Sensors in Sustainability

Jim KuroseDepartment of Computer ScienceUniversity of MassachusettsAmherst MA USA

NSF WICS Workshop Salt Lake City

networking &computation people

(rich)sensing

networking &computation people

traditional data push: from sensors to people

(rich)sensing

CPS/DDDAS: closed-loop “pull”; user driven

(rich)sensing

networking &computation people

heat, humidity sensors

CPS: data centers (monitoring and control)

power systems

cooling systems

computers, storage

computation

data presentation

resource analysisscheduling, optimization,control

control: VM storage, migration, cooling, energy consumption, scheduling

CPS: Smart Grid (next-gen electricity systems)

energy consumers: smart buildings,Home, cars, appliances

energy producers: powerplants, solar& wind farms

energy consumers: smart buildings,Home, cars, appliances

energy producers: powerplants, solar& wind farms

computationresource analysis,prediction, scheduling, optimization,

control: supply/demand balance, power routing, energy prediction/pricing signals, energy market info,

end users:NWS,

emergencyresponse

Cyril

RushSprings

Chickasha

Lawton

radars (sensors)

CPS: hazardous weather sensing

data storage

resource allocation,optimization

MC&C: Meteorological command and control

computation,communication

data storage

resource allocation,optimization

MC&C: Meteorological command and control

data storage

resource allocation,optimization

MC&C: Meteorological command and control

CASA: Collaborative Adaptive Sensing of the Atmosphere

radar control: sense when and where user needs are greatest

Common themes:

rich sensors: on beyond “motes” closed loop, real time control

complex multifunctional systems: need for architecture client-server, P2P, data-driven-sense-and-response

critical infrastructure: on beyond “best effort”

sensing networkingcomputationand control people

158 radars operated by NOAA

230 km Doppler mode, 460 km reflectivity-only mode 3 km coverage floor

“surveillance mode”: sit and spin

NEXRAD (current US weather sensing system)

Observational Data “Push”

NEXRAD (current US weather sensing system)

10,000 ft

tornado

wind

earth surface

snow

3.05

km

3.05

km

0 40 80 120 160 200 240RANGE (km)

Horz. Scale: 1” = 50 kmVert. Scale: 1” -=- 2 km

5.4

km

1 km 2

km

4 km

gap

CASA: dense network of inexpensive, short range radars

instead of this….

CASA: dense network of inexpensive, short range radars

this:

10,000 ft

tornado

wind

earth surface

snow

3.05

km

3.05

km

0 40 80 120 160 200 240RANGE (km)

CASA: dense network of inexpensive, short range radars

see close to ground finer spatial

resolution beam focus: more

energy into sensed volume

multiple looks: sense volume with most appropriate radars

this:

Oklahoma 4-node test bed

Cyril

RushSprings

Chickasha

Lawton

Norman OK(NOC)

NEXRAD Comparison

CASA High Resolution Data

Testbed: observations

sector scans at multiple elevations

CASAobservations

CASA: information, control everywhere

streamingstorage

storage

queryinterface

data

End users: NWS,emergencyresponse

Resource planning,optimization

data policy

resource allocation

SNR

Meteorological DetectionAlgorithms

1 2 3 4 5 6 7 8 9A G3 G3 G3 G3 G3 G3 G3 G3 G3B G3 G3 G3 G3 G3 G3 G3 G3 G3C G3 G3 G3 G3 G3 G3 G3 G3 G3D G3 G3 G3 G3 G3 G3 G3 G3 G3E G3 G3 G3 G3 G3 G3 G3 G3 G3F G3 G3 G3 G3 G3 G3 G3 G3 G3G G3 G3 G3 G3 G3 G3 G3 G3 G3H R1 R1 R2 R2 R1 G3 C2 G3 G3I R1 F1 F2, R1 F2,H2 R1 G3 C2 G3 G3J R1 H1,F1 H1,F1 T2,R1 R1 G3 C2 G3 G3K R1 H1 T2,H1 T2,R1 R1 G3 G3 G3 G3

Feature Repository

MC&C: Meteorological command and control

Meteorological Task

Generation

blackboard

1 Mbps (moment)100 Mbps (raw)

30 sec. “heartbeat”

prediction

CASA: information, control everywhere

End users: NWS,emergencyresponse

Resource planning,optimization

data policy

resource allocation

SNR

1 2 3 4 5 6 7 8 9A G3 G3 G3 G3 G3 G3 G3 G3 G3B G3 G3 G3 G3 G3 G3 G3 G3 G3C G3 G3 G3 G3 G3 G3 G3 G3 G3D G3 G3 G3 G3 G3 G3 G3 G3 G3E G3 G3 G3 G3 G3 G3 G3 G3 G3F G3 G3 G3 G3 G3 G3 G3 G3 G3G G3 G3 G3 G3 G3 G3 G3 G3 G3H R1 R1 R2 R2 R1 G3 C2 G3 G3I R1 F1 F2, R1 F2,H2 R1 G3 C2 G3 G3J R1 H1,F1 H1,F1 T2,R1 R1 G3 C2 G3 G3K R1 H1 T2,H1 T2,R1 R1 G3 G3 G3 G3

Meteorological Task

Generation

blackboard

user utility: utility of particular sensing configuration sensed-state- and time-dependent; per-user group optimized myopically at each time step validated with end users

transmission

homebusiness

industry

substations

substations

distribution

distributed generation

operations

Grid power distribution network

generation

Smart Grid: Physical Infrastructure

Smart Grid: power flows

FACTS: control line impedance: actively route power Internet-like “traffic engineering: control

amount of flow going over each line

Smart Grid: information, control everywhere

data, real-time controlPMUs: measure substation voltage, current msecs generation: distributed sourcesdemand reponse, pricingAMI: advanced metering infrastructure

Smart Grid: info, control dissemination

pub-sub: data, control dissemination: quasi-centralization consistent with Internet trend

separating control from data switching centralization (RCP, 4D)

challenges: reliability, manageability, security

SCADA: simple centralized polliing inadequate as # data producers, consumers

increase

Reflection: what can the Internet teach us?

similarities (on the surface): power routing = internet flow routing grid management = network management

Internet technologies, research developed over past 40 years, can be used to green the grid

Keshav’s hypothesis

but…. Internet best effort service model won’t cut it manageability, security, reliability (five 9’s) not yet

Internet main strengths research needed: smart grid architecture, protocols

networking, distributed systems real-time systems

Reflection: what can the Internet teach us?

architecture: punctuated equilibrium? today’s IP v4: 30+ years old today’s meteorological sensing network: 30+ years old telephone network: manual to stored-program-control

to IP over 100 years

The next decade will determine the structure of the grid in 2120

Keshav’s 2nd hypothesis

…… the time is indeed now

Take home:

rich sensors: on beyond “motes” closed loop, real time control: sense and

response smart grid:

data (sensor) rich transition underway … help needed!