1. Department of Computer Science University of Massachusetts, Amherst PRESTO: Feedback-driven Data Management in Sensor Network Ming Li, Deepak Ganesan, and Prashant Shenoy University of Massachusetts, Amherst (*PREdictive STOrage )

2. UNIVERSITY OF MASSACHUSETTS, AMHERST Emerging large-scale sensor networks ◊Hierarchical wireless networks composed of low power sensors. ◊Enables densely and closely monitoring of phenomena. Tracking Surveillance Structure/Machinery Monitoring

3. UNIVERSITY OF MASSACHUSETTS, AMHERST Hierarchical Sensor Network Architecture Internet Client Data Browsing, Querying and Processing Mesh Network Base-station Sensor Proxy Remote Sensors Sensor Proxy Remote Sensors

4. UNIVERSITY OF MASSACHUSETTS, AMHERST Approaches to Proxy-Sensor Interaction Sensor-centric Architecture Proxy-centric Architecture

5. UNIVERSITY OF MASSACHUSETTS, AMHERST Proxy-Centric Architecture ◊Overview  Proxy determines when to pull data, which sensor to query, and what data to pull using complex modeling and query processing mechanisms. ◊Pros:  Intelligence placed where resources are available.  More complex algorithms possible. ◊Cons:  Cannot capture anomalies.  Less energy-efficiency  Greater query error. BBQ [Deshpande04]

6. UNIVERSITY OF MASSACHUSETTS, AMHERST Sensor-Centric Architecture ◊Overview  Forward queries into the sensor network. Perform data fusion, query processing and filtering within the network. ◊Pros:  Greater query accuracy  Better energy-efficiency. ◊Cons:  Greater sensor complexity.  Greater query latency. Directed Diffusion [Heidemann01]

7. UNIVERSITY OF MASSACHUSETTS, AMHERST PRESTO Model Sensor-centric Proxy-centric PRESTO

8. UNIVERSITY OF MASSACHUSETTS, AMHERST Key Ideas in PRESTO ◊Steal from the rich (proxy) and give to the poor (sensors). ◊Exploit predictable structure in sensor data when possible. ◊Adapt to data & query dynamics to minimize energy usage. ◊Exploit low-power storage for efficient archival querying.

9. UNIVERSITY OF MASSACHUSETTS, AMHERST Outline ◊Motivation ◊Key Ideas ◊Example ◊ARIMA Model ◊Evaluation ◊Summary & Future Work

10. UNIVERSITY OF MASSACHUSETTS, AMHERST Sensor Proxy Example-Modeling Data Model Build Model

11. UNIVERSITY OF MASSACHUSETTS, AMHERST Sensor Example-Model Driven Push Proxy Predict Yes

12. UNIVERSITY OF MASSACHUSETTS, AMHERST Sensor Example-Query Proxy Query What is the reading at time t with confidence c? Yes No Pull T t

13. UNIVERSITY OF MASSACHUSETTS, AMHERST Sensor Proxy Example-Feedback Build Model Model

14. UNIVERSITY OF MASSACHUSETTS, AMHERST Sensor Example - Update Cache after Push Push T t Proxy Interpolatio n

15. UNIVERSITY OF MASSACHUSETTS, AMHERST Sensor Example - Update Cache after Pull Pull T t Proxy Interpolatio n Re- prediction

16. UNIVERSITY OF MASSACHUSETTS, AMHERST Outline ◊Motivation ◊Key Ideas ◊Example ◊ARIMA Model ◊Evaluation ◊Summary & Future Work

17. UNIVERSITY OF MASSACHUSETTS, AMHERST Goals ◊Catches data trends ◊Easy to compute on sensors

18. UNIVERSITY OF MASSACHUSETTS, AMHERST Data Trends ◊Temperature data trace shows very obvious temporal trend ◊Shows both long term trend and short term trend. Seasonal Period

19. UNIVERSITY OF MASSACHUSETTS, AMHERST Data Trends ◊ARIMA model can catch both of these trends Long Term Trend Short Term Trend

20. UNIVERSITY OF MASSACHUSETTS, AMHERST Computation ◊Easy to predict  Five additions and three multiplies Previous prediction results Previous prediction errors

21. UNIVERSITY OF MASSACHUSETTS, AMHERST Outline ◊Motivation ◊Key Ideas ◊Example ◊ARIMA Model ◊Evaluation ◊Summary & Future Work

22. UNIVERSITY OF MASSACHUSETTS, AMHERST Evaluations ◊Both numerical simulations and real deployments ◊Test Bed:  1 Stargate (Proxy) / 20 Tmote’s (Sensor)  1 Stargate acts as emulator ◊Data Trace:  James Reserve

23. UNIVERSITY OF MASSACHUSETTS, AMHERST Micro Benchmark ComponentOperation Energy (nJ) NAND Flash 20B Read + 8B Write 152 MSP430 Processor Predict 1 Sample 24 CC2420 Radio Transmit 1 byte 2000 Model Asymmetry ComponentOperationEnergy (nJ) StargateModel Building 11000 Telos Mote Predict 1 Sample 24 Cost of model building is 500x more than prediction Total cost of prediction and storage is 10x less than communication. Breakdown of Energy Costs

24. UNIVERSITY OF MASSACHUSETTS, AMHERST Model-driven Push Performance ◊Matlab simulation shows that Model-driven push performs better than model-driven pull.

25. UNIVERSITY OF MASSACHUSETTS, AMHERST Scalability ◊Impact of System Scale  Uses emulator to get large network scale Support up to 100 sensor nodes per proxy

26. UNIVERSITY OF MASSACHUSETTS, AMHERST Scalability ◊Impact of Query Frequency  System adapts to high query frequency.  Query latency does increase with query frequency Most of the queries are answered using proxy cache

27. UNIVERSITY OF MASSACHUSETTS, AMHERST Adaptation ◊Adapt to query dynamics  Reduce query latency by 50% compared to before adaptation Adapt to the low query tolerance after a short period Average query tolerance changes to a lower value which brings more pulls

28. UNIVERSITY OF MASSACHUSETTS, AMHERST Adaptation ◊Adapt to data dynamics  Reduce communication by 30% compared to non-adaptive scheme Reduces 30% of communications

29. UNIVERSITY OF MASSACHUSETTS, AMHERST Failure Detection ◊Detect sensor failure using pulling messages  Detection latency decreases with query interval, as well as query tolerance. Longest detection latency less than 2 hours

30. UNIVERSITY OF MASSACHUSETTS, AMHERST Summary and Future Work