Adaptive Data Aggregation for Wireless Sensor Networks

Adaptive Data Aggregation for Wireless Sensor Networks

S. Jagannathan

Rutledge-Emerson Distinguished Professor

Department of Electrical and Computer Engineering

Professor of Computer Science

Missouri University of Science and Technology

Rolla, MO 65409.

[email protected]

1Research performed by Priya Kasirajan is thankfully acknowledged

mailto:[email protected]

Agenda• Introduction• Background• Challenges• Proposed Methodology• Results and Discussion• Hardware results

• Conclusions and Future work

2

• Why compression?– Reduction in amount of data transmitted– Reduction in energy consumption– Improvement in network lifetime

• Compression vs Aggregation– Data condensed at the source

node– Aggregation implies data from spatially separated sensors combined

statistically using min, avg, max, count, sum– Need location or node ID

Node

Clusterhead

Introduction

3

Background

4

• Survey of data aggregation (Rajagopalan and Varshey, 2006)– Chain based data aggregation, tree based, PEDAP, Grid based, Network flow based, network

correlated data aggregation, QoS-aware aggregation

• Quantization

– Lossy compression scheme

– Quantization error is proportional to step size

– Step size is dependent on dynamic range

• Adaptive Differential Pulse Code Modulation (ADPCM)

– Quantize difference between actual sample and estimated sample

– Exploits the correlation between adjacent samples to reduce bit rate and to achieve compression.

• Real world sensor data with multiple modalities does not always boast correlation and linear relationship

Challenges

• Data compression/aggregation can be a complex nonlinear process

• Nonlinear processing is computationally more intensive

• Data reconstruction can be involved– Location aware or context aware– Node ID

• Performance guarantees in terms of distortion, compression ratio, energy efficiency, hard to show

5

Proposed MethodologyChannel

e(k)

Some y(k)

y(k)

Quantizer

EstimatorEstimator EncoderEncoder DecoderDecoder EstimatorEstimator

Analytical ResultsTheorem 1 (Estimator-Ideal Performance): In the ideal case with no

reconstruction errors and noise present, the estimation error approaches to zero asymptotically while the parameter estimation error vector is bounded.

Theorem 2 (Estimator Performance—General Case): Let the hypothesis presented in Theorem 1 hold and if the functional reconstruction error is bounded, then estimation error is bounded while the parameter errors are also bounded.

Analytical Results (contd.)

Theorem 3 (NADPCMC Distortion): If the estimator reconstruction and quantization errors are considered bounded, then the distortion at the destination is bounded. On the other hand in the absence of estimator reconstruction and quantization errors, the distortion is zero.

Theorem 4 (NADPCMC Performance): The compression ratio, defined as the ratio of the amount of uncompressed data to the amount of compressed data, is greater than one. Moreover, the proposed scheme will render energy savings.

8

Simulation Results

• River Discharge Data• Audio Data• Geophysical Data

FLoating point Operations Per Second – FLOPSNADPCMC encoding

7050 FLOPS 1.224 micro joules

NADPCMC decoding7425 FLOPS 1.289 micro joules

XBee radio – transmit power – 1 mW for 30 m

Energy Consumption

River Discharge Data

0 20 40 60 80 100 120 140 160 1800

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Iteration

Am

plit

ud

e

NADPCMC with 8 bit encoded error

Original

Decoded

0 20 40 60 80 100 120 140 160 180-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Iteration

Re

con

stru

ctio

n e

rro

r

Total error in NADPCMC reconstruction

8 bit error

6 bit error

Time Time

River Discharge Data (contd.)

11

MethodCompression

ratio

Energy savings at

nodes

Energy savings at CH

Distortion Overhead

Huffman 1.453 NA 31.177% NA 480 bytes

Differential Huffman 1.642 21.56% 39.099% NA 480 bytes

Scaling and

approximation1.137 13.65% 11.65% 0.0657% 0

Scaling and 9 bit

quantization1.778 43.76% 43.76% 0.943% 0

Scaling and 8 bit

quantization2.000 50% 50% 2.0685% 0

Scaling and5 bit

quantization3.200 68.75% 68.75% 16.451% 0

Linear ADPCM 2 50% 50% 13.72% 0

NADPCMC with 8 bit

encoding1.9459 48.61% 48.61% 2.65% 10 bytes

NADPCMC with 6 bit

encoding2.5487 60.76% 60.76% 6.08% 10 bytes

Audio Data

0 0.5 1 1.5 2 2.5 3 3.5 4

x 104

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

Iteration

To

tal r

eco

nst

ruct

ion

err

or


0 0.5 1 1.5 2 2.5 3 3.5 4

x 104

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

IterationT

ota

l re

con

stru

ctio

n e

rro

r


Time Time

Audio Data (contd.)

