Application-Aware Techniques for Energy Efficient Data Collection in Wireless Sensor Networks

Towards a True Energetically Sustainable WSN: A Case Study with Prediction-Based Data Collection and a Wake-up ReceiverAlessandro Bogliolo, Valerio Freschi, Emanuele Lattanzi, Amy L. Murphy and Usman Raza

1Energetic Sustainable Power consumption Model. Title and Simulator evaluation details are missing altogether

Gps - wakeup radio prototypes - motivate case study with that

No details of 380 and wakeup receiver !!!!!JUSTIFY!

Define baseline

1Lamp levels typically statically determined, ignoring environmentalOverprovisioned to meet the regulationsProblems: waste energy and potential security hazardIdea: place wireless sensors along tunnel, adjust lamps to actual conditionsEliminate overprovisioning, account for environmental variations2A Motivating Case Study:Adaptive Lighting with WSNs

stopdistance

Put it

How it is typically doneObjective: day time, It is not to dark .. Night not too bright driver Statically over provisions the lightOver provisionsPonte alto and its statistics

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2-lane carriagewayTunnel length of 260 m, 40 battery powered WSN nodesFull, operational system described in IPSN114Goal: Using Renewable Energy for Achieving a Long Term Operation

Currently, nodes are powered with disposable batteries Problem:Short lifetime Replacement is expensive, labour intensive and a safety hazardGoal: long term operation with rechargeable batteries and energy harvesters

LifetimeshortnenWanna get rid of batteriesBatteries are expensive

45Goal: Using Renewable Energy for Achieving a Long Term Operation

Currently, nodes are powered with disposable batteries Problem:Short lifetime Replacement is expensive, labour intensive and a safety hazardGoal: long term operation with rechargeable batteries and energy harvesters

LifetimeHarvestable energy is two orders of magnitude less than the power consumptionPut text5HarvesterVirtualSense6

Approach: A Software Hardware Co-design for Minimizing Energy ConsumptionPrediction Based Data CollectionDynamic Power ManagementWakeup ReceiverPhotovoltaic123SoftwareHardwareIt also defines the layoutAdd Sotware hardware labels Add the numbers6

Power consumption modelFunctional state diagramEmpirical hardware measurements7Evaluation MethodologyModel described in ENSSys13

Network traffic Actual data from the tunnel 47 days, 1 sample every 30s, 5.4 million measurementsMultiple data collection treesData collection trees bias unfairly Forwarding +

Particular wakeup reTree

7time1: Prediction Based Data CollectionTypical WSN System

Sink gathers all sensor readings of the WSN.Advantage: precisePrediction Based Data Collection/ WSNs

Sink predicts sensor readings of the WSN.Advantage: less traffic8HarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic1Exactly same data ------------------ A lot of DATAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAactualless

Sink GATHERSSink PREDICTS8Derivative Based Prediction (DBP)A linear model: Easy to computeExcellent data approximation

99% reduction in data trafficsaves radio communication cost9Sensor valueTimeDBP is described in PerCom121: Prediction Based Data CollectionHarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic1DBP ModelCongestionOverhearing Communication costPBDC DPM Energy saving Wakeup receiver: periodic receive checks, transmission times & receptions times910Lifetime ImprovementNo.Dynamic Power ManagementWakeup ReceiverLifetime ImprovementMCURadioPeriodicDBP1StandbyLPM1No1x1.7xStandard hardware + NO software Optimization = BaselineDBP almost doubles the lifetimeStandard HardwareHarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic1What this table is????

