1. @scale: Insights from a Large, Long-Lived Appliance Energy WSN Stephen Dawson-Haggerty, Steven Lanzisera, Jay Taneja, Rich Brown, and David Culler Computer Science Division, University of California, Berkeley Environmental Energy Technologies Division, Lawrence Berkeley National Lab

2. Motivation April 17, 2012IPSN 2012: Beijing, China2 US Department of Energy Representative sample of plug-load power and energy Capture traces, usage patterns, and models

3. @scale April 17, 2012IPSN 2012: Beijing, China3 90k ft 2 /4 floor building 1 year deployed 5000 plug-loads 460 plug-load meters 7 edge routers 650m data points Evaluate networking dynamics Scale to hundreds of meters over multiple floors Maximize accuracy of inexpensive meters

4. Study methodology Hardware (2009) Calibration Safety testing Testing and Setup Software validation Installation Device inventory Stratified sampling Operation (2011) Maintenance and debugging April 17, 2012IPSN 2012: Beijing, China4

5. System architecture April 17, 2012IPSN 2012: Beijing, China5 6LoWPAN at the edge ⇒ access network ⇒ data closet

6. Next Application design Network lessons April 17, 2012IPSN 2012: Beijing, China6

7. Interaction April 17, 2012IPSN 2012: Beijing, China7 http://www.screenr.com/ydh8

8. Application design Application/IPv6 allows scripted interaction with a large number of motes –Upload calibration tables –Modify reporting destination –Change MAC parameters –Avoid reprogramming Interaction uses standard tools April 17, 2012IPSN 2012: Beijing, China8 Time StampDescription LocalTimeCounter since reboot GlobalTimeUNIX time SequenceMonotonic data sequence number InsertTimeTime at database

9. System architecture: HYDRO principles Maintains multiple next-hop options –Manage explore/exploit tradeoff Horizontally scalable with multiple Load Balancing R0uters (LBRs) April 17, 2012IPSN 2012: Beijing, China9

10. Networking data set 5-minute snapshots –Top four links from each network node, as reported to the edge –Link and device churn are common –Mean network degree is at least 16, diameter is about 4.5 –Analysis performed March-April 2011 April 17, 2012IPSN 2012: Beijing, China10

11. Network data yield April 17, 2012IPSN 2012: Beijing, China11 Most days exhibit 5 th percentile data yield > 99%

12. Device dynamics When do devices come on/off? Device reset detector based on LocalTime rollover April 17, 2012IPSN 2012: Beijing, China12

13. Exploration is ongoing April 17, 2012IPSN 2012: Beijing, China13 Path length and router degree show diurnal and weekly variation

14. Is there a single, stable routing tree? April 17, 2012IPSN 2012: Beijing, China14 Exploration of new potential candidate links is continuous Diameter increases by factor of 2 using only “stable” links stable links all links L t : reported link set at time “t”

15. Lessons: networking and management No such thing as a “static” network Scriptability/automated management is key Data reliability is not all about the wireless part: “Internet” and “practical” considerations –Back up or replicate your database –Local buffering at the end points, middle-boxes? –Meters walk away –Sometimes the whole building is turned off Horizontal scalability is a must April 17, 2012IPSN 2012: Beijing, China15

16. What makes up building 90 plugs energy? 16 Computers 50% of energy Displays 10% of energy Task Lighting 7% Networking 6% Other 7% Imaging 10% of energy Misc. HVAC 10% of energy Timer controlled plug strips? 75 MWh/year 30% of non-computer plug total 6% of building total Computer power management? 150 MWh/year 60% of computer total 12% of building total April 17, 2012IPSN 2012: Beijing, China

17. Conclusions Toolkit for domain scientists needed –802.15.4e, RPL-based networking –Common hardware problem –Exploding the instrument is possible! Further standardization: CoAP 90% solutions Overall theme: careful simplicity April 17, 2012IPSN 2012: Beijing, China17

18. QUESTIONS Special thanks to Sara Alspaugh, Alice Chang, Iris Cheung, Albert Goto, Xiaofan Jiang, Shelley Kim, Margarita Kloss, Judy Lai, and Ken Lutz April 17, 2012IPSN 2012: Beijing, China18

