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Energy-Efficient Data Gathering in Wireless Sensor Networks with Asynchronous Sampling

Energy-Efficient Data Gathering in Wireless Sensor Networks with Asynchronous Sampling. JING WANG, YONGHE LIU, and SAJAL K. DAS Presented By: Ashirth Pai. Introduction. Asynchronous Sampling. Lossless Data Gathering. Strategies involved. Asynchronous Sampling Strategy.

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Energy-Efficient Data Gathering in Wireless Sensor Networks with Asynchronous Sampling

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  1. Energy-Efficient Data Gathering inWireless Sensor Networks withAsynchronous Sampling JING WANG, YONGHE LIU, and SAJAL K. DAS Presented By: AshirthPai

  2. Introduction

  3. Asynchronous Sampling

  4. Lossless Data Gathering

  5. Strategies involved

  6. Asynchronous Sampling Strategy

  7. Finite Impulse Response low-pass filter is used to filter the asynchronous samples. • Digital low-pass filter is not ideal.

  8. Collaborative Reconstruction

  9. Signal Construction from Irregular Samples

  10. Lossy Data Gathering • Objective is to use asynchronous sampling to improve the quality of the sensory data in terms of increasing the entropy. • Temporal-Spatial Correlation Models • Exploits redundancy in sensory data there by increasing entropy. • Exponential correlation model: • Assumes that the data gathered contains true values and noise. • Spatial correlation exists due to the spread of nodes in physical space. • Temporal correlation denotes the correlation between the data sampled at different time instances. • Combining both spatial and temporal correlation, we define the correlation between nodes I and j as:

  11. Example of correlation: Stochastic process whose covariance is an exponential model

  12. Covariance decreases with distances between sensory nodes. It was also observed that the covariance coefficient of temperatures from different sensors decrease with time.

  13. Results of the experiment concluded that spatial correlation of data was irregularly dispersed but the temporal correlation followed the exponential model discussed. Hence asynchronous sampling could be applied to reduce correlation among sensory data.

  14. Benefits of Asynchronous Sampling

  15. Designing Asynchronous Sampling Strategies

  16. Designing Asynchronous Sampling Strategies

  17. Simulation Experiments

  18. It was observed that the high frequency part of the signals were attenuated in terms of power and frequency when compared without asynchronous sampling.

  19. After reconstruction of the signals from the asynchronous samples,

  20. Experimental Observations

  21. Comparison of regression performance Clearly, asynchronous sampling considerably reduces regression distortion.

  22. Related Work

  23. Conclusion

  24. Questions

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