Radio Tomography: Environmental Inference from Wireless...
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Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Radio Tomography: Environmental Inferencefrom Wireless Network Signal Strength
Neal Patwari
Utah IEEE SP/COM Chapter Seminar
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Outline
1 Introduction
2 Shadowing-based RTI
3 Variance-based RTI
4 Bayesian DFL
5 Breathing Inference
6 Conclusion
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Outline
1 Introduction
2 Shadowing-based RTI
3 Variance-based RTI
4 Bayesian DFL
5 Breathing Inference
6 Conclusion
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Device-free localization (DFL) Applications
RFID identifies, locates people’s tagsHow about people, objects not tagged?Apps: emergency response, smart homes,context-aware computing
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
DFL: Technologies
Video cameras. Don’t work in dark, through smoke orwalls. Privacy concerns.Thermal imagers. Limited by walls. High cost.IR Motion detectors. Limited by walls. High false alarms.Ultra wideband (UWB) radar. High cost.Received signal strength (RSS) in a wireless network
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
RSS-DFL: Measure many spatially distinct links
5 7
18 19 20 2114 15 16 17
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Mesh network of N transceivers→ O(N2) RSS
measurementsLink RSS changes due to people in environment near linkOne person / object affects multiple links
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
DFL Ideas From Many Sources
IPSN 2007 Extreme Sensing Competition: won by RSSHero lab at U.M.: Motion detection w/ RSS vectorsU. Utah link shadowing correlation study
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Channel Modeling
Generic model for received power:
Pa = P̄(da)− Xa
Pa: measured received power on link a: at node ratransmitted by node ta (dBm),P̄(da): model for large-scale fading: Ensemble mean dBmreceived power at distance da.Xa: shadowing, small-scale fading loss, measurement error
Question: Are {Xa}a independent?
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Deployments for Channel Modeling
Fifteen indoor and six outdoor measurement campaignsResults: close links have correlated Xa
1
1P. Agrawal and N. Patwari, “Correlated Link Shadow Fading in Multi-hop Wireless Networks,” IEEE
Trans. Wireless Commun., August 2009.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Experimental Correlation Results
Link Geometry vs. Correlation Coefficient (Observed)
Geom- Meas- Geom- Meas-etry ured ρ etry ured ρ
1 0.33*** 4 0.14***2 0.21*** 5 0.21***3 0.05 6 0.23***
*** p < 0.005
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
What mechanism explains shadowing correlation?
xi
xj
xk
xl
link a link b
shadowing field ( )p xSpatially correlated shadowing field p(x)
Assume Xi , for i ∈ {a,b} are integrals ofp(x)
Xi =1
‖xti − xri‖1/2
∫ xri
xti
p(y)dy. (1)
Mutual dependence on p(x)→correlation of Xa, Xb
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Experimental Correlation Results
Link Geometry vs. Correlation Coefficient (Observed, Model)
Correlation ρ Correlation ρGeom- Meas- Prop. Geom- Meas- Prop.
