano Seismic Imaging
100+ nodes, real-time sampling at
100Hz
Cloud-based Robotic Vision
local sensing, remote processing
5fps, 640*480px
27
Motivation
 Many sensing apps require in-network realtime processing of high-rate data
 Mote-based platforms?
 Telosb Motes: 48K bytes flash, 10K bytes RAM
 Poor programmability
 Single-board embedded platforms?
 E.g., Gumstix SheevaPlug and Raspberry Pi
 Not optimized for low-power sensing
 Lack of many comm./sensing modules
28
Advantages of Smartphones
 Rich computation and storage resources
 E.g., Moto-G with a quad-core CPU
 Rich comm. interfaces & sensing modalities
 WiFi, 3G/4G, Bluetooth
 Accel., camera, mic., compass, temp. and etc.
 User-friendly interface & programming
 Low cost
29
Limitations of Smartphones
 High power consumption
 Lack of real-time functionalities
 Highly variable sampling rate
 Poor time-stamping accuracy
 Poor hardware extensibility
 Lack of embedded programming support
 No resource-efficient data processing libraries
 No unified primitives for peripheral sensor control
30
ORBIT System
Application Pipeline
XML
JAVA
Task Partitioner
Processing
Library
Exec. profiler
Task Controller
Sampling &
timestamping
Msg.
protocol
IOIO
31
Arduino
ORBIT Features
A platform for data-intensive sensing apps





Smartphone-based multi-tier system
Dynamic task and data partitioning
Unified messaging protocol
Data processing library
Energy-efficiency, programmability, extensibility
Real Implementation/evaluation
 Microbenchmarks and 3 case studies
32
Acknowledgement
• Group members and collaborators
– 8 Ph.D + 3 postdoc
– Collaborators from UNC, W&M, Ohio State, CMU, PARC, Nokia
• National Science Foundation
– Total ~3M since 2009
– CDI, VolcanoSRI, 2011-2015 (in collaboration with WenZhan Song @
Georgia State University, Jonathan Lees@University of North Carolina,
Chapel Hill)
– CAREER, performance-critical sensor networks, PI, 2010-2015.
– ECCS, aquatic sensor networks, PI, 2010-2013 (in collaboration with
Xiaobo Tan @ MSU)
– CNS, real-time and performance control of networked sensor system, MSU
PI, 2012-2015 (in collaboration with Xiaorui Wang @ Ohio State)
– CNS, Interference in crowded spectrum, MSU PI, 2009-2012 (in
collaboration with Gang Zhou @ William & Mary)
33