1. RIDA: A Robust Information-Driven Data Compression Architecture for Irregular Wireless Sensor Networks Nirupama Bulusu (joint work with Thanh Dang, Wu-chi Feng) Portland State University http://www.cs.pdx.edu/~nbulusu

2. Data Compression –Communication is expensive in dense WSNs Dominant energy consumer; limited network capacity Useful to compress sampled data before transmission

3. Objective Design a robust architecture for data compression and analysis in sensor networks

4. Idea A sensor data map can be viewed as an image Can we apply image compression techniques to sensor networks? X Y

5. Compression Challenges Multiple data sources, limited memory and computation Irregular network topology Unavailability of meta-information such as sensor location Frequent failures/missing sensor data Network and environmental dynamics

6. Contributions Multiple data sources, limited power, memory and computation Irregular network topology Unavailability of meta information such as sensor location Frequent failures/missing sensor data Network and environmental dynamics Resiliency mechanism Logical Mapping Distributed data transformation RIDA: A Robust Information-Driven Data Compression Architecture

7. Outline Related Work Understanding Data Correlation RIDA: Robust Information-Driven Architecture Evaluation Conclusion and Future Work

8. (Partial) Related Work Source Coding –Lempel-Ziv-Welch (S-LZW) (Sadler et al, Sensys 06) –Individual node codes the source using LZW algorithm –Delay, not robust to failure, does not explore spatial correlation Channel Coding –DISCUS (Pradhan et al, ISIT 03) –Code the channel with side-correlated information to reduce number of information bits –No guarantees for optimal performance

9. (Partial) Related Work Transform Coding –Fourier-Based Transform, Wavelet-Based Transform (Cancio and Ortega, ICASSP 2004, Raymond et al) (Ganesan et al 2003), Random Projection (Candes and Tao, 2004), –Rely on communication path, location, regularity of the networks –Most of these only focus on adapting the transformation to the network, no guarantee of optimal performance, not robust to failure

10. Understanding Data Correlation Physical Map Light Reading Map Sensors that are not spatial neighbors can report correlated data

11. Sensors with similar voltage level tend to degrade together regardless of changes in environmental condition Understanding Data Correlation Physical Map Voltage Reading Map Correlation of data may be independent from external factors such as location

12. Thesis To explore the correlation of sensor data, examine the value of the data itself (information) –Correlation amongst sensor data can be obtained by statistically observing the data values over a short period of time

13. RIDA: A Robust Information- Driven Architecture Transmit only non-zero coefficients RESILIENCY MECHANISM Node id = 16 Logical node id = (7, 7)

14. RIDA: Logical Mapping Logical Mapping – M is the mapping from sensor s to logical index (x,y) based on d(s), the data value of sensor s D, the set of data values of all sensors in the cluster only consider a single-hop cluster in this work intended to be periodic Choosing M – depends on specific applications and underlying basis functions for data transformation – Gradients with DCT

15. RIDA: Distributed Data Transformation A node calculates only the coefficient corresponding to its index –E.g.: With 2-D mapping, for node (i, j) perform DCT operations only on corresponding row i and column j Only non-zero coefficients are transmitted Flexible to work on logical indices

16. RIDA: Resiliency Mechanism Original data 255 128 0 64 Classify values below a threshold as faulty Decompressed data Project to [128,255] Missing readings Normal readings CompressionDe-compression

17. Evaluation of RIDA Compression Performance –Logical mapping (1D, 2D) –Data transformation (DCT, Wavelets) Robustness –Accuracy vs. number of faulty sensors Energy and Bandwidth Savings

18. Methodology Experiments on real world data –source: Intel Research, Berkeley –54 sensors from February 28th and April 5th, 2004 –Modified data set : sensor data is interpolated in time –Real data set: sensor data is kept as original

19. Metrics: Compression Performance Compression Ratio n: number of nodes n’: number of non-zero coefficients Normalized MSE d i : reconstructed value of sensor data o i : original value of sensor data n : number of nodes 1/2

20.  Mapping vs. without mapping Logical (Sorted) mapping gives lower error than without mapping for the same Data transformation scheme Normalized MSE (%) Compression Ratio Compression Performance (Ideal Data)

21. Compression Performance on Real Data (Humidity) DCT slightly better than Wavelet Although the compression ratio is around 4:1, error is less than 5% Quantization Scale Compression Ratio Normalized MSE (%)

22. Metrics: Error Detection TP-True Positive: # correctly classified healthy nodes TN-True Negative: # correctly classified faulty nodes FP-False Positive: # incorrectly classified healthy nodes FN-False Negative: # incorrectly classified faulty nodes

23. Even when half the nodes are missing, accuracy > 90%, recall > 97% Classification Recall (%) Number of faulty nodes Detection Accuracy (%) Error Detection

24. Conclusions RIDA: A Data Compression Architecture –Time-slicing across multiple sensor data streams –Information-driven approach maximally leverages correlation –Logical mapping decouples compression from physical topology –Resiliency mechanism provides robustness to data loss –Adapted to DCT and Wavelet Transforms Results –Compression ratios of 10:1 (ideal) and 4:1 (real) with less than 5% error –90% accuracy, 97% recall even when half the network data is missing.

25. Future Directions Appropriate system parameters for sensor data –projection range, quantization Energy Balancing System Deployments Non-scalar or high rate data –vibration, audio and video

26. Thank You Questions?

27. Backup Slides

28. RIDA: Deployment View

29. Performance in a Faulty Environment (Temperature) Even when half the nodes are missing, compression ratio of 4:1 can be achieved with less than 5% of error DCT results in much lower error with the same compression ratio Normalized MSE (%) Compression Ratio Number of faulty nodes

30. Metrics: Energy Savings Bench mark : n is the network size h is average hop count t x, t r are transmitting and receiving power Compression using DCT transform d : cost to compress the data n ’ : number of non-zero coefficients, n/n’ ~ 20 for jpeg Energy Saving:

31. Energy Savings Power compression for transmitting one packet is still 2.5 times that of sensing and data compression Simulation Time (Virtual) Consumed Energy (mJ) Ratio Energy Consumption by RF and CPU RF Transmission Power vs. CPU Power Ratio

32. Energy Savings Using distributed DCT compression Bandwidth Savings: 80-90% Energy Savings: 36% for 4- hop network Energy Saving Using Compression (%) Number of Hops12345 Interpolated data-50.020.043.355.062.0 Real data-68.81.424.736.443.4