etry ured Model etry ured Model1 0.33*** 0.31 4 0.14*** 0.152 0.21*** 0.25 5 0.21*** 0.223 0.05 0.06 6 0.23*** 0.26
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Impact of correlation on sensor networks
s
1 2
d
43
Multiple routes are not independent!Route diversity: links fade simultaneously more oftenLocalization: RSS errors don’t “average out” after manylinks. But correlation = spatial information.Correlation implies spatial field p(y) can be estimated2
2N. Patwari, P. Agrawal, “Effects of Correlated Shadowing: Connectivity, Localization, and RF Tomography,”
IPSN 2008, April, 2008.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Outline
1 Introduction
2 Shadowing-based RTI
3 Variance-based RTI
4 Bayesian DFL
5 Breathing Inference
6 Conclusion
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Loss is Linear with Dynamic Object Shadowing Field
Two shadowing fields: 1) Static, 2) DynamicLet p(y) be the dynamic dB shadowing loss fieldLet Xa be the “dynamic” shadowing loss (change from“empty” condition)
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Discrete-space Loss Field Model
Consider simultaneously all M pair-wise links:
x = Wp + n
x = [X1, . . .XM ]T = measured losses (dB) vs. “empty”p = [p1, . . .pN ]T = discretized loss field (dB/voxel)W = [[wi,j ]]i,j = weights; n = noise
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Shadowing Field Estimation Problems
Measure x, assume known W . Estimate p.Ill-posed! Pixels� links, other issuesLow SNR: RSS varies without human motion in area.Linear model isn’t true physics; best W is unknown.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Initial Spatial Model
What is loss in link l vs. person’s position?No validated spatial model exists for WOur initial model: Pixels k in ellipse (w/ foci at TX and RX)have Wl,k = 1, zero o.w.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Real-time Approaches to Image Estimation
Real-time requirement: look for linear algorithm
p̂ = Πx
Projection Π needs only be calculated onceComplexity: Order of # Links × # pixels
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Regularized Image Estimation Algorithms
1 Tikanov Regularized inverse: minimize penalized squarederror 3
f (p) = ‖Wp− x‖2 + α‖Qp‖2
when Q is the derivative:
ΠTik =[W T W + α(DT
X DX + DTY DY )
]−1W T
2 Assume correlated p and use regularized least squares.
ΠRLS =(
W T W + αC−1p
)−1W T
3J. Wilson and N. Patwari, “Radio Tomographic Imaging with Wireless Networks”, IEEE Transactions on Mobile
Computing, May 2010.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Real-Time Implementation: Testbed
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Crossbow Telosb, 2.4 GHz, IEEE 802.15.4SPIN: Token passing MAC; when onetransmits, others measure RSSOpen source:http://span.ece.utah.edu/spin
Packet data: latest measured RSS valuesLaptop-connected mote overhears all trafficComplete meas’t of p 3-4 times/sec (28 nodes)
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Video
Video clip: Atrium of Warnock Engineering Building
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Alternate Algorithms: TV
Use total variation (TV) norm to pull out sparsity of image 4
4J. Wilson and N. Patwari, “Regularization Methods for Radio Tomographic Imaging,” in Proc. 2009 Virginia
Tech Wireless Symposium.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Outline
1 Introduction
2 Shadowing-based RTI
3 Variance-based RTI
4 Bayesian DFL
5 Breathing Inference
6 Conclusion
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Through-wall Deployment Tests
Tested system with 34 nodes, outside of external walls ofarea of house 5
0 5 10 15 20 25 30
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Door
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x coordinate (feet)
yco
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(feet)
Nodes
Walls
5J. Wilson and N. Patwari, “See Through Walls: Motion Tracking Using Variance-Based Radio Tomography
Networks”, IEEE Transactions on Mobile Computing, (accepted).
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Problem: Low SNR
y c
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(fee
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x coordinate (feet)0 5 10 15 20 25 30
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RTI does not indicate actual image human location (X)
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Problem: What Happened?
100 200 300 400 500 600 700−90
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Vacant network area
100 200 300 400 500 600 700−90
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Moving human obstructing link
Time (samples)
Link (27,0) to (15.45,26.4)
Link (6,0) to (20,26.4)
Moving people affectRSS, but change is upand downE.g.: Blocking personincreases RSS (- - - -)E.g.: Moving personincreases RSS variance(both links)
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Demo: Person-induced Temporal Fading
Run live demo of RSS measurements with two nodes.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Idea: Use Variance to Image Motion
Model: Assume variance is linear combination of motionoccurring in each pixel:
s = Wm + n
s = [s1, . . . sM ]T = windowed sample variancem = [m1, . . .mN ]T = motion ∈ [0,1]
W = [[wi,j ]]i,j = variance added to link i caused by motionin voxel j
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Variance-based Radio Tomographic Imaging
x coordinate (feet)
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Apply regularized inversion to estimate m.VRTI image indicates actual image human location (X)
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
VRTI Video
Advice: Use YouTube (>150k hits for two videos)
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
VRTI-based Tracking
1 Spot motion test: avg. error = 0.45 m2 Track image max w/ Kalman filter: avg. error = 0.63 m
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Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Problem: Noise from Intrinsic Motion
Intrinsic motion: e.g.fans, moving machines, wind.
