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@scale: Insights from a Large, Long-Lived Appliance Energy WSN

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  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 US Department of Energy • Representative sample of plug-load power and energy • Capture traces, usage patterns, and models IPSN 2012: Beijing, China

  3. @scale • 90k ft2/4 floor building • 1 year deployed • 5000 plug-loads • 460 plug-load meters • 7 edge routers • 650m data points Scale to hundreds of meters over multiple floors Evaluate networking dynamics Maximize accuracyof inexpensive meters IPSN 2012: Beijing, China

  4. Study methodology IPSN 2012: Beijing, China

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

  6. Next • Application design • Network lessons IPSN 2012: Beijing, China

  7. Interaction http://www.screenr.com/ydh8 IPSN 2012: Beijing, China

  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 IPSN 2012: Beijing, China

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

  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 IPSN 2012: Beijing, China

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

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

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

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

  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 IPSN 2012: Beijing, China

  16. What makes up building 90 plugs 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 Computers 50% of energy Task Lighting 7% Networking 6% Other 7% Misc. HVAC 10% of energy Displays 10% of energy Imaging 10% of energy IPSN 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 IPSN 2012: Beijing, China

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

  19. BACKUPS IPSN 2012: Beijing, China

  20. Network co-development and deployment 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 IPSN 2012: Beijing, China

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

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

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

  25. Introduction • Energy science goals • Computer science goals • Related Work • Deployments • Study overview • System design & architecture • Results IPSN 2012: Beijing, China

  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 IPSN 2012: Beijing, China

  27. Methodology: Multipoint Calibration • Automated 20-point calibration on every meter ⇒ 3-part piecewise calibration • 90thpercentile error is <2 Watts across 1000 devices IPSN 2012: Beijing, China

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

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

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

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

  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.” “Because the cost of data collection is high … between 1% and 12% of participating customers were metered.” 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. IPSN 2012: Beijing, China

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

  34. Inform new appliance standards 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 IPSN 2012: Beijing, China

  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 IPSN 2012: Beijing, China

  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 IPSN 2012: Beijing, China