Adaptive Sampling for Sensor Networks

Ankur Jain٭and Edward Y. Chang

University of California, Santa Barbara

DMSN 2004

08/30/2004 DMSN 2004 2

Outline Sampling in sensor networks Adaptive sampling using Kalman Filter Problem formulation Results

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Sampling in Sensors Sampling Interval (SI) – time interval

between successive measurements Sensitive to streaming data characteristics,

query precision and available resources Over-sampling comes at increased resource

usage CPU – at the sensor and the central server Network Bandwidth – within the sensor network Power Usage – at the sensor

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Examples Habitat Monitoring – Animal activity

Higher bandwidth to sensors reporting “interesting events”

Unusual changes in temperature, sound levels

Video Surveillance – Parking Lot Higher rate video capturing in area

“experiencing unexpected traffic pattern” Random swirling, speeding

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Related Work Network Contention

Considers network contention before putting data on the network channel

Better delivery rate at the server Stochastic Estimation

Adapts to input data characteristics using stochastic models

Does not consider multiple sensors scenario

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Modeling Streaming Data Characteristics A Kalman Filter (KF) is used by each

sensor to estimate expected values (value at the next measurement)

Estimation error (ER) from KF is used to quantify streaming data characteristics High error compensated by lower SI

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The KF cycle

Time Update(Predict)

Measurement Update(Correct)

Adjusts the current state estimate

Projects the current state estimate

Measurementfrom the sensor

Estimation Error (ER)

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Adaptive Sampling All sensors stream updates to a central

server ER is calculated at each measurement Based on ER, the sensors can adjust the

sampling interval within a specified range SIR (Sampling Interval Range)

Beyond the range the sensor requests the server for lower sampling interval (more bandwidth)

The server allocates bandwidth based on available resources

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Sensor Side No server mediation required as long as the

desired change in Sampling Interval (SI) is within SIR

SI last – last SI received from the server SI desired – desired SI to reduce ER

High activity streams can be captured at low SI avoiding delays due to server response or network congestion

)/()/( 22 SIRSISISIRSI lastdesiredlast

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Sensor Side New SI is proportional to estimation error from

the KF over a sliding window of sizeW

SI new – desired SI SI current – current SI θ – user parameter (max. change in SI) f – fractional change in ER over sliding window

If SI new is out of range, a new SI is requested from the server

ΔSI – change in SI requested

)1(* fcurrentnew eSISI

2/SIRSISI new

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Server Side The server puts requests in a queue with 5 attributes

Fractional Error (f) – fractional error at the sensor Request (Req) – change in SI requested History (h) – age of the request in the queue Grant (g) – amount by which the request has been satisfied Query Weight (w) – Weight from the query processor

The server forms an optimization problem such that A is the amount granted and Ravail is the available resource

ReqA

RA.t.s

Req

A*

g

g*

w

w*

h

h*

f

fmin avail

A ≤0

≤1 ∑

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Experiments Oporto simulator used to obtain trajectories of

moving shoals One sensor per shoal (12 Shoals) 3000 tuples at each sensor Results compared with uniform sampling

approach Effective Resource Utilization (ERU) ξ

m* η – mean fractional error between real and actual trajectorym – fraction of messages exchanges between sensors and server

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Results – ERU vs. Number of Sources

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Results – ERU vs. Sliding Window Size

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Future Work Extension to multi hop sensor networks Application of other estimation models

(particle filters) Dynamic SIR’s Development of better algorithms to

reduce message overheads

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Thank you !