•
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
s
m
P
x
U
,
)
(
)
}
({
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
s
k
s
S
s
M
m
s
ms
x
L
l
c
x
x
w
w
L
NETWORK
2
)
,
(
,
1
)
(
)
(
l
s
k
s
k
s
l
c
t
x
t
s
m
s
ms
x
U
t
x
w
)
(
)
(
)
,
(
,
)
(
)
1
*
)
(
(
*
)
(
)
(
)
(
s
flow
l
l
s
k
s
k
l
s
s
Miss
m
ms
s
c
t
t
x
t
w
t
x
dt
d
ss to pay"
"willingne
x
w
x
w
w
U
U
SINK
s
ms
ms
s
m
set
s
ms
m
m
m
m
m
m
:
*
.
0
over
)
(
maximize
:
)
;
(
)
(
(1)
(2)
(3)
(4)
Distributed Solution for INP

NUM
)
(
)
,
(
)
,
(
)
(
s
Miss
m
k
q
q
s
k
s
ks
out
q
s
k
tot
k
s
m
s
s
x
C
k
s
x
x
P
x
U
x
dt
dx
)
(
)
,
(
)
,
(
)
(
i
Miss
m
v
q
q
i
v
ki
vi
out
q
ki
v
tot
v
ki
m
ki
ki
l
C
v
i
x
l
P
l
U
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!
Enter the password to open this PDF file:
File name:

File size:

Title:

Author:

Subject:

Keywords:

Creation Date:

Modification Date:

Creator:

PDF Producer:

PDF Version:

Page Count:

Preparing document for printing…
0%
Comments 0
Log in to post a comment