Energy Efficient Wireless Sensor Networks using Asymmetric Distributed Source Coding

1. Energy Efficient Wireless Sensor Networks using Asymmetric Distributed Source Coding

2. OBJECTIVE • Model WSN • Use Distributed Source Coding - Remove spatial redundancy • Reduce bandwidth, energy consumption

3. MOTIVATION • The MIT Technology Review - number one emerging technology • set of unique constraints and  requirements.  • Limited power – runs on battery

4. BACKGROUND DSC

5. Background • Liveris - LDPC, near Slepian limit • DSC with WSN • Suggested - Xiong • Applied for analog data • – Chou et al • “Tracking and exploiting correlations in dense sensor networks,”

6. Our Solution • For binary Data • Use DSC for source coding • Use asymmetric chanel codes • move the complexity receiver • Govinda et al [1] • Simulation

7. Simulation Approach • Model Sources • spatial correlation • BSC between sources • Model WSN using Simulink • Apply DSC using Matlab • We have chosen BPSK  as the modulation with RS(331,21) –[1] • Decode Jointly

8. DSC  Estimating Y with the knowledge of X Algorithm • Create random binary X, create Y from passing X through BSC. • Encode X, Y with [n,k] code. • Send encoded X (n bits)and syndrome of Y (n-k) bits • Decode X • Decoded Y = decode(X+ syndrome(Y)) • Calculate BER of Y

9. Mathematical Modelling Method 1: Without DSC Method 2: With DSC

10. Energy Calculation Energy saving ratio proportional to the no of bits saved

11. Simulink Diagram

12. IMPLEMENTATION • Wireless sensor network • Nodes connected in star topology • AWGN channel with fading  • Parameters varied are • types of coding • n • k • Variance between nodes

13. Performance vs Correlation distributed source coding is useful only if the Correlation between X and Y is at least 0.6

14. Performance vs EbNo and Correlation

15. Conclusion • Method for reducing Bandwidth requirement explored • BER performance compare • Theoretical and simulation results obtained • Result case single node • Correlation of 95% • save 25% energy • increase in BER of 10-3 • Theoretical value for multiple node presented

16. Future Work • More realistic simulations using simulation softwares like NS2 or Qualnet. • Make Energy measurements and compare different coding techniques • Improve distributed source coding by trying different coding techniques • Add different fading channels like Nakagami

17. REFERENCES • Slepian, D. and Wolf, J.K., “Noiseless coding of correlated information sources”. IEEE Trans.Inform.Theory, vol. IT-19, pp.471-480, July (1973). • Wyner, A. and Ziv, J., “The rate-distortion function for source coding with side information at the decoder,” IEEE Trans. Inform. Theory, vol. 22, pp. 1-10, January (1976) • Pradhan, S. and Ramchandran, K. “Distributed source coding using syndromes (DISCUS): Design and construction,” IEEE Trans. Inform. Theory, vol. 49, pp. 626–643, Mar. (2003). • Kamath, G., Shekar, Y. Kini G. A., Sripati U., Kulkarni, M. –“Performance Analysis of Energy Efficient Asymmetric Coding/Modulation Schemes for Wireless Sensor Networks” - Wireless Pervasive Computing (ISWPC), 5th IEEE International Symposium on, pp 460 – 464 (2010).

18. REFERENCES • Chou, J., Petrovic, D., and Ramchandran, K., “Tracking and exploiting correlations in dense sensor networks,” Proceedings of the Asilomar Conference on Signals, Systems and Computers, November (2002). • Liveris, A.D.,.Xiong, Z., “Compression of Binary Source with side information at the Decoder using LDPC“ IEEE Communications Letters Vol 6,No.10 Oct (2002) • Gracia-Frias, J., and Zhao, Y., “Compression of correlated binary sources using turbo codes”. IEEE commum.Lett., vol. 6, pp.379-381, (2002). • Liveris, A.D., Xiong, Z., and Georghiades, C.N., “A distributed source coding technique for correlated images using turbo codes”. IEEE Commum. Lett., vol. 6,pp.379-781 (2002).