Adaptive Data Aggregation for Wireless Sensor Networks S. Jagannathan Rutledge-Emerson Distinguished Professor Department of Electrical and Computer Engineering Professor of Computer Science Missouri University of Science and Technology Rolla, MO Research performed by Priya Kasirajan is thankfully acknowledged

Agenda Introduction Background Challenges Proposed Methodology Results and Discussion Hardware results Conclusions and Future work 2

Why compression? –Reduction in amount of data transmitted –Reduction in energy consumption –Improvement in network lifetime Compression vs Aggregation –Data condensed at the source node –Aggregation implies data from spatially separated sensors combined statistically using min, avg, max, count, sum –Need location or node ID  Node  Clusterhead Introduction 3

Background 4 Survey of data aggregation (Rajagopalan and Varshey, 2006) –Chain based data aggregation, tree based, PEDAP, Grid based, Network flow based, network correlated data aggregation, QoS-aware aggregation Quantization –Lossy compression scheme –Quantization error is proportional to step size –Step size is dependent on dynamic range Adaptive Differential Pulse Code Modulation (ADPCM) –Quantize difference between actual sample and estimated sample –Exploits the correlation between adjacent samples to reduce bit rate and to achieve compression. Real world sensor data with multiple modalities does not always boast correlation and linear relationship

Challenges Data compression/aggregation can be a complex nonlinear process Nonlinear processing is computationally more intensive Data reconstruction can be involved –Location aware or context aware –Node ID Performance guarantees in terms of distortion, compression ratio, energy efficiency, hard to show 5

Proposed Methodology Channel e(k) Some y(k) Quantizer Estimator Encoder Decoder Estimator

Analytical Results Theorem 1 (Estimator-Ideal Performance): In the ideal case with no reconstruction errors and noise present, the estimation error approaches to zero asymptotically while the parameter estimation error vector is bounded. Theorem 2 (Estimator Performance—General Case): Let the hypothesis presented in Theorem 1 hold and if the functional reconstruction error is bounded, then estimation error is bounded while the parameter errors are also bounded.

Analytical Results (contd.) Theorem 3 (NADPCMC Distortion): If the estimator reconstruction and quantization errors are considered bounded, then the distortion at the destination is bounded. On the other hand in the absence of estimator reconstruction and quantization errors, the distortion is zero. Theorem 4 (NADPCMC Performance): The compression ratio, defined as the ratio of the amount of uncompressed data to the amount of compressed data, is greater than one. Moreover, the proposed scheme will render energy savings. 8

Simulation Results River Discharge Data Audio Data Geophysical Data FLoating point Operations Per Second – FLOPS NADPCMC encoding 7050 FLOPS micro joules NADPCMC decoding 7425 FLOPS micro joules XBee radio – transmit power – 1 mW for 30 m Energy Consumption

River Discharge Data Time

River Discharge Data (contd.) 11 Method Compression ratio Energy savings at nodes Energy savings at CH DistortionOverhead Huffman1.453NA31.177%NA480 bytes Differential Huffman %39.099%NA480 bytes Scaling and approximation %11.65%0.0657%0 Scaling and 9 bit quantization % 0.943%0 Scaling and 8 bit quantization % %0 Scaling and5 bit quantization % %0 Linear ADPCM250% 13.72%0 NADPCMC with 8 bit encoding % 2.65%10 bytes NADPCMC with 6 bit encoding % 6.08%10 bytes

Audio Data Time

Audio Data (contd.) 13 MethodData rate Compression ratio Energy savings at nodes DistortionOverhead Scaling and 8 bit quantization 64kbps250%10.59%NA Scaling and 6 bit quantization 48kbps %46.28%NA 5 bit linear ADPCM40kbps %11.37%NA 4 bit linear ADPCM32kbps475%23.14%NA 3 bit linear ADPCM24kbps %28.45%NA 2 bit linear ADPCM16kbps887.5%35.86%NA NADPCMC with 8 bit encoding 64kbps %2.04%20 bytes NADPCMC with 6 bit encoding 48kbps %6.16%20 bytes NADPCMC with 4 bit encoding 32kbps %14.44%20 bytes

Geophysical Data Performance 14 Time Method Compression ratio Energy savings at nodes DistortionOverhead Scaling and 8 bit quantization 250%4.36%0 Scaling and 6 bit quantization %13.42%0 Linear ADPCM 250%35.87%0 NADPCMC with 8 bit encoding250%1.02%20 bytes NADPCMC with 6 bit encoding %4.22%20 bytes

Aggregation using NADPCMC 8 bit NADPCMC at all source nodes 6 bit NADPCMC at CH 1, 2 and 3 – 61.34% savings – 1.90% 4 bit NADPCMC at CH 1, 2 and 3 – 73.61% savings % 4 bit NADPCMC at CH 5 – 74.54% savings – –Synthetic data: 7.01% –River discharge data: 4.83% –Audio data: 6.09% 15

Hardware Implementation 16 Compression ratio – Energy savings – 45.83% Distortion – 1.67% Compression ratio – Energy savings – 60.42% Distortion – 4.60%

Nano Sensor Data Performance 17 Data generation rate Transmissio n rate Compress ion ratio Energy savings Distortion Uncompress ed data 2.56 kbps3.424 kbpsNA Compressed data 2.56 kbps1.712 kbps %0.78% for sensor data 0.81% for river discharge data Compressed and aggregated data – 6 bit NADPCMC 2.56 kbps1.284 kbps %3.58% for sensor data 2.78% for river discharge data Compressed and aggregated data – 4 bit NADPCMC 2.56 kbps856 bps %8.21% for sensor data 10.90% for river discharge data

Conclusions Data aggregation process is nonlinear and must be location/self-aware for enhanced performance NADPCMC addresses nonlinear issues in data and performs well for different sensor modalities. Aggregation is achieved through iterative compression. Performance depends on number of aggregation levels and Quantizer resolution. Network size does not impact performance. Future work involves evaluation of the proposed scheme for larger size networks with different types of data by considering latency, life time and security