Figure: Identical experiments show very different VRTIperformance on a (Left) still vs. (Right) windy day.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Intrinsic Noise Solution: SubVRT
SubVRT, subspace decomposition for VRTI6
In windy experiment, location error reduced by > 40%.
6Y. Zhao, N. Patwari, “Noise reduction for variance-based device-free localization and tracking”, SECON 2011.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Need: Spatial Model for Variance
Where does motion have highest impact on RSS variance?
1 Near TX, RX [Yao et. al. 2008]2 At midpoint between TX, RX [Zhang et. al. 2007]3 Our work: In (narrow) ellipse w/ TX & RX as foci4 Pixels which intersect link line [Kanso and Rabbat 2009]
Need for measurements, analytical models
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Variance Measurement
Measurement at Bookstore, nodes on shelvesNormalize link, person position s.t. xr = (-1, 0), xt = (1,0)Find average variance by human position w.r.t. RX, TX
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Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Analytic Model: Intro
Do simple standard multipathassumptions explain data? a
Human = tall cylinder diameter D[Ghaddar et. al. 2004, Huanget. al. 2006]Scatterers/Reflectors in a plane.TX, RX, in plane ∆z above.Propagation via single bounce
aN. Patwari and J. Wilson, “Spatial Models for Human Motion-Induced Signal Strength Variance on Static Links",
IEEE Trans. Info. Forensics & Security, Sept. 2011.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Analytic Model: Details
Locations: TX xt , RX xr , bounce at xPropagation mechanism (a) scattering or (b) reflection
(a): Ps(x) =cs
‖xt − x‖2‖xr − x‖2
(b): Pr (x) =cr
(‖xt − x‖+ ‖xr − x‖)np
cr , cs,np ∈ R+ are propagation parameters [Nørklit &Andersen 1998, Liberti and Rappaport 1996]Variance prop. to expected total affected power (ETAP)
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Analytic Model: Results
Variance ∝ spatial functions:
(a) −2 −1 0 1 2−2
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Ours & [Yao 2008]: similar to reflection ETAP, low ∆zThose of [Zhang 2007]: high ∆z, either modality
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Outline
1 Introduction
2 Shadowing-based RTI
3 Variance-based RTI
4 Bayesian DFL
5 Breathing Inference
6 Conclusion
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Problems: Tracking from RSS
Tracking motion from image estimate is ad hocImage estimate may be very poor when DFL is possibleVRTI only tracks people in motion, not stationary peopleChange in mean and variance only two aspects of a r.v.What would Bayes do? NEED: distribution parameterizedby peoples’ locations
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
A Tale of Two Links
30 40 50 60 70 80Time index
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40RS
S (d
Bm)
Link 1Link 2
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Outcome: Fade-Level based Model
Link distributions are different, based on fade level: 7
If a link is in deep fade: RSS and variance increase whenobstructedIf a link is in anti-fade: RSS decreases when obstructed
7J. Wilson and N. Patwari, "A Fade Level Skew-Laplace Signal Strength Model for Device-Free Localization With
Wireless Networks", IEEE Trans. Mobile Computing, appeared online 12 May 2011.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Fade-Level Model Justification
Pi,j is power in phasor sum of multipath.(a) When sum is in a null, change tends to “pull it out”(b) When in constructive sum, change will “pull it down”
(a)
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Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Fade-Level based Model
Fade level = RSS model - meas’t:
F = P̄(di,j)− Pi,j
Determined in calibration (known sensor locations)Skew-Laplace pdf parameters: linear function of F
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Particle Filter
Need: track when meas’ts are non-Gaussian, non-linearParticle filtering: Bayesian coordinate est. given meas’tsConvergence as links more / less likely using (a) 15% ofmeas’ts, (b) 30% of meas’ts.
