3/31/05Defense Science Board 1 Power Issues in Wireless Sensor Nets David Culler University of California, Berkeley culler

Power Issues in Wireless Sensor NetsDavid CullerUniversity of California, Berkeleyhttp://www.cs.berkeley.edu/~culler

Defense Science Board

OutlineBasic model of operationNode Design a for low power consumptionOperating System IssuesDesign of the power-supply subsystemMAC-level network design for powerHigher-level network design for powerApplication levelImportant areas of developmentDiscussion


Model of operationSleep Active [Wakeup / Work]Peak Power Essentially sum of subsystem componentsMW in supercomputer, kW in server, Watts in PDAmilliwatts in mote class deviceSleep powerMinimal running components + leakageMicrowatts in mote-classAverage powerPave = = (1-factive)*Psleep + factive*Pactive Pave = fsleep*Psleep + fwakeup*Pwakeup+ fwork*PworkLifetimeEnergyStore / (Pave - Pgen )ActiveActive


Passive VigilanceSense only when there is something useful to detectListen only when there is something useful to hearHow do you know?By arrangementBy cascade of lower power triggers


Mote Power Parameters1s Microwatts sleep10s of milliwatts active (wakeup or work)Wakeup substantialMilliseconds (1000s of instructions)1% Duty Cycle is commonWakeup matters


BatteriesStill the best energy storeIssuesVoltageSource currentLeakageVoltage profileRecharge


Design of a Low Power Node


Key Design ElementsEfficient wireless protocol primitivesFlexible sensor interfaceUltra-low power standbyVery Fast wakeupWatchdog and MonitoringData SRAM is critical limiting resource

proctimersWireless NetInterfaceWired NetInterfaceRFtransceiverantennaserial linkUSB,EN,Low-powerStandby & WakeupFlash Storagepgm imagesdata logsWD


Mote Platform Evolution3


802.15.4 PlatformsFocused on low power Sleep - Majority of the timeTelos: 2.4mAMicaZ: 30mAWakeupAs quickly as possible to process and return to sleepTelos: 290ns typical, 6ms maxMicaZ: 60ms max internal oscillator, 4ms externalProcessGet your work done and get back to sleepTelos: 4MHz 16-bitMicaZ: 8MHz 8-bitTI MSP430Ultra low power1.6mA sleep460mA active1.8V operation

Standards BasedIEEE 802.15.4, USBIEEE 802.15.4CC2420 radio250kbps2.4GHz ISM bandTinyOS supportNew suite of radio stacksPushing hardware abstractionMust conform to std linkEase of development and TestProgram over USBStd connector headerInteroperabilityTelos / MicaZ / ChipCon devUCB TelosXbow MicaZ


TinyOS-driven architecture3K RAM = 1.5 mm2CPU Core = 1mm2multithreadedRF COMM stack = .5mm2HW assists for SW stackPage mapping SmartDust RADIO = .25 mm2SmartDust ADC 1/64 mm2I/O PADS

Expected sleep: 1 uW 400+ years on AA150 uW per MHzRadio: .5mm2, -90dBm receive sensitivity1 mW power at 100KbpsADC: 20 pJ/sample 10 Ksamps/second = .2 uW.jhill mar 6, 2003


MicrocontrollersMemory starvedFar from Amdahl-Case 3M ruleFairly uniform active inst per nJFaster MCUs generally a bit betterImproving with feature sizeMin operating voltage1.8 volts => most of battery energy2.7 volts => lose half of battery energyStandby powerRecently a substantial improvementProbably due to design focusFundamentally SRAM leakageWake-up time is keyTrade sleep power for wake-up timeMemory restoreDMA Support: permits ADC sampling while processor is sleeping


RadioTrade-offs: resilience / performance => slow wake upWakeup vs interface levelAbility to optimize vs dedicated support


Flash TechnologyOne write per bit per erase cycleFlash characteristics:Not used in current motes


Power States at Node LevelActiveActive


Tiny OS ConceptsScheduler + Graph of Componentsconstrained two-level scheduling model: threads + eventsComponent:Commands, Event HandlersFrame (storage)Tasks (concurrency) Constrained Storage Modelframe per component, shared stack, no heapVery lean multithreadingEfficient Layering

structured event-driven executionNever wait or spinMessaging ComponentinitPower(mode)TX_packet(buf)TX_packet_done (success)RX_packet_done (buffer)Internal Stateinitpower(mode)send_msg(addr, type, data)internal threadCommandsEvents


