Adaptive Sampling for Sensor Networks

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

2. Outline • Sampling in sensor networks • Adaptive sampling using Kalman Filter • Problem formulation • Results DMSN 2004

3. 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 DMSN 2004

4. 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 DMSN 2004

5. 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 DMSN 2004

6. 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 DMSN 2004

7. Projects the current state estimate Adjusts the current state estimate The KF cycle Measurement from the sensor Measurement Update (Correct) Time Update (Predict) Estimation Error (ER) DMSN 2004

8. 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 DMSN 2004

9. 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 SIavoiding delays due to server response or network congestion DMSN 2004

10. Sensor Side • NewSI 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 inER over sliding window • If SI new is out of range, a new SI is requested from the server • ΔSI – change in SI requested DMSN 2004

11. 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 DMSN 2004

12. 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 DMSN 2004

13. Results – ERU vs. Number of Sources DMSN 2004

14. Results – ERU vs. Sliding Window Size DMSN 2004

15. 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 DMSN 2004

16. Thank you ! DMSN 2004