Overview of Cyber-Physical Systems Research

Guoliang Xing

Associate ProfessorDepartment 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 solicitation135022

Our CPS Projects

• 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

 Tungurahua Volcano, Ecuador

Volcano Monitoring Sensors

Data Center Monitoring, HPCC, MSU

Harmful Algae Bloom in Lake Mendota in Wisconsin, 1999

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

• 23% server outages are heat-induced shutdowns

An aerial view of EMC's new data center in Durham, North Carolina2 An EMC data center 2

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

Calibration

Sensing(CPU, fan speed, temperature, airflow)

Real-time Prediction

Computational Fluid Dynamics

Modeling

geometric model (server/rack dimension and placement)

9

Data Center Experiment

• Testbed configuration– 5 racks, 229 servers, 2016 cores– 4 in-row CRAC units– 35 temperature sensors– 4 airflow sensors

• Dynamic CPU utilization

Airflow sensor

Temperature sensor

Chained Temp. sensor

In-row CRACs

In-row CRACs

10

Experiment Results

• 12-day experimentOutlet

Inlet

11

10-minute temperature prediction

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

• Rising cost– Increase by 75% in 10 years

• Understanding usage– Real-time power readings– Fine-grained usage info

Industrial25.5%

Residential36.7%

Commercial34.2%

Others

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: SuperoSmart meter

Light and acoustic sensors

Base station

Event Correlation(remove false alarm)

Event clustering

Event-Appliance Association

100W

‘+1’

Light/acoustic event Power reading14 / 23

Light + acoustic captures90% power consumption

Implementation & Deployments

• System– TelosB/Iris + TED5000 + KAW ground truth meters

• Five deployments– Three apartments (40~150 m2), two houses– 9 ~ 22 sensors

TelosB (light)Iris (acoustic) Kill-A-Watt Apartment-1 deployment

15 / 23

10-day Results

• 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

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

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

• 7% world population live near active volcanoes• 20 - 30 explosive eruptions/year

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 2010A week-long airspace closure[Wikipedia]

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 Receiver

Seismic Amplifier

Arduino Due

ProcessorBoard

XBee Radio

Seismic Sensor

GPS Antenna

24 Bit ADC

XBee Antenna

SDCard

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

Robotic fishHABs in a lake Boat sensingphoto 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

26

Data-intensive Sensing ApplicationsVolcano Seismic Imaging

MSU news: http://www.cse.msu.edu/About/Notable.php?Nid=423 Cloud-based Robotic Vision

27

Data-intensive Sensing Applications

4D Volcano Seismic Imaging

100+ nodes, real-time sampling at 100Hz

Cloud-based Robotic Vision

local sensing, remote processing5fps, 640*480px

28

Motivation

Many sensing apps require in-network real-time 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

29

Advantages of Smartphones Rich computation and storage resources

E.g., Moto-G with a quad-core CPU

Rich comm. interfaces & sensing modalitiesWiFi, 3G/4G, BluetoothAccel., camera, mic., compass, temp. and etc.

User-friendly interface & programming Low cost

30

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

31

ORBIT System

Msg. protocol

Task Controller

Sampling & timestampingExec. profiler

Processing Library

Application Pipeline

Task Partitioner

XML

JAVA

IOIO Arduino

32

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

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