Cooperative InterfacesPower management extends std control1000-fold range of power drawComponents informed of intention to go to sleepTake internal actionsPropagate controlScoreboard determined permissible depth of sleep stateScheduler drops to sleep on idleKey interrupts drive wake-upRich communication interfacesSignal strengthPost-MAC time-stampingSub-carrier signaling


Power-supply SubsystemEnergy StorePower SourceConsumerManagement & Control


Importance or primary bufferNode is able to operate from capacitorsModerate period of time (~week)Source active load (mAs !)Absorb energy inputPerform frequent charge cycles (daily)shallowSource high voltage recharge of secondaryPower MCU during secondary recharge


Its all about leakageBigger isnt betterMore doesnt helpWe use two 22 F in seriesoperate in the flatunder load (1%, 10 mA)


RechargingHigh density & Low leakageSoftware on MCU manages recharge sequenceInclude temperature compensationPulsing charge current (~1x battery capacity) to 80%High level charge management and load control


OutlineBasic model of operationNode Design a for low-power consumptionOperating System IssuesDesign of the power-supply subsystemCommunication BasicsMAC-level network design for powerHigher-level network design for powerImportant areas of developmentDiscussion


BasicsPower required to transmit a given distance grows like the r3 in free space with omnidirectional antennaCan be as bad as r7 close to the groundSlower growth rate with directional, but Power required to route data hop-by-hop a given distance grows only linearlyConnectivity determined by a host of factorsSNRTransmission power, receiver sensitivity, distanceInterferenceobstructions


The Basic PrimitiveTransmit a packetReceived by a set of nodesDynamically determinedDepends on physical environment at the timeWhat other communication is on-goingEach selects whether to retransmitPotentially after modificationAnd if so, when


Routing MechanismUpon each transmission, one of the recipients retransmitdetermined by source, by receiver, by on the edge of the cell


Communication and PowerCosts power whenever radio is onTransmitting, receiving, or just listeningTransmit is easy, Rcv is whats trickyWant to turn it on just when there is something to hearTwo approachesSchedule transmission intervalsStatically, dynamically, globally, locallyMake listening cheapofflistenoffRXTX


TDMA variantsTime Division Media AccessEach node has a schedule of awake timesTypically used in star around coordinatorBluetooth, ZIGBEECoordinator hands out slotsFar more difficult with multihop (mesh) networksFurther complicated by network dynamicsNoise, overhearing, interference


Complexity of ConnectivityDirect Reception

Non-isotropicLarge variation in affinityAsymmetric linksLong, stable high quality linksShort bad onesVaries with traffic loadCollisionsDistant nodes raise noise floorReduce SNR for nearer onesMany poor neighborsGood ones mostly near, some far


S-MACYe, Heidemann, and Estrin, INFOCOM 2002Carrier Sense Media AccessSynchronized protocol with periodic listen periodsIntegrates higher layer functionality into link protocolHard to maintain set of schedulesT-MAC [van Dam and Langendoen, Sensys 2003]Reduces power consumption by returning to sleep if no traffic is detected at the beginning of a listen periodNode 1Node 2sleeplistenlistensleep


Low Power Listening (LPL)Energy Cost = RX + TX + ListenScheduling tries to reduce listeningAlnternative, reduce listen costExample of a typical low level protocol mechanismPeriodically wake up, sample channel, sleepPropertiesWakeup time fixedCheck Time between wakeups variablePreamble length matches wakeup intervalRobust to variationComplementary to schedulingOverhear all data packets in cellDuty cycle depends on number of neighbors and cell trafficRXTXsleepsleepsleepsleepsleepsleepNode 2Node 1timetime


Communication Trade-offsConnectivity graph is not staticComplicates explicit schedulingTime Synchronization Time of reference required for rendezvousLow-power listening (preamble sampling)Reduce the cost to listenAllows coarser time synch and more flexible schedules


The Common Case: Data GatheringCollection of nodes take periodic samplesStream data towards a root node

Root announces interestdepth = 0Nodes listenWhen hear neighbor with smaller depthstart transmitting data to best lower neighborset own depth to one greater (and include with data)Data transmission continuously reinforces & adjusts routesAggregation within nodes or within the tree


Radio Cells


Continuous Network Discovery0


Local Operations => Global BehaviorNodes continually sense network environmentuncertain, partial informationPackets directed to a parent neighborall other neighbors hear toocarry additional organizational informationEach nodes builds estimate of neighborhoodadjusted with every packet and with timeInteractively selects parent# trans := 1/ParentRate + #trans(Parent->root)Routes traffic upwardCollectively they build and maintain a stable spanning treetakes energy to maintain structure

node #depthchild?parent?% linkgoodness171yes90.763yes75.6...


