near-optimal sensor placements: maximizing information while minimizing communication cost
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Near-optimal Sensor Placements: Maximizing Information while Minimizing Communication Cost. Andreas Krause, Carlos Guestrin, Anupam Gupta, Jon Kleinberg. Monitoring of spatial phenomena. Building automation (Lighting, heat control) Weather, traffic prediction, drinking water quality... - PowerPoint PPT PresentationTRANSCRIPT
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Near-optimal Sensor Placements:Maximizing Information while
Minimizing Communication Cost
Andreas Krause, Carlos Guestrin, Anupam Gupta, Jon Kleinberg
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Monitoring of spatial phenomena
Building automation (Lighting, heat control) Weather, traffic prediction, drinking water
quality...
Fundamental problem:Where should we place the sensors?
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Temperature data from sensor network
Light datafrom sensor network
Precipitation data from Pacific NW
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Trade-off: Information vs. communication cost
efficient communication!
extra node
extra node
The “closer” the sensors: The “farther” the sensors:
worse information quality! better information quality!
worse communication!
We want to optimally trade-off information quality and communication cost!
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Predicting spatial phenomena from sensors
Can only measure where we have sensors Multiple sensors can be used to predict
phenomenon at uninstrumented locations A regression problem: Predict phenomenon
based on location
Temphere?
X1=21 C X3=26 C
X2=22 C
23 C
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Predicted temperature throughout the space
x y
Tem
p.
(C)
Regression models for spatial phenomena
Real deploymentof temperature
sensorsmeasurements from 52 sensors(black dots)
x
ymany sensors around !
trust estimate here
few sensors around ! don’t trust estimate
Good sensor placements:Trust estimate everywhere!
Data collected at Intel Research Berkeley
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Probabilistic models for spatial phenomena
x
ysensor
locationsx y
Tem
p. (C
)
regressionmodel
yx
vari
an
ce
estimate uncertainty in predictionmany sensors around !
trust estimate here few sensors around ! don’t trust estimate
Modeling uncertainty is fundamental! We use a rich probabilistic model
Gaussian process, a non-parametric model [O'Hagan ’78]
Learned from pilot data or expert knowledge Learning model is well-understood
! focus talk on optimizing sensor locations
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Pick locations A with highest information quality lowest “uncertainty” after placing sensors measured in terms of entropy of the posterior
distribution
x y
sensor placement A (a set of locations)
uncertainty in prediction
after placing sensors
yx
un
cert
ain
ty
information quality I(A)(a number)
placement A
I(A) = 10
placement B
I(B) = 4
Information quality
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The placement problem Let V be finite set of locations to choose from For subset of locations A µ V, let
I(A) be information quality and C(A) be communication cost of placement A
Want to optimizemin C(A) subject to I(A) ¸ Q
Q>0 is information quota
How do we measure communication cost?
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Communication Cost Message loss requires retransmission This depletes the sensor’s battery quickly Communication cost for two sensors means
expected number of transmissions (ETX) Communication cost for placement is sum of all
ETXs along routing tree
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ETX 1.2
ETX 1.4
ETX 2.1 ETX 1.6
ETX 1.9
Total cost = 8.2
Many other criteria possible in our approach (e.g. number of sensors, path length of a robot, …)
Modeling and predicting link quality hard! We use probabilistic models
(Gaussian Processes for classification)! Come to our demo on Thursday!
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We propose:The pSPIEL Algorithm
pSPIEL: Efficient, randomized algorithm(padded Sensor Placements at Informative and cost-Effective Locations)
In expectation, both information quality and communication cost are close to optimum
Built system using real sensor nodes for sensor placement using pSPIEL
Evaluated on real-world placement problems
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Minimizing communication cost while maximizing information quality
V – set of possible locationsFor each pair, cost is ETXSelect placement A µ V, such that:
tree connecting A is cheapest
minA C(A)
C(A)=
locations are informative: I(A) ¸ Q
I(A) = I(
ETX =
3
ETX
= 1
0
ETX
= 1
.3
ETX12
ETX34
1.3
[…)
+ + + …
[
A1
A4
A8
[
First: simplified case, where each sensor provides independent information: I(A) = I(A1) + I(A2) + I(A3) + I(A4) + …
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Quota Minimum Steiner Tree (Q-MST) Problem
Problem:Each node Ai has a reward I (Ai)
Find the cheapest tree that collects at least Q reward:
but very well studied [Blum, Garg, …]
NP-hard…
Constant factor 2 approximation algorithm available!
