•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 ResourcesMissions/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 fusionCorrelation

In-network Processing

• In-network processing is an attractive option conserving bandwidth and energyo 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 flyo With variable compression ratios

• Forwarding nodes can fuse multiple streamso the location of these fusion points can be determined on the fly

• Dual trade-off o Bandwidth vs. loss of informationo 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

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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

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Background: WSN-NUM Model

Airtime constraint over “transmission-specific” cliques

Cliques => “contention region”

No two transmissions in a clique can occur simultaneously Ll

c

x

)(XU

LUSENSOR

l(k,s) k,s

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Connectivity graph Multicast trees (with broadcast transmissions)

Transmission-basedConflict 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

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, cliqueeach for ,1 subject to

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Distributed Solution for INP-NUM

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Impact on utility

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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

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Flow 1: x1Flow 2: x2

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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

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Illustration of INP-NUM Feedback

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Cumulative Info

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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!