Software hw

10112: VirtualSense

Ultra low power platformIdeal for energy harvesting WSNs

Features Dynamic power management Novel wakeup receiver

HarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic2VirtualSense NodeFocus on next Treat it as outline11Microcontroller: TI MSP430f54xxTurn off components between idle periods(infrequent transmissions of DBP models) Power consumption varies from 0.66nW and 10mW122.1: Dynamic Power Management (DPM)

Radio: CC2520 RF Transceiver Deep sleep mode (LPM2)Infrequent transmissions of DBP modelsCurrent draw (~0.1 uA) in receive modeFrame FilteringAllows discarding unintended packets

HarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic2Hints to case study ??FF: unicast unintendedpackets

1213Lifetime ImprovementNo.Dynamic Power ManagementWakeup ReceiverLifetime ImprovementMCURadioPeriodicDBP1StandbyLPM1No1x1.7xStandard HardwareHarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic11314Lifetime ImprovementNo.Dynamic Power ManagementWakeup ReceiverLifetime ImprovementMCURadioPeriodicDBP1StandbyLPM1No1x1.7x2StandbyLPM2No1.7x7.8x3StandbyLPM2+FFNo2x7.8x4SleepLPM2No1.7x7.9x5SleepLPM2+FFNo2.0x7.9xImprovement not two orders of magnitude: Not energetically sustainable !!!Multiple DPM configurationsHarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic2Not energeticallyDid a lot got littleRadical

14Uses ultra sound technologyOut of band triggeringturns ON expensive data transceiver only for data receptions.Ultra-low energy consumptionRx: 820nA vs. 18.5mA for primary data radioRange 14m

2.2: Wakeup Receiver

HarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic2Ultrasound Wakeup Receiver Could not find comparable numbers1516TxRxSenderReceiver2.2: Wakeup ReceiverHarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic2WithoutEnergy Efficiency: No receive checks and shorter TxDominant receive checksShorter RxSenderReceiverTriggerTxWithWakeup receiver ONAdd AnimationBorder Too much blueFix it visually

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1617Lifetime ImprovementNo.Dynamic Power ManagementWakeup ReceiverLifetime ImprovementMCURadioPeriodicDBP1StandbyLPM1No1x1.7x2StandbyLPM2No1.7x7.8x3StandbyLPM2+FFNo2x7.8x4SleepLPM2No1.7x7.9x5SleepLPM2+FFNo2.0x7.9xHarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic218Lifetime ImprovementNo.Dynamic Power ManagementWakeup ReceiverLifetime ImprovementMCURadioPeriodicDBP1StandbyLPM1No1x1.7x2StandbyLPM2No1.7x7.8x3StandbyLPM2+FFNo2x7.8x4SleepLPM2No1.7x7.9x5SleepLPM2+FFNo2.0x7.9x6SleepLPM2Yes2.6x+ Wakeup ReceiverModest improvement- huge trafficHarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic2A lot of traffic tx + fws 2.6 eliminating benefit

1819Lifetime ImprovementNo.Dynamic Power ManagementWakeup ReceiverLifetime ImprovementMCURadioPeriodicDBP1StandbyLPM1No1x1.7x2StandbyLPM2No1.7x7.8x3StandbyLPM2+FFNo2x7.8x4SleepLPM2No1.7x7.9x5SleepLPM2+FFNo2.0x7.9x6SleepLPM2Yes2.6x380x+ Wakeup ReceiverTwo order of magnitude improvement with DBP + wakeup reeciverHarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic220Harvested3: Harvester Energetic Sustainability?HarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic321

Harvested

HarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic33: Harvester Energetic Sustainability?HarvestedHardware22Not energetically sustainable 3: Harvester Energetic Sustainability?HarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic32223HarvestedHardwareHardware+SoftwareEnergetically sustainable even for nodes with least harvestable energy3: Harvester Energetic Sustainability?HarvesterVirtualSenseSoftwareDPMWURxPhotovoltaic324ConclusionHarvesterVirtualSensePrediction Based Data CollectionDynamic Power ManagementWakeup ReceiverPhotovoltaic

LifetimeIt also defines the layoutAdd Sotware hardware labels Add the numbers24This is only the beginningShort range of wakeup receiver: dense deploymentDirectional wakeup receiver: fixed tree/ robustness?Analytical model is promising, real node evaluation is needed25ConclusionEven though it is a case study, results are potentially wideDBP is generally applicable to WSNsTunnel = data collection, common in most WSNs VirtualSense hardware is modular: expandable Not to forget, we got excellent results!380 x improvement lifetime25Thank youraza@fbk.eu 2627Data reduction with DBP