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Adaptive Data Aggregation for Wireless Sensor Networks

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 65409. sarangap@mst.edu.

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Adaptive Data Aggregation for Wireless Sensor Networks

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  1. 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 65409. sarangap@mst.edu Research performed by Priya Kasirajan is thankfully acknowledged

  2. Agenda • Introduction • Background • Challenges • Proposed Methodology • Results and Discussion • Hardware results • Conclusionsand Future work

  3. Introduction • 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

  4. Background • 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

  5. 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

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

  7. 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.

  8. 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.

  9. Simulation Results • River Discharge Data • Audio Data • Geophysical Data Energy Consumption • FLoating point Operations Per Second – FLOPS • NADPCMC encoding • 7050 FLOPS • 1.224 micro joules • NADPCMC decoding • 7425 FLOPS • 1.289 micro joules • XBee radio – transmit power – 1 mW for 30 m

  10. River Discharge Data Time Time

  11. River Discharge Data (contd.)

  12. Audio Data Time Time

  13. Audio Data (contd.)

  14. Geophysical Data Performance Time Time

  15. 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 - 6.10% • 4 bit NADPCMC at CH 5 – 74.54% savings – • Synthetic data: 7.01% • River discharge data: 4.83% • Audio data: 6.09%

  16. Hardware Implementation • Compression ratio – 1.846 • Energy savings – 45.83% • Distortion – 1.67% • Compression ratio – 2.526 • Energy savings – 60.42% • Distortion – 4.60%

  17. Nano Sensor Data Performance

  18. 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

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