1. ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks Bugra Gedik Ling Liu Philip S. Yu IEEE Transactions on Parallel and Distributed Systems 2007 1

2. Introduction Sensors are power constrained Event-based data collection Periodic data collection Model-based data collection Intra-node modeling Inter-node modeling 2

3. System Model (1/2) 3

4. System Model (2/2) Adaptive data collection and model prediction 1. Collect sensing data for updating 2. Perform prediction Correlation-based sampler selection 1. Divide nodes into sub clusters 2. Select sampler nodes Sensing-driven cluster construction 1. Cluster formation 2. Cluster head selection 4

5. Sensing-driven Cluster Construction (1/3) 5

6. Sensing-driven Cluster Construction (2/3) m.org m.ttl m.rnd m.src m.dmu Hop distanceData distance 6

7. Sensing-driven Cluster Construction (3/3) 1.A limited TTL 2.Cluster-connection tree 7

8. Correlation-based Sampler Selection (1/3) 8

9. Correlation-based Sampler Selection (2/3) 9

10. Correlation-based Sampler Selection (3/3) 10

11. Model-based Prediction A. Prediction Model The values of non-sampler nodes are predicted by probabilistic model Forced samples do not propagate up to the based node Non-sampler Sampler 11

12. Model-based Prediction 12

13. Evaluations 13

14. A. Messaging Cost 14

15. B. Energy Consumption 15

16. C. Data Collection Quality(1/3) 16

17. C. Data Collection Quality(2/3) 17

18. C. Data Collection Quality(3/3) Only messaging and sensing consume energy Negative MAD implies the network exceeds its lifetime 18