13

Method Data rateCompression ratio

Energy savings at nodes

Distortion Overhead

Scaling and 8 bit

quantization

64kbps 2 50% 10.59% NA

Scaling and 6 bit

quantization

48kbps 2.67 62.5% 46.28% NA

5 bit linear ADPCM 40kbps 3.199 68.74% 11.37% NA

4 bit linear ADPCM 32kbps 4 75% 23.14% NA

3 bit linear ADPCM 24kbps 5.332 81.25% 28.45% NA

2 bit linear ADPCM 16kbps 8 87.5% 35.86% NA

NADPCMC with 8 bit

encoding

64kbps 1.9992 49.98% 2.04% 20 bytes

NADPCMC with 6 bit

encoding

48kbps 2.6653 62.48% 6.16% 20 bytes

NADPCMC with 4 bit

encoding

32kbps 3.997 74.98% 14.44% 20 bytes

0 100 200 300 400 500 600 700 800 900 1000-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

Iteration

To

tal r

eco

nst

ruct

ion

err

or


Geophysical Data Performance

14

0 100 200 300 400 500 600 700 800 900 1000-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

Iteration

To

tal r

eco

nst

ruct

ion

err

or


Time Time

MethodCompression ratio

Energy savings at nodes

Distortion Overhead

Scaling and 8 bit quantization 2 50% 4.36% 0

Scaling and 6 bit quantization 2.667 62.5% 13.42% 0

Linear ADPCM 2 50% 35.87% 0

NADPCMC with 8 bit encoding 2 50% 1.02% 20 bytes

NADPCMC with 6 bit encoding 2.667 62.5% 4.22% 20 bytes

Aggregation using NADPCMC

• 8 bit NADPCMC at all source nodes• 6 bit NADPCMC at CH 1, 2 and 3 –

61.34% savings – 1.90%• 4 bit NADPCMC at CH 1, 2 and 3 –

73.61% savings - 6.10%• 4 bit NADPCMC at CH 5 – 74.54%

savings –– Synthetic data: 7.01%– River discharge data: 4.83%– Audio data: 6.09%

15

Hardware Implementation

16

• Compression ratio – 1.846• Energy savings – 45.83%• Distortion – 1.67%

• Compression ratio – 2.526• Energy savings – 60.42%• Distortion – 4.60%

Nano Sensor Data Performance

17

0 100 200 300 400 500 600 700 8000

0.5

1

1.5

2

2.5

3

3.5

Time

Am

plit

ud

e

Compression and Aggregation

Original

6 bit Aggregation4 bit Aggregation

0 20 40 60 80 100 120 140 160 1800

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Time

Am

plit

ud

e

Compression and aggregation

Original

6 bit aggregation4 bit aggregation

Data

generation

rate

Transmissio

n rate

Compress

ion ratio

Energy

savings

Distortion

Uncompress

ed data

2.56 kbps 3.424 kbps NA NA NA

Compressed

data

2.56 kbps 1.712 kbps 1.8751 46.67% 0.78% for sensor data

0.81% for river discharge data

Compressed

and

aggregated

data – 6 bit

NADPCMC

2.56 kbps 1.284 kbps 2.3250 56.99% 3.58% for sensor data


Compressed

and

aggregated

data – 4 bit

NADPCMC

2.56 kbps 856 bps 3.5002 71.43% 8.21% for sensor data


Conclusions

• Data aggregation process is nonlinear and must be location/self-aware for enhanced performance

• NADPCMC addresses nonlinear issues in data and performs well for different sensor modalities.

• Aggregation is achieved through iterative compression.

• Performance depends on number of aggregation levels and Quantizer resolution.

• Network size does not impact performance.

• Future work involves evaluation of the proposed scheme for larger size networks with different types of data by considering latency, life time and security

Documents

Adaptive Data Aggregation for Wireless Sensor Networks