Prediction-based Monitoring in Sensor Networks: Taking

Lessons from MPEG

Samir Goel and Tomasz Imielinski

Department of Computer Science

Rutgers, The State University Of New Jersey

ACM Computer Communication Review

Vol. 31, No. 5, October 2001

Outline

Background Model PREMON Experiment Conclusion

Background

The compression techniques in MPEG-2– Spatial compression– Temporal compression

Model

Large, non-deterministic topology Cluster-based Limited energy Access points Location-aware

PREdiction Based MONitoring

Update-mode Centralized approach A base station maintains the database of

current reading of all the sensors in the sensor field.

Classes of Prediction Models

Spatial– Reading at sensor X in time slot t is the same as r

eading at sensor Y during the same time slot. Temporal

– Reading at sensor X in time slot t is 2 greater than its reading in the previous time slot

Spatio-temporal– Reading at sensor X in time slot t is the same as t

he reading of sensor Y in the previous time slot. Absolute

– Readings at sensor X in time slots t, t+1, and t+2 will be 32, 34, and 35 respectively.

Key Characteristics of PREMON

Trades computation for communication– Cost(computation) << Cost(communication)

Works well if one can tolerate:– “small” amount of errors in predictions– “some” latency in generating prediction models

Applicable whenever correlation (temporal, spatial, or spatio-temporal) exists

The Framework

Spatial-Temporal Assumption

– All sensors within the <spatial-region> fall within one cluster.

– All sensors operate in the update-mode. The sensors in the <spatial-region>

Visualization

Monitoring may be seen as watching the snapshot images on a continuous scale

Visualization

Monitoring may be seen as watching a video of sensed values

Prediction Operation

Monitoring operation:– Initially, all sensors transmit their current reading t

o the base station– Subsequently, sensors transmit only when their re

adings change In the visualization:

– Initially, the full image is transmitted– Subsequently, only the diffs from the previous ima

ge are transmittedThis is analogous to how MPEG encodes a video!

PREMON

Apply block-matching algorithm to compute motion-vectors

Translate motion-vectors into motion-predictions

Frame#2Frame#1

<2, 0>

Frame#3

Is<2, 0>valid ?

Translating Motion-vectors into Prediction Models

No-motion case (motion-vector: <0, 0>):– Generate a Constant Value Prediction

General Case (Motion-vector: <dx, dy>):– Generate a Movement Prediction

MPEG Analogy

Sensor Field

Base station

updateframes

All sensors send data (I-frame)

Sensors send updates when their valuediffers from predicted ones (P-frames)

sensor

predictions

time

Differences between MPEG and PREMON

Hard real-time requirements for MPEG Soft real-time requirements for sensor nets Limited energy for sensor networks The number of sensors is small compared

to pixels The frame rate is an order of magnitude

higher compared to PREMON Non-uniform placement

Architecture

Processing at the Base Station– Collect updates from the sensors– Generate prediction model– Send the update– Send a set of prediction models

(If the previous model resulted in fewer updates) When low on power, the base station may divide

its cluster in spatial blocks and may only send average reading of each block to the access point.

Architecture

Processing at the Sensor– Update-mode by default: send an update

whenever the reading changes– Receive a prediction-model– After the prediction-model expires, revert

back to update-mode.

Prediction Model

Gridding– Interpolate or extrapolate the readings at gri

d points– Assign the closest sensor to a grid point– Or assign the average of the closest sensors– Transparent grid point

Prediction Model

Divide the image into macro-blocks

Block-matching and find motion vectors

Transparent pixel matches any other pixel

Only when the percentage of transparent pixels in a macro-block is above threshold

Prediction Model

The base station– With 4 most recent frames, apply block-ma

tching to frames 1 and 2 to get MVs.– For each MV, check frames 2 and 3, and 3

and 4.

Prediction Model

If a motion vector “holds”, generate an absolute model based on it; otherwise, discard it.

Their data is encoded in a more efficient way – depending on the type of sensors.

The magnetometers output binary values: LOW/HIGH

Only the coordinate of the largest rectangle of 1s is sent and only the prediction model within this range is valid.

While no motion, send only one flag to indicate it.

Prediction Model

Type– Absolute– Spatial– Temporal– Statio-temporal

Model– Tuples(<time, reading>) or a funciton

Destination– A broadcast address, sensor id, or a spatial polygon

TTL– Valid time

Experiments

4 MHz processor Radio: 10kbps 8KB program memory 512 bytes data memory Light sensor

Experimental Setup335 333 331

317 315 313 311

BS

BS location of base-station-mote

sensor-mote

focused light source

335 333 331

317 315 313 311

BSBS

BSBS location of base-station-mote

sensor-mote

focused light source

Experimental Setup One-dimensional version of the problem Base-station code fully resides in a mote Cases considered:

– Case#1: Default mode: Sensors send their sensed values once every second

– Case#2: Constant-value predictions only BS makes a constant value prediction if the value of a sens

or doesn’t change for 2 consecutive frames. BS doesn’t transmit movement predictions

– Case#3: Constant and movement predictions BS issues both constant-value and movement predictions BS makes a movement prediction based on two correlated

motio-sensor readings

Constants

Cost of transmission/bit = 1 µJ Cost of reception/bit = 0.5 µJ Cost of computing = 0.8 µJ per 100 instructions Update size = 11 bytes (Tu = 88 µJ, Ru = 44 µJ) Prediction size

– Movement Prediction = 8 bytes (Tp = 64 µJ, Rp = 32 µJ)– Constant Value Prediction = 5 bytes (Tp = 40 µJ, Rp = 20 µJ)

Results

Performance Graph

0

100

200

300

400

500

600

1 2 3Cases

Ener

gy C

omsu

med

(mJo

ules

)

Sensor#4 Sensor#8 Sensor#12

Sensor#14 Base-station-mote Total Energy Consumed

950mJCase#1: default mode

Case#2: constant-value predictions only

Case#3: constant and movement predictions

Summary of Results:- Case#3 performs 5 times better than case#1- Case#3 performs 28% better than case#2

Conclusion

Prediction-based monitoring paradigm can significantly increase energy efficiency

Monitoring of sensor data may be visualized as watching a “video” and MPEG-2 algorithms may be adapted for generating predictions