=10
=12
=12=8
I(A) = I(A1) + I(A2) + I(A3) + I(A4) + …I(A1) I(A4)I(A2) I(A3)
+ + + … ¸ Q
Perhaps could use to solve our problem!!!
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I(B)
A1
A2
B2
B1
I(A)
Are we done? Q-MST algorithm works if I(A) is modular, i.e.,
if A and B disjoint, I(A [ B)=I(A)+I(B) Makes no sense for sensor placement!
Close by sensors are not independent For sensor placement, I is submodular
I(A [ B) · I(A)+I(B) [Guestrin, K., Singh ICML 05]
“Sensing regions” overlap, I(A [ B) < I(A) + I(B)
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Must solve a new problem
Want to optimize min C(A) subject to I(A) ¸ Q
if sensors provide independent informationI(A) = I(A1) + I(A2) + I(A3) + …
a modular problemsolve with Q-MST
but info not independent
sensors provide submodular information
I(A1 [ A2) · I(A1) + I(A2)
a new open problem!submodular steiner tree strictly harder than Q-MST
generalizes existing problemse.g., group steiner
Insight: our sensor problem has additional structure!
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Locality
If A, B are placements closeby, then I(A [ B) < I(A) + I(B) If A, B are placements, at least r apart, then
I(A [ B) ¼ I(A) + I(B)
Sensors that are far apart are approximately independent
We showed locality is empirically valid!
A1
I(B) B1
B2
r
A2
I(A)
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Our approach: pSPIEL
approximate by a modular problem:
for nodes Asum of rewards A ¼ I(A)
submodular steiner treewith locality
I(A1 [ A2) · I(A1) + I(A2)
solve modular approximation
with Q-MST
obtain solution of original problem
(prove it’s good)
use off-the-shelf Q-MST solver
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C1C2
C3C4
pSPIEL: an overview
¸ r
diameter · r
Build small, well-separated clusters over possible locations
[Gupta et al ‘03] discard rest (doesn’t hurt)
Information additive between clusters!
locality!!! Don’t care about
comm. within cluster (small)
Use Q-MST to decide which nodes to use from each cluster and how to connect them
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Our approach: pSPIELapproximate by a
modular problem (MAG):for nodes A
sum of rewards A ¼ I(A)
submodular steiner treewith locality
I(A1 [ A2) · I(A1) + I(A2)
solve modular approximation
with Q-MST
obtain solution of original problem
(prove it’s good)
use off-the-shelf Q-MST solver
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C1
C2
C4 C3
G1,1 G2,1
G4,1 G3,1
G1,2
G1,3
G2,2
G2,3
G4,2G4,3
G4,4
G3,2 G3,3
G3,4
pSPIEL: Step 3modular approximation graph
Order nodes in “order of informativeness”
Build a modular approximation graph (MAG)
edge weights and node rewards ! solution in MAG ¼ solution of original problem
Cost: C(G2,1[G2,2[G3,1[G4,1[G4,2) ¼
w4,1–4,2
w3,1–4,1
w2,1–3,1
w2,1–2,2
+ ++
Info: I(G2,1[G2,2[G3,1[G4,1[G4,2) ¼
most importantly,additive rewards:
R(G4,2)
R(G4,1) R(G3,1)
R(G2,2)R(G2,1)
+ + + +
if we were to solve Q-MST in MAG:
To learn how rewards arecomputed, come to our demo!
C1
C2
C4 C3
G2,1
G1,2
G2,3
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use off-the-shelf Q-MST solver
Our approach: pSPIELapproximate by a
modular problem (MAG):for nodes A
sum of rewards A ¼ I(A)
submodular steiner tree
I(A1 [ A2) · I(A1) + I(A2)
solve modular approximation
with Q-MST
obtain solution of original problem
(prove it’s good)
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C1 C2
C3C4
C1 C2
C3C4
pSPIEL: Using Q-MST
tree in MAG ! solution in original graph
Q-MST on MAG !solution to original problem!
