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Enhancing Data Collection Efficiency in Wireless Sensor Networks via Compressed Sensing

This outline discusses the potential of compressed sensing to improve energy efficiency in data collection for wireless sensor networks. Compressed sensing allows networks to recover signals using far fewer samples than traditional methods, reducing traffic load at sink nodes. The outline covers compressed sensing techniques, network models, and future work suggestions for further exploration. Key points include node deployment strategies, reconstruction algorithms, and factors influencing data collection efficiency.

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Enhancing Data Collection Efficiency in Wireless Sensor Networks via Compressed Sensing

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  1. Compressed sensing in data collection Yiying Zhao

  2. Outline • Introduction • Compressed sensing • Network model • Future work

  3. 1. Introduction • Energy efficiency of data collection is one of the dominating issues of wireless sensor networks(WSNs), especially when considering data collection. Because data collection will cause serious traffic load at the sink node. • The newly developed technique, compressed sensing, provides another method that allowing network recover the signal with high probability from far fewer samples than original dimension.

  4. 2. Compressed sensing

  5. 2. Compressed sensing • When reconstruct signal from v, we solve the following problem • There are many methods to solve this problem, such as basis pursuit algorithm and matching pursuit algorithm.

  6. 3. Network model • In this model, I deploy node intentionally, which means I already know the connection among these nodes. I also neglect the transmission loss. It extremely simplifies the whole model. • The model contains ones sink node to collect data and other sensor nodes to gather data.

  7. 3. Network model • Step 1 • Divide the nodes set (apart from the sink node) into two sets, A and B. the sum of those nodes are n. • A set contains nodes which receive data less than k-1. • B set contains nodes receiving data more than k.

  8. 3.Network model

  9. 3. Network model

  10. 3. Network model

  11. 3. Network model • Step 3 • Reconstruction

  12. 3. Network model • In reconstruction part, we are about to recover the data transmit from H1, H2, H3. • In the part, we choose the matching pursuit algorithm.

  13. 3. Network model

  14. 4. Future work • 1. do some simulation • 2. take more factors into consideration

  15. Thank you

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