19. BACKUPS April 17, 2012IPSN 2012: Beijing, China19

20. Network co-development and deployment April 17, 2012IPSN 2012: Beijing, China20 2005: Redwoods 2007: RFC4944: 6LoWPAN 2008: Full sensornet IP architecture proposed 2008: BLIP/Contiki 6LoWPAN released 2009: GreenOrbs (1000 Nodes) 2010: Collection Tree Protocol 2012: RFC6553: RPL, IEEE 802.15.4e 2012: @scale

21. How Common is Computer-Display Power Management? 21 40 Hour Work Week PM w/ breaks Rarely power down monitor 83% of monitors use power management 15% use it with breaks for days at a time 2% do not use it April 17, 2012IPSN 2012: Beijing, China

22. What is the Distribution of LCD Computer Display Energy Use? 22 N=118 April 17, 2012IPSN 2012: Beijing, China

23. How Common is Desktop Computer Power Management? 23 40 Hour Work Week 39% rarely powered down 44% managed April 17, 2012IPSN 2012: Beijing, China

24. Findings and Next Steps Bldg 90 network demonstrated large-scale, end-to-end WSN and collected a lot of useful data –IT equipment should be focus of office energy management programs –Using data for LBNL-wide plug-load management Inventory and meter installation are labor-intensive –Exploring using public (homeowner & building occupant) participation for data collection –Integrate metering & communications into products Robust sensor network needs more engineering –? Evaluate commercial products now available Electricity only part of buildings energy problem –Developing low-cost WSN for gas and water metering 24April 17, 2012IPSN 2012: Beijing, China

25. Introduction –Energy science goals –Computer science goals Related Work –Deployments Study overview System design & architecture Results April 17, 2012IPSN 2012: Beijing, China25

26. MELS: Miscellaneous Electric Loads Large, rigorous study of miscellaneous electric loads (mostly plugs) –Roughly 1/3 of building energy consumption –Difficult to study due to large number of small consumers April 17, 2012IPSN 2012: Beijing, China26

27. Methodology: Multipoint Calibration April 17, 2012IPSN 2012: Beijing, China27 Automated 20-point calibration on every meter ⇒ 3-part piecewise calibration 90 th percentile error is <2 Watts across 1000 devices

28. Identify when metered devices change 28 Chang from older 20” LCD to new 24” LCD Increase screen area 44%; reduce energy 33%. April 17, 2012IPSN 2012: Beijing, China

29. How Much of Whole Building is Plugs? 29 All Building Electricity 40% of Building Electricity 3 month weekday average: March, April, May Note: no cooling during these months Projected based on full inventory and sample weights All Plugs April 17, 2012IPSN 2012: Beijing, China

30. Building 90 Device Inventory & Energy 30April 17, 2012IPSN 2012: Beijing, China

31. Impact $18 billion spent on Demand Side Management from 1990-1998 April 17, 2012IPSN 2012: Beijing, China31 Plug loads make up ⅓ of the $100 billion per year spent on commercial building energy [2005]

32. In situ: evaluation of energy savings “End-use metering is often regarded as the most accurate savings evaluation methodology because it measures the quantities most directly related to energy savings.” April 17, 2012IPSN 2012: Beijing, China32 Joseph Eto, Suzie Kito, Leslie Shown, and Richard Sonnenblick. “Where Did the Money Go? The Cost and Performance of the Largest Commercial Sector DSM Programs.” in Energy Journal, Vol. 21, No. 2. 2000. “Because the cost of data collection is high … between 1% and 12% of participating customers were metered.”

33. Standards coverage Energy standards cover many of the other energy-consuming loads EnergyStar, mandatory standards April 17, 2012IPSN 2012: Beijing, China33

34. Inform new appliance standards April 17, 2012IPSN 2012: Beijing, China34 S. Meyers, J.E. McMahon, M. McNeil, X. Liu. “Impacts of US federal energy efficiency standards for residential appliances.” In Energy 28 (2003) 755-768

35. Energy science goals Plug loads make up ⅓ of the $100 billion per year spent on commercial building energy [2005] –Representative sample of plug-load power and energy –Capture traces, usage patterns, and models –Study correlations between workplace devices, e.g. computers, displays, lighting April 17, 2012IPSN 2012: Beijing, China35

36. Computer science challenges Scale to hundreds of meters over multiple floors Maximize the accuracy of inexpensive meters Collect data for thorough networking dynamics study April 17, 2012IPSN 2012: Beijing, China36