TY - GEN
T1 - Netcompress
T2 - 2010 IEEE Workshop on Signal Processing Systems, SiPS 2010
AU - Nguyen, Nam
AU - Jones, Douglas L.
AU - Krishnamurthy, Sudha
PY - 2010
Y1 - 2010
N2 - Measurements from sensor networks consisting of thousands of nodes are often correlated, since nearby sensors observe the same phenomenon. Using Compressed Sensing, that data can be reconstructed with a high probability from a small collection of random linear combinations of those measurements. This opens a new approach to simultaneously extract, transmit and distribute information in wireless sensor networks. Efficient communication schemes well matched to compressive sensing are, nonetheless, needed to realize the full benefits of this approach. We present a simple, practical scheme, called NetCompress, using a novel form of Network Coding. It preserves the reconstruction conditions required for Compressed Sensing and also overcomes the high link-failure rate in wireless sensor networks. NetCompress simultaneously transmits packets of sensor measurements and encodes them to form random projections for Compressed Sensing recovery. A recent result in Compressed Sensing guarantees that the data at all nodes can be accurately recovered with a high probability from a small number of projections, which is much less than the total number of nodes in the network. NetCompress demonstrates this result on both the TOSSIM simulation platform and a testbed comprising 20 micaz and tmote sensor nodes. Our experimental results show that the number of packets that is needed to reconstruct light intensity measurements with reasonable quality is just half the number of nodes in the network.
AB - Measurements from sensor networks consisting of thousands of nodes are often correlated, since nearby sensors observe the same phenomenon. Using Compressed Sensing, that data can be reconstructed with a high probability from a small collection of random linear combinations of those measurements. This opens a new approach to simultaneously extract, transmit and distribute information in wireless sensor networks. Efficient communication schemes well matched to compressive sensing are, nonetheless, needed to realize the full benefits of this approach. We present a simple, practical scheme, called NetCompress, using a novel form of Network Coding. It preserves the reconstruction conditions required for Compressed Sensing and also overcomes the high link-failure rate in wireless sensor networks. NetCompress simultaneously transmits packets of sensor measurements and encodes them to form random projections for Compressed Sensing recovery. A recent result in Compressed Sensing guarantees that the data at all nodes can be accurately recovered with a high probability from a small number of projections, which is much less than the total number of nodes in the network. NetCompress demonstrates this result on both the TOSSIM simulation platform and a testbed comprising 20 micaz and tmote sensor nodes. Our experimental results show that the number of packets that is needed to reconstruct light intensity measurements with reasonable quality is just half the number of nodes in the network.
KW - Compressed sensing
KW - Network coding
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=78650339793&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650339793&partnerID=8YFLogxK
U2 - 10.1109/SIPS.2010.5624815
DO - 10.1109/SIPS.2010.5624815
M3 - Conference contribution
AN - SCOPUS:78650339793
SN - 9781424489336
T3 - IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
SP - 356
EP - 361
BT - 2010 IEEE Workshop on Signal Processing Systems, SiPS 2010 - Proceedings
Y2 - 6 October 2010 through 8 October 2010
ER -