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Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Tracking Results
0 20 40 60 80 100 1200.00.51.01.52.02.53.03.54.04.5
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Person walks in rectangularpathEstimate avg. error: 0.58 mThrough-home: 0.90 mNeeds: proposal methods,human dynamics models
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Two-Person Tracking Results
2 0 2 4 6 8 10 12X Coordinate (m)
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Two people walk in rectanglesEstimate avg. error: 0.84 mThrough-home: 1.10 m
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Outline
1 Introduction
2 Shadowing-based RTI
3 Variance-based RTI
4 Bayesian DFL
5 Breathing Inference
6 Conclusion
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Observation-Led Motivation
Some links, sometimes, change RSS due to inhalation,exhalationOnly a small percentageCan we enable reliable breathing monitoringValue: Contact free, through walls
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Applications
Baby / patient breathing monitorElder careDetect breathing in rubbleLimitation: Measure change in chest, not respiration
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Experiments: Medical Appl
Clinical room, Anesthesiology, SOMSubject: breathing w/ metronomeConnected to end-tidal CO2 monitorOur system: 20 transceivers bedsideEach link 4 measurements / sec
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Algorithms
Filter out DC using IIR filterApprox. MLE:
1 Sum links’ squared DTFT2 If too low, no breathing (!)3 If not, max is at breathing rate
Linear: Low complexity, can be real time
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Results
Average spectrum plot 8
30 seconds of dataEstimated rate: 15.18bpmActual rate: 15.00 bpm
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8N. Patwari, J. Wilson, S. Ananthanarayanan P.R., S. K. Kasera, D. Westenskow, “Monitoring Breathing via
Signal Strength in Wireless Networks”, arXiv:1109.3898v1.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Results
Need to use link data simultaneouslyBPM within 0.2-0.4 of actualWith 25-30s data, perfect detectionBetter than capnometerTransceiver height didn’t matterDirectional antennas keep focus on bed
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Results
Links that detect breathingtend to be:
those that cross throughthe chestthose in a deep fade
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Y C
oord
inate
(m
)
��������A�
B�CDEA
6 F�ADEC��EEC�����E�
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Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Multi-channel Results
RSS on five different channels: Low RSS links exhibitbreathing9
135 140 145 150 155 160−85
−80
−75
−70
−65
−60
−55
−50
−45
Time (sec)
RS
S (
dB
m)
Ch 11 Ch 15 Ch 18 Ch 20 Ch 26
0 2 4 6 8
0
1
2
3
4
5
6
7
8
1
2
3
4
5
6 7 8 9 10 11
12
13
14
15
1617181920
X Coordinate (m)Y
Co
ord
ina
te (
m)
������
���
ABCC
9O. Kaltiokallio, manuscript in preparation, 2012.
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Outline
1 Introduction
2 Shadowing-based RTI
3 Variance-based RTI
4 Bayesian DFL
5 Breathing Inference
6 Conclusion
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Recap: Fading is a Environmental Signal
The wireless network is the sensorObjects in environment change the RSSModels of RSS changes→ algorithmsRSS device free location (DFL), “Radio Tomography”Real-time imaging, localization, breathing monitoring
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Commercialization
Security sensor: Tomographic Motion Detection (TMD)Big need: warehouse security systemsHidden, low false alarm rate, can’t “get around” it
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Current Work: Large-scale Reliable Systems
Deploy across 15,000 sq. feet in buildingDFL in low-link densityMulti-channel DFLBetter Bayesian solutions
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Acknowledgements
Dr. Joey WilsonDr. Piyush AgrawalYang ZhaoDr. Maurizio BoccaOssi KaltiokallioUniversity of Utah SPAN Lab
Career award ECCS-0748206, CPS award 1035565
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength
Introduction Shadowing-based RTI Variance-based RTI Bayesian DFL Breathing Inference Conclusion
Questions and Comments
More info on http://span.ece.utah.edu/
Neal Patwari UofU / ECE / SPAN
Radio Tomography: Environmental Inference from Wireless Network Signal Strength