Processing for Mission-oriented Wireless

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21 Νοε 2013 (πριν από 3 χρόνια και 6 μήνες)

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Sharanya Eswaran
, Penn State University


Matthew Johnson, CUNY


Archan Misra, Telcordia Technolgoies


Thomas La Porta, Penn State University

Utility
-
driven Energy
-
aware In
-
network
Processing for Mission
-
oriented Wireless
Sensor Networks

Annual Conference of ITA (September 24, 2009)

The Problem

.

.

.

Sensor Resources

Network Resources

Missions/

Applications

“How to share the network resources (bandwidth, energy) to maximize the

effectiveness of sensor
-
enabled applications (missions)?”




Limited bandwidth



Limited energy



Heterogeneous missions utilizing multiple types of sensors



Variable degrees of in
-
network processing

-

Forwarding nodes may compress or fuse data

Perimeter
monitoring

Gunfire
localization

Mobile insurgent
tracking

Surveillance

.

.

.

Image fusion

Correlation

In
-
network Processing


In
-
network processing is an attractive
option conserving bandwidth
and energy

o

Compression

o

Fusion



Non
-
negligible energy footprint for streaming applications


Stream
-
oriented data comprise sophisticated DSP
-
based operations
(e.g., MPEG compression, wavelet coefficient computation)




Forwarding nodes can compress on the fly

o
With variable compression ratios



Forwarding nodes can fuse multiple streams

o
the location of these fusion points can be determined on the fly


Dual trade
-
off

o

Bandwidth vs. loss of information

o

Communication cost vs. computation cost


Adaptive In
-
network Processing


Variable quality compression


Each forwarding node compresses data to different ratios, depending on


Residual energy at that and downstream nodes


Congestion in the region


Effect of compression on application



Dynamic fusion operator placement


Select best node in the path each time for fusion, depending on


Residual energy at that and downstream nodes


Congestion in the region



Variable source rate



1

2

A

C

M

B

Our Approach



Each mission has a “utility”:



A measure of how “happy” the mission is



A function of rates received from all its sensors




Allocate WSN resources (bandwidth and energy of
nodes) to maximize cumulative utility.


Network Utility Maximization (NUM)


A Distributed, Utility
-
Based Formulation of Resource Sharing

Objective:

“Joint Congestion and Energy Control for Network Utility Maximization”

Optimization Problem







M
m
k
nodes
k
tot
m
set
s
rec
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U
,
)
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)
}
({

maximize

Q
q
c
k
i
x
q
i
k
ki
out






,
1
)
,
(
:
constraint
Capacity

i)
subject to
)
,
(
k
comp
k
trans
k
rec
k
tot
k
k
tot
P
P
P
P
k
nodes
P
P






where
,
:
constraint
Energy

ii)
max,
Background: WSN
-
NUM Model



Airtime constraint over
“transmission
-
specific” cliques



Cliques => “contention region”



No two transmissions in a clique
can occur simultaneously

L
l
c
x
)
(X
U
L
U
SENSOR
l
(k,s)
k,s
s
M
m
m
m








clique

maximal
each
for
1
:
subject to

maximize
:
)
,
(
Connectivity graph

Multicast trees (with
broadcast transmissions)

Transmission
-
based

Conflict graph

2

1

3

4

5

m1

m2

m3

WSN
-
NUM Protocol


Price
-
based, iterative, receiver
-
centric scheme



Solve two independent sub
-
problems



Network nodes:


Aim to maximize “revenue”


Compute Clique cost: degree of
congestion in the clique


Flow cost = sum of costs of all cliques
along the flow


Mission (sink):



Aims to maximize its utility minus the
cost


Sends path cost to each source


Sends ‘willingness to pay’ for each
source


Sensor (source):


Adjusts rate to drive gradient to zero




.
0
over
,

clique
each
for

,
1

subject to
);
log(

maximize
:
)
;
(
)
,
(
,









s
s
k
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k
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L
NETWORK
2
)
,
(
,
1
)
(
)
(


















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Miss
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ss to pay"
"willingne
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SINK
s
ms
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set
s
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*
.
0
over
)
(

maximize
:
)
;
(
)
(








(1)

(2)

(3)

(4)

Distributed Solution for INP
-
NUM




























)
(
)
,
(
)
,
(
)
(
s
Miss
m
k
q
q
s
k
s
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out
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tot
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dt
dx































)
(
)
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(
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(
)
(
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Miss
m
v
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l
dt
dl




Impact on utility

1

2

A

C

M

B

At each source:

Energy cost

Congestion cost

At each forwarding node:

Impact on utility

Energy cost

Congestion cost



Two penalty values:


-

Congestion cost, µ


-

Energy cost,
η

Adaptive Operator Placement


We assume that fusion can be shared across multiple nodes


Can be thought of as time
-
sharing



Each candidate node fuses a fraction (
θ
) of the flow


Sink receives multiple sub
-
flows, each fused at a different node



Optimize
θ

such that fusion is most efficient

1

2

A

C

M

B




























)
(
)
,
(
,
,
,
,
,
)
,
(
)
(
i
Miss
m
v
q
q
i
v
k
s
op
vi
out
q
k
s
op
v
tot
v
k
s
op
m
k
s
op
k
s
op
C
v
i
x
P
U
dt
d








1

A

m

2

B

C

Flow 1: x
1

Flow 2: x
2

)
,
(
2
2
1
1
x
x
f
A
A


Af
l
1
1
)
1
(
x
A


1
A
l
2
2
)
1
(
x
A


2
A
l
`

Illustration of INP
-
NUM

Fused flow f

Challenges in INP
-
NUM Protocol


Missions do not know about original flow and the
transformations (compression and fusion)


Fusion placement and compression ratio adaptation require
different sets of data.


Feedback received and processed by each forwarding node in the
path


It is modified before forwarding upstream


If it is a fusion point, it updates the feedback to include the effect
of fusion


Based on chain rule of differentiation


dx
dx
dx
dx
dx
dx
dx
dU
dx
dU
n
rec
rec





2
1
1
Illustration of INP
-
NUM Feedback

1

A

m

B

f
f
x
U
x



*
pay

to
s
willingnes
m
f
C
f
B
f
A
f
f
x
x
x
x
x




C

2

2

1

2

f
A

f
B

Cumulative Info

2

1

2

f
A

Cumulative Info

Rate Info

Energy Info

Congestion Info

Rate Info

Energy Info

Congestion Info

1

Rate Info

Energy Info

Congestion Info

Cumulative Info

2

Rate Info

Energy Info

Congestion Info

Cumulative Info

Addressing Practical Constraints


Often in reality, fully elastic compression may not be possible


Only discrete levels of compression


E.g., JPEG allows 100 discrete values for compression ratio, video may be
encoded in a finite set of bitrates depending on the encoding technique


Similarly, partial fusion may not be feasible


Fusion operation may need to take place at a solitary node.


NP
-
hard to solve both problems without these assumptions


We can use approximation heuristics


Determine nearest valid compression ratio


Pick node with most responsibility for solitary fusion

Evaluation

High Utility

Medium Utility

Low Utility

Utility Gain

Effect of Discretization

Conclusion


Protocol for adaptive compression and fusion placement


Fully distributed


Low overhead


Provably optimal utilization of bandwidth and energy



Heuristics for realistic constraints provide near
-
optimal
solution



In future, we will develop a model taking lifetime
requirements of missions into account


Thank You!