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Sensors in Sustainability

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 &

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Sensors in Sustainability

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  1. Sensors in Sustainability Jim Kurose Department of Computer Science University of Massachusetts Amherst MA USA NSF WICS Workshop Salt Lake City

  2. (rich) sensing networking & computation people

  3. traditional data push: from sensors to people (rich) sensing networking & computation people

  4. (rich) sensing networking & computation people CPS/DDDAS: closed-loop “pull”; user driven

  5. data presentation computers, storage computation resource analysis scheduling, optimization, control heat, humidity sensors control: VM storage, migration, cooling, energy consumption, scheduling CPS: data centers (monitoring and control) power systems cooling systems

  6. 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: Smart Grid (next-gen electricity systems) energy consumers: smart buildings, Home, cars, appliances energy producers: power plants, solar& wind farms

  7. MC&C: Meteorological MC&C: Meteorological MC&C: Meteorological command and control command and control command and control data storage data storage data storage Cyril Chickasha Rush Springs radar control: sense when and where user needs are greatest resource allocation, optimization resource allocation, optimization resource allocation, optimization Lawton radars (sensors) end users: NWS, emergency response computation, communication CPS: hazardous weather sensing CASA:Collaborative Adaptive Sensing of the Atmosphere

  8. Common themes: • rich sensors: on beyond “motes” • closed loop, real time control computation and control sensing networking people • complex multifunctional systems: need for architecture • client-server, P2P, data-driven-sense-and-response • critical infrastructure: on beyond “best effort”

  9. 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)

  10. NEXRAD (current US weather sensing system) Observational Data “Push”

  11. gap 4 km 2 km 1 km 5.4 km CASA: dense network of inexpensive, short range radars instead of this…. 10,000 ft 3.05 km snow wind 3.05 km tornado earth surface Horz. Scale: 1” = 50 km Vert. Scale: 1” -=- 2 km 0 40 120 160 200 80 240 RANGE (km)

  12. 10,000 ft 3.05 km snow wind 3.05 km tornado earth surface 0 40 120 160 200 80 240 RANGE (km) CASA: dense network of inexpensive, short range radars this:

  13. 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:

  14. Cyril Chickasha Rush Springs Lawton Oklahoma 4-node test bed Norman OK (NOC)

  15. sector scans at multiple elevations Testbed: observations CASA High Resolution Data NEXRAD Comparison CASA observations

  16. MC&C: Meteorological data command and control storage query Meteorological interface streaming Detection storage Algorithms Feature Repository 1 2 3 4 5 6 7 8 9 A G3 G3 G3 G3 G3 G3 G3 G3 G3 B G3 G3 G3 G3 G3 G3 G3 G3 G3 C G3 G3 G3 G3 G3 G3 G3 G3 G3 D G3 G3 G3 G3 G3 G3 G3 G3 G3 E G3 G3 G3 G3 G3 G3 G3 G3 G3 F G3 G3 G3 G3 G3 G3 G3 G3 G3 G G3 G3 G3 G3 G3 G3 G3 G3 G3 H R1 R1 R2 R2 R1 G3 C2 G3 G3 F 2,H2 R1 G3 C2 G3 G3 R1 I R1 F 1 F 2, J R1 H1 , F1 H1 , F1 T 2,R1 R1 G3 C2 G3 G3 K R1 H1 T 2,H1 T 2,R1 R1 G3 G3 G3 G3 End users: NWS, emergency response SNR policy data Resource planning, Meteorological optimization Task resource allocation Generation CASA: information, control everywhere 1 Mbps (moment) 100 Mbps (raw) blackboard prediction 30 sec. “heartbeat”

  17. 1 2 3 4 5 6 7 8 9 A G3 G3 G3 G3 G3 G3 G3 G3 G3 B G3 G3 G3 G3 G3 G3 G3 G3 G3 C G3 G3 G3 G3 G3 G3 G3 G3 G3 D G3 G3 G3 G3 G3 G3 G3 G3 G3 E G3 G3 G3 G3 G3 G3 G3 G3 G3 F G3 G3 G3 G3 G3 G3 G3 G3 G3 G G3 G3 G3 G3 G3 G3 G3 G3 G3 H R1 R1 R2 R2 R1 G3 C2 G3 G3 R1 I R1 F 1 F 2, Meteorological Task Generation 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 F 2,H2 R1 G3 C2 G3 G3 J R1 H1 , F1 H1 , F1 T 2,R1 R1 G3 C2 G3 G3 K R1 H1 T 2,H1 T 2,R1 R1 G3 G3 G3 G3 End users: NWS, emergency response SNR policy data Resource planning, optimization resource allocation

  18. Smart Grid: Physical Infrastructure operations distributed generation substations transmission substations distribution generation home business Grid power distribution network industry

  19. Smart Grid: power flows FACTS: • control line impedance:activelyroute power • Internet-like “traffic engineering: control amount of flow going over each line

  20. Smart Grid: information, control everywhere data, real-time control PMUs: measure substation voltage, current msecs generation: distributed sources demandreponse, pricing AMI: advanced metering infrastructure

  21. 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

  22. 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

  23. Reflection: what can the Internet teach us? Keshav’s2nd 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

  24. Take home: • rich sensors: on beyond “motes” • closed loop, real time control: sense and response • smart grid: • data (sensor) rich • transition underway … help needed!

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