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Smart Sensing Networks for Sustainable Infrastructure Management

Explore the integration of sensors in sustainability efforts, focusing on closed-loop data-driven systems for efficient resource allocation and optimization in various applications such as power systems, smart buildings, and weather monitoring. Learn about Collaborative Adaptive Sensing of the Atmosphere (CASA) and its impact on meteorological command and control. This workshop dives into the use of rich sensors, real-time control, and architecture design for critical infrastructure. Gain insights on sensor networks' roles in ensuring efficient energy consumption and hazard detection, with a special emphasis on Smart Grid technologies and meteorological data management.

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Smart Sensing Networks for Sustainable Infrastructure Management

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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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