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
(rich)
sensing
networking &
computation
people
traditional data push: from sensors to people
(rich)
sensing
networking &
computation
people
CPS/DDDAS: closed-loop “pull”; user driven
CPS: data centers (monitoring and control)
data presentation
power systems
cooling systems
heat, humidity
sensors
computation
resource analysis
scheduling, optimization,
control
computers, storage
control: VM storage, migration, cooling,
energy consumption, scheduling
CPS: Smart Grid
(next-gen electricity systems)
energy consumers: smart buildings,
Home, cars, appliances
computation
resource analysis,
prediction, scheduling,
optimization,
control: supply/demand balance,
power routing, energy prediction/pricing
signals, energy market info,
energy producers: power
plants, solar& wind farms
CPS: hazardous weather sensing
data storage
Cyril
Chickash
a
Rush
Springs
Lawto
n
radars
(sensors)
MC&C: Meteorological
command and control
radar control: sense
when and where user
needs are greatest
resource allocation,
optimization
computation,
communication
CASA: Collaborative Adaptive Sensing of the Atmosphere
end users:
NWS,
emergency
response
Common themes:


rich sensors: on beyond “motes”
closed loop, real time control
sensing


networking
computation
and control
people
complex multifunctional systems: need for architecture
 client-server, P2P, data-driven-sense-and-response
critical infrastructure: on beyond “best effort”
NEXRAD (current US weather sensing system)


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”
CASA: dense network of inexpensive,
short range radars
snow
wind
3.05 km
5.4 km
2 km
4 km
10,000 ft
1 km
3.05 km
instead of this….
gap
tornado
Horz. Scale: 1” = 50 km
Vert. Scale: 1” -=- 2 km
0
40
80
120
160
RANGE (km)
200
240
CASA: dense network of inexpensive,
short range radars
this:
snow
wind
tornado
0
40
80
120
160
RANGE (km)
200
3.05 km
3.05 km
10,000 ft
240
CASA: dense network of inexpensive,
short range radars
this:




see close to ground
finer spatial
resolution
beam focus: more
energy into sensed
volume
multiple looks: sense
volume with most
appropriate radars
Oklahoma 4-node test bed
Cyril
Chickasha
Rush
Springs
Norman OK
(NOC)
Lawton
Testbed: observations
CASA
observations
sector
scans at
multiple
elevations
CASA High
Resolution
Data
NEXRAD
Comparison
CASA: information, control everywhere
storage
1 Mbps (moment)
100 Mbps (raw)
streaming
storage
data
MC&C: Meteorological
command and control
query
interface
Meteorological
Detection
Algorithms
Feature Repository
blackboard
prediction
SNR
data
30 sec.
“heartbeat”
policy
Resource planning,
optimization
resource allocation
A
B
C
D
E
F
G
H
I
J
K
1
G3
G3
G3
G3
G3
G3
G3
R1
R1
R1
R1
2
3
4
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
R1
R2
R2
R1
F2,H2
F1
F2,
H1,F1 H1,F1 T2,R1
H1
T2,H1 T2,R1
5
G3
G3
G3
G3
G3
G3
G3
R1
R1
R1
R1
6
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
7
G3
G3
G3
G3
G3
G3
G3
C2
C2
C2
G3
Meteorological
Task
Generation
8
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
9
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
End users: NWS,
emergency
response
CASA: information, control everywhere
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
blackboard
A
B
C
D
E
F
G
H
I
J
K
SNR
data
policy
Resource planning,
optimization
resource allocation
1
G3
G3
G3
G3
G3
G3
G3
R1
R1
R1
R1
2
3
4
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
R1
R2
R2
R1
F2,H2
F1
F2,
H1,F1 H1,F1 T2,R1
H1
T2,H1 T2,R1
5
G3
G3
G3
G3
G3
G3
G3
R1
R1
R1
R1
6
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
7
G3
G3
G3
G3
G3
G3
G3
C2
C2
C2
G3
Meteorological
Task
Generation
8
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
9
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
G3
End users: NWS,
emergency
response
Smart Grid: Physical Infrastructure
distributed
generation
operations
substations
home
business
industry
substations
transmission
distribution
Grid power distribution network
generation
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 control
PMUs: measure substation voltage, current msecs
generation: distributed sources
demand reponse, pricing
AMI: advanced metering infrastructure
Smart Grid: info, control dissemination
SCADA: simple centralized polliing

inadequate as # data producers, consumers
increase
pub-sub: data, control dissemination:


quasi-centralization consistent with Internet trend
 separating control from data switching
 centralization (RCP, 4D)
challenges: reliability, manageability, security
Reflection: what can the Internet teach us?
Keshav’s hypothesis
Internet technologies, research developed over
past 40 years, can be used to green the grid



similarities (on the surface):
 power routing = internet flow routing
 grid management = network management
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?
Keshav’s 2nd hypothesis
The next decade will determine the structure of
the grid in 2120
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 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!