Power-aware RoutingCost-based RoutingMinimize number of hopsMinimize loss rate along the pathPerform local retransmissions, minimize number along pathEnergy balanceUtilize nodes with larger energy resourcesUtilize redundancyNodes near the sink route more traffic, hence use more energyGive them bigger batteries or provide more of them and spread the loadRandomize routesUtilize heterogeneityRoute through nodes with abundant power sources


Communication SchedulingTDMA-like scheduling of listening slotsNode allocates listen slots for each child Transmission slots to parentHailing slot to hear joinsTo join listen for full cyclePick parent and announce selfGet transmission slotCSMA to manage mediaAllows slot sharingLittle contentionReduces loss & overhearingConnectivity changes cause mgmt traffic


In-network ProcessingBest way to reduce communication cost is to not communicateCompute at the sensorOnly communicate important eventsCompute over localized regions of the networkDistributed detectionValidation


Exceptional Event DetectionChallenge: Detecting Exceptional Events Rare most time spent monitoring noiseRandom nearly continuous samplingEphemeral low bound of duty-cycling off timeApproach: Passive VigilanceMulti-modal, low-power sensors Duty-cycled, where possible and arranged inEnergy-Quality hierarchy with low (E, Q) sensorsTriggering higher (E, Q) sensors, and so onHow to Estimate Energy Consumption?Power = idle power + energy/event x events/timeEstimate event rate probabilistically: p(tx) =

from ROC curve and decision threshold for H0 & H1How to Optimize Energy-Quality?Let x* = (x1*, x2*,..., xn*) be the n decision boundaries between H0 & H1. for n processes. Then, given a set of ROC curves, optimizing for energy-quality is a matter of minimizing the function f(x*) = E[power(x*)] subject to the power, probability of detection, and probability of false alarm constraints of the system.

FalseAlarmRateEnergyUsage

HighLowLowHighTrigger network includes hardware wakeup, passive infrared, microphone, magnetic, fusion, and radio, arranged hierarchicallyNodes: sensing, computing, and comm processesEdges:

Energy-Quality Hierarchysub. SPOTS 05


Example: Detection & Tracking


Key Areas to ImproveLow leakage SRAM1T sramLow leakage supercapsCommunication acceleratorsRadio wake-up cascadeVery low power detection of signal triggers receiverRobust communication protocolsSensor detection cascades


Where to read for moreTelos: Enabling Ultra-Low Power Wireless Research, Joseph Polastre, Robert Szewczyk, David Culler To appear in The Fourth International Conference on Information Processing in Sensor Networks: Special track on Platform Tools and Design Methods for Network Embedded Sensors (IPSN/SPOTS), April 25-27, 2005Perpetual Environmentally Powered Sensor Networks, Xiaofan Jiang, Joseph Polastre, David Culler To appear in The Fourth International Conference on Information Processing in Sensor Networks: Special track on Platform Tools and Design Methods for Network Embedded Sensors (IPSN/SPOTS), April 25-27, 2005 Versatile Low Power Media Access for Wireless Sensor Networks, Joe Polastre and David Culler, The Second ACM Conference on Embedded Networked Sensor Systems, Nov. 2004"Design of a Wireless Sensor Network Platform for Detecting Rare, Random, and Ephemeral Events", Prabal Dutta, Mike Grimmer, Anish Arora, Steve Bibyk, and David Culler, In The Fourth International Conference on Information Processing in Sensor Networks (IPSN'05), 2005.


Discussion


Issues of whats hidden from youDont reveal factors, well take care of itWell adjust all underlying things so you dont have toEverything in the black box.

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3/31/05Defense Science Board 1 Power Issues in Wireless Sensor Nets David Culler University of California, Berkeley culler