sensors in sustainability
DESCRIPTION
Sensors in Sustainability. Jim Kurose Department of Computer Science University of Massachusetts Amherst MA USA. NSF WICS Workshop Salt Lake City. (rich) sensing. networking & computation. people. traditional data push: from sensors to people. (rich) sensing. networking & - PowerPoint PPT PresentationTRANSCRIPT
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Sensors in Sustainability
Jim KuroseDepartment of Computer ScienceUniversity of MassachusettsAmherst MA USA
NSF WICS Workshop Salt Lake City
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networking &computation people
traditional data push: from sensors to people
(rich)sensing
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CPS/DDDAS: closed-loop “pull”; user driven
(rich)sensing
networking &computation people
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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
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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,
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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
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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
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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)
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Observational Data “Push”
NEXRAD (current US weather sensing system)
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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….
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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)
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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:
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Oklahoma 4-node test bed
Cyril
RushSprings
Chickasha
Lawton
Norman OK(NOC)
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NEXRAD Comparison
CASA High Resolution Data
Testbed: observations
sector scans at multiple elevations
CASAobservations
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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
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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
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transmission
homebusiness
industry
substations
substations
distribution
distributed generation
operations
Grid power distribution network
generation
Smart Grid: Physical Infrastructure
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Smart Grid: power flowsFACTS: control line impedance: actively route power Internet-like “traffic engineering: control
amount of flow going over each line
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Smart Grid: information, control everywhere
data, real-time controlPMUs: measure substation voltage, current msecs generation: distributed sourcesdemand reponse, pricingAMI: advanced metering infrastructure
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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
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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
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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
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Take home: rich sensors: on beyond “motes” closed loop, real time control: sense and
response smart grid:
data (sensor) rich transition underway … help needed!