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Adaptive Sampling for Sensor Networks Ankur Jain ٭ and Edward Y. Chang University of California, Santa Barbara DMSN 2004
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08/30/2004DMSN 20042 Outline Sampling in sensor networks Adaptive sampling using Kalman Filter Problem formulation Results
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08/30/2004DMSN 20043 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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08/30/2004DMSN 20044 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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08/30/2004DMSN 20045 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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08/30/2004DMSN 20046 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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08/30/2004DMSN 20047 The KF cycle Time Update (Predict) Measurement Update (Correct) Adjusts the current state estimate Projects the current state estimate Measurement from the sensor Estimation Error (ER)
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08/30/2004DMSN 20048 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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08/30/2004DMSN 20049 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
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08/30/2004DMSN 200410 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
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08/30/2004DMSN 200411 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 R avail is the available resource
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08/30/2004DMSN 200412 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 η – mean fractional error between real and actual trajectory m – fraction of messages exchanges between sensors and server
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08/30/2004DMSN 200413 Results – ERU vs. Number of Sources
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08/30/2004DMSN 200414 Results – ERU vs. Sliding Window Size
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08/30/2004DMSN 200415 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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08/30/2004DMSN 200416 Thank you !
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