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Our approach: pSPIELapproximate by a
modular problem (MAG):for nodes A
sum of rewards A ¼ I(A)
submodular steiner tree
I(A1 [ A2) · I(A1) + I(A2)
solve modular approximation
with Q-MST
obtain solution of original problem
(prove it’s good)
use off-the-shelf Q-MST solver
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Theorem: pSPIEL finds a placement A with
info. quality I(A) ¸ () OPTquality,
comm. cost C(A) · O (r log |V|) OPTcost
r depends on locality property
Guarantees for sensor placement
logfactor
approx.comm.cost
const.factor
approx.info.
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Summary of our approach
1. Use small, short-term “bootstrap” deployment to collect some data (or use expert knowledge)
2. Learn/Compute models for information quality and communication cost
3. Optimize tradeoff between information quality
and communication cost using pSPIEL4. Deploy sensors5. If desired, collect more data and
continue with step 2
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We implemented this… Implemented using Tmote Sky motes Collect measurement and link information
and send to base station
We can now deploy nodes, learn models and come up with placements!
See our demo onThursday!!
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Proof of concept study
Learned model from short deployment of 46 sensors at the Intelligent Workplace
Time
learned GPs forlight field & link qualities
deployed 2 sets of sensors:
pSPIEL and manually selected
locations
evaluated bothdeployments on
46 locations
0102030405060708090
100accuracy
CMU’s Intelligent Workplace
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Proof of concept study
Manual (M20) pSPIEL (pS19) pSPIEL (pS12)
0
10
20
30
4050
60
7080
90
100
0
5
10
15
20
25
30Root mean squares error (Lux)
bett
er
accuracy on46 locations
bett
er
Communication cost (ETX)
M20
M20
pS
19 pS
19
pS
12
pS
12
pSPIEL improve solution over intuitive manual placement: 50% better prediction and 20% less comm. cost, or 20% better prediction and 40% less comm. cost
Poor placements can hurt a lot! Good solution can be unintuitive
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Comparison with heuristics
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Temperature data fromsensor network
16 placement locations
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Hig
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rmat
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Optimalsolution
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Comparison with heuristics
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Greedy-Connect
Temperature data fromsensor network
16 placement locations
Greedy-Connect: Maximizes information quality, then connects nodes
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Comparison with heuristics
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Cost-benefitGreedy
Temperature data fromsensor network
16 placement locations
Greedy-Connect: Maximizes information quality, then connects nodes Cost-benefit greedy: Grows clusters optimizing benefit-cost ratio info. / comm.
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Comparison with heuristics
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pSPIEL
Greedy-Connect
Cost-benefitGreedy
Temperature data fromsensor network
16 placement locations
pSPIEL is significantly closer to optimal solution similar information quality at 40% less comm.
cost!
Greedy-Connect: Maximizes information quality, then connects nodes Cost-benefit greedy: Grows clusters optimizing benefit-cost ratio info. / comm.
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0 20 40 60 80 100 1200
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rmat
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pSPIEL Greedy-Connect
Cost-benefitGreedy
Comparison with heuristics
Precipitationdata167
locations
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Temperature data
100 locations
80More expensive (ETX)
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Hig
her
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rmat
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ity
pSPIEL
Greedy-Connect
Cost-benefitGreedySweet spotof pSPIEL
pSPIEL outperforms heuristics Sweet spot captures important region: just enough
sensors to capture spatial phenomena
Greedy-Connect: Maximizes information quality, then connects nodes Cost-benefit greedy: Grows clusters optimizing benefit-cost ratio info. / comm.
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Conclusions Unified approach for deploying wireless sensor
networks – uncertainty is fundamental Data-driven models for phenomena and link qualities
pSPIEL: Efficient, randomized algorithm optimizes tradeoff: info. quality and comm. cost guaranteed to be close to optimum
Built a complete system on Tmote Sky motes, deployed sensors, evaluated placements
pSPIEL significantly outperforms alternative methods