Adaptive Sampling for Sensor Networks Ankur Jain ٭ and Edward Y. Chang University of California, Santa Barbara DMSN 2004

08/30/2004DMSN Outline  Sampling in sensor networks  Adaptive sampling using Kalman Filter  Problem formulation  Results

08/30/2004DMSN 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

08/30/2004DMSN 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

08/30/2004DMSN 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

08/30/2004DMSN 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

08/30/2004DMSN 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)

08/30/2004DMSN 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

08/30/2004DMSN 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

08/30/2004DMSN 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

08/30/2004DMSN 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

08/30/2004DMSN 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

08/30/2004DMSN Results – ERU vs. Number of Sources

08/30/2004DMSN Results – ERU vs. Sliding Window Size

08/30/2004DMSN 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

08/30/2004DMSN Thank you !