@scale: Insights from a Large, Long-Lived Appliance Energy WSN
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@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
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@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. - PowerPoint PPT Presentation
Transcript of @scale: Insights from a Large, Long-Lived Appliance Energy WSN
sMAP: Integrating and Managing Physical Data
@scale: Insights from a Large, Long-Lived Appliance Energy WSNStephen Dawson-Haggerty, Steven Lanzisera, Jay Taneja, Rich Brown, and David Culler
Computer Science Division, University of California, BerkeleyEnvironmental Energy Technologies Division, Lawrence Berkeley National Lab
1Motivation
April 17, 2012IPSN 2012: Beijing, China2US Department of EnergyRepresentative sample of plug-load power and energy
Capture traces, usage patterns, and models
@scaleApril 17, 2012IPSN 2012: Beijing, China3
90k ft2/4 floor building1 year deployed5000 plug-loads460 plug-load meters7 edge routers650m data points
Evaluate networking dynamicsScale to hundreds of meters over multiple floorsMaximize accuracy of inexpensive meters
Manual device inventory resulted in about 5,000 plug-load devices, more than 10 per occupant.
ACme meters previously presented at SenSys.3Study methodologyApril 17, 2012IPSN 2012: Beijing, China4
Pick up where Jiang 09 leaves off4System architectureApril 17, 2012IPSN 2012: Beijing, China5
6LoWPAN at the edge access network data closet
One routing domain among the meter network
No app-specific code on the LBRs: implementation of the IP architecture
Full adoption of the IP architecture
5NextApplication design
Network lessons
April 17, 2012IPSN 2012: Beijing, China6
Interaction
April 17, 2012IPSN 2012: Beijing, China7http://www.screenr.com/ydh8
$ Ifconfig tun0$ telnet :: 6106Helplinks $ tcpdump I tun0$ ping6 fec0::936$ blip-shell fec0::936readreadcfg pwrealcalreadcfg dest
7Application designApplication/IPv6 allows scripted interaction with a large number of motesUpload calibration tablesModify reporting destinationChange MAC parametersAvoid reprogrammingInteraction uses standard tools
April 17, 2012IPSN 2012: Beijing, China8Time StampDescriptionLocalTimeCounter since rebootGlobalTimeUNIX timeSequenceMonotonic data sequence numberInsertTimeTime at database
Limited development/engineering time available focus on whats important8System architecture: HYDRO principlesMaintains multiple next-hop optionsManage explore/exploit tradeoffHorizontally scalable with multiple Load Balancing R0uters (LBRs)April 17, 2012IPSN 2012: Beijing, China9
Published 2010, similar to RPL non-storing mode.
Trickle-ized, density-sensitive state propagation
9Networking data set5-minute snapshots Top four links from each network node, as reported to the edgeLink and device churn are commonMean network degree is at least 16, diameter is about 4.5Analysis performed March-April 2011
April 17, 2012IPSN 2012: Beijing, China10
View the link topology, filtered through the lens of what the routing protocol did.10Network data yieldApril 17, 2012IPSN 2012: Beijing, China11
Most days exhibit 5th percentile data yield > 99%
Device dynamicsWhen do devices come on/off?Device reset detector based on LocalTime rolloverApril 17, 2012IPSN 2012: Beijing, China12
Also energy science implications for motes turning on and off
12Exploration is ongoing
April 17, 2012IPSN 2012: Beijing, China13Path length and router degree show diurnal and weekly variation
Methodology: construct directed graph based on reported topology at each slice. Examine degree, mean shortest path.13
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 linksall linksLt: reported link set at time t
Do we end up with a single set of stable links?
L = U(L_t)
14Lessons: networking and managementNo such thing as a static networkScriptability/automated management is keyData reliability is not all about the wireless part: Internet and practical considerations Back up or replicate your databaseLocal buffering at the end points, middle-boxes?Meters walk awaySometimes the whole building is turned offHorizontal scalability is a must
April 17, 2012IPSN 2012: Beijing, China15
You need to recover when everything is turned on at once.15What makes up building 90 plugs energy?16
Computers50% of energyDisplays10% of energyTask Lighting 7% Networking 6%Other 7%Imaging10% of energyMisc. HVAC10% of energyTimer controlled plug strips?75 MWh/year30% of non-computer plug total 6% of building totalComputer power management?150 MWh/year60% of computer total 12% of building total
April 17, 2012IPSN 2012: Beijing, China
75 MWh = $11k/year, Cost @ $25/unit = $10kComputer power management 150 MWh + 150 MWh from power management of other devicesWhy is other so high mid-day: 18 microwaves, 10 toasters, 40 coffee makers, 14 on demand hot water, 10 electric tea kettles
16ConclusionsToolkit for domain scientists needed802.15.4e, RPL-based networkingCommon hardware problemExploding the instrument is possible!Further standardization: CoAP90% solutionsOverall theme: careful simplicity
April 17, 2012IPSN 2012: Beijing, China17
What does a large, relatively successful deployment like this really tell us?Productization is still a challenge for academics: either need a company or we need to collaborate more on infrastructure
17QuestionsSpecial thanks to Sara Alspaugh, Alice Chang, Iris Cheung, Albert Goto, Xiaofan Jiang, Shelley Kim, Margarita Kloss, Judy Lai, and Ken LutzApril 17, 2012IPSN 2012: Beijing, China18
BACKUPSApril 17, 2012IPSN 2012: Beijing, China19
Network co-development and deployment April 17, 2012IPSN 2012: Beijing, China202005: Redwoods2007: RFC4944: 6LoWPAN2008: Full sensornet IP architecture proposed2008: BLIP/Contiki 6LoWPAN released2009: GreenOrbs (1000 Nodes)2010: Collection Tree Protocol2012: RFC6553: RPL, IEEE 802.15.4e2012: @scale
Source for 76M is PG&E
20How Common is Computer-Display Power Management?
2140 Hour Work WeekPM w/ breaksRarely power down monitor83% of monitors use power management15% use it with breaks for days at a time2% do not use it
April 17, 2012IPSN 2012: Beijing, China
19 of 115 or 21What is the Distribution of LCD Computer Display Energy Use?22
N=118April 17, 2012IPSN 2012: Beijing, China
How Common is Desktop Computer Power Management?
2340 Hour Work Week39% rarely powered down44% managedApril 17, 2012IPSN 2012: Beijing, China
Findings and Next Steps Bldg 90 network demonstrated large-scale, end-to-end WSN and collected a lot of useful dataIT equipment should be focus of office energy management programsUsing data for LBNL-wide plug-load managementInventory and meter installation are labor-intensiveExploring using public (homeowner & building occupant) participation for data collectionIntegrate metering & communications into productsRobust sensor network needs more engineering? Evaluate commercial products now availableElectricity only part of buildings energy problemDeveloping low-cost WSN for gas and water metering24April 17, 2012IPSN 2012: Beijing, China
IntroductionEnergy science goalsComputer science goalsRelated WorkDeploymentsStudy overviewSystem design & architectureResultsApril 17, 2012IPSN 2012: Beijing, China25
MELS: Miscellaneous Electric LoadsLarge, rigorous study of miscellaneous electric loads (mostly plugs)Roughly 1/3 of building energy consumptionDifficult to study due to large number of small consumers
April 17, 2012IPSN 2012: Beijing, China26
Methodology: Multipoint CalibrationApril 17, 2012IPSN 2012: Beijing, China27
Automated 20-point calibration on every meter 3-part piecewise calibration
90th percentile error is
