Compressed sensing in data collection
Yiying Zhao
Outline
1. Introduction
2. Compressed sensing
3. Network model
4. Future work
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.
2. Compressed sensing
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.
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.
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.
3.Network model
3. Network model
3. Network model
3. Network model
Step 3
Reconstruction
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.
3. Network model
4. Future work
1. do some simulation
2. take more factors into consideration
Thank you