TY - GEN
T1 - Compressive wireless sensing
AU - Bajwa, Waheed
AU - Haupt, Jarvis