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

2. networking & computation people (rich) sensing

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

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

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

6. CPS: Smart Grid (next-gen electricity systems) energy consumers: smart buildings, Home, cars, appliances energy producers: power plants, solar& wind farms energy consumers: smart buildings, Home, cars, appliances energy producers: power plants, solar& wind farms computation resource analysis, prediction, scheduling, optimization, control: supply/demand balance, power routing, energy prediction/pricing signals, energy market info,

7. end users: NWS, emergency response Cyril Rush Springs Chickash a Lawto n 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

8. 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 networking computation and control people

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

11. CASA: dense network of inexpensive, short range radars instead of this….

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

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

16. CASA: information, control everywhere blackboard 1 Mbps (moment) 100 Mbps (raw) 30 sec. “heartbeat” prediction

17. CASA: information, control everywhere End users: NWS, emergency response Resource planning, optimization data policy resource allocation SNR F2,H2R1G3C2G3 JR1H1,F1H1,F1T2,R1R1G3C2G3 KR1H1T2,H1T2,R1R1G3 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

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

19. Smart Grid: power flows FACTS:  control line impedance: actively route 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 demand reponse, pricing AMI: advanced metering infrastructure

21. 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

22. 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

23. 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 2 nd hypothesis …… 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!