An Efficient Method to Deploy and Move Sensor Motes Yin Chen Andreas Terzis November 2 2011 What to do about the transitional region Place motes in the transitional region vs in the connected region ID: 617633
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On the Implications of the Log-normal Path Loss Model: An Efficient Method to Deploy and Move Sensor MotesYin Chen, Andreas TerzisNovember 2, 2011Slide2
What to do about the transitional region?
Place motes in the
transitional region
vs in the connected region
Transitional region
2
Connected regionSlide3
Our ProposalOccupy the transitional regionPerform random trials to construct links with high PRRBased on the Log-normal radio model
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Motivation: Placing Relay Nodes4Slide5
OutlineIntroduce log-normal path loss modelDiscuss pitfallsPresent the experimental results – reality check5Slide6
Log-normal Path Loss ModelReceived signal strength at a distance is , is a Gaussian random variableDue to artifacts in the environment (occlusions, multipath, etc.)Does not consider temporal variation
Power of the transmitted signal
Path loss at distance
Path loss exponent
Random variation
Sender
Receiver
distance
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Three Regions of Radio LinksAs the distance increases, we go through 3 regionsConnected: Transitional: Disconnected: ObservationThe packet reception ratio at any given location is random
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Connected RegionIn connected regionPRR is very likely to be highTrying one location will likely produce good linkSenderReceiver
5 meters
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Transitional RegionIn transitional regionPRR may or may not be highTrying a few spots should yield a good linkSenderReceiver
15 meters
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Disconnected RegionIn disconnected regionPRR is very unlikely to be highTrying multiple spots seems worthlessSenderReceiver
40 meters
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OutlineIntroduce log-normal path loss modelDiscuss pitfallsPresent the experimental results – reality check11Slide12
PitfallsLog-normal path loss model is not perfectThe Gaussian variation in signal strength is a statistical observationSignal strengths at nearby locations are correlated12Slide13
Reality CheckVerify log-normal path loss modelQuantify spatial correlationsCount number of trials to construct good linksInvestigate temporal variations13Slide14
Experimental Setup DevicesTelosB motesiRobot with an Ebox-3854 running LinuxEnvironmentsOutdoor parking lotLawnIndoor hallwayIndoor testbedTwo forests14Slide15
Evaluations on the Log-normal ModelHolds well in all the environmentsExample figure for the parking lotWe can subtract the solid line from the raw RSSI readingsThe residual RSSI values are samples of the random variable :
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Q-Q Plot of the Residual RSSI Values
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Reality CheckVerify log-normal path loss modelQuantify spatial correlationsCount number of trials to construct good linksInvestigate temporal variations17Slide18
Spatial CorrelationPRR measurements at a parking lotiRobot moves in a 2-d plane (the ground)Black cell : PRR below 85%; Gray cell : PRR above 85%PRR are correlatedTrying two adjacent locations flipping two coinsIn
all
of our experiments,
1 meter is sufficient to remove most correlation
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Reality CheckVerify log-normal path loss modelQuantify spatial correlationsCount number of trials to construct good linksInvestigate temporal variations19Slide20
Number of Trials - ConfigurationGrid samplingBernoulli trialsNumber of trials to find a good PRR is geometrically distributed
distance
1 meter
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Number of Trials - ResultsMeasure and compute the length of connected region Place motes at distances longer than
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Number of Trials – Fitting Geometric DistributionSuggests that 1 meter ensures independent trials.22Slide23
Connecting Two MotesMote AMote BRelay
TAR: number of trials to connect to A
TBR: number of trials to connect to B
TARB: number of trials to connect to both A and B
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TARB
TAR
TBR
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Reality CheckVerify log-normal path loss modelQuantify spatial correlationsCount number of trials to construct good linksInvestigate temporal variations24Slide25
Temporal VariationBox plots of residual RSSI values for two forests25Slide26
ConclusionLog-normal model fits sensornetsSignal correlation vanishes at 1 meter separationEasy to find good links in the transitional regionRule of thumb: at twice the length of connected region, number of trials is less than 5 with high probability26Slide27
Application – Placing Relay NodesNumber of relay nodes at large scalePlace 120 sensor motes in an area of size 800m by 800m Run Steiner Tree algorithm to place relay nodes27Slide28
Application – Mobile Sensor NetworksMobile sinkIf the current spot yields low PRR, move 1 meterMinimize travel distanceMobile motes Signal variation in the space domainSignal variation in the time domain28Slide29
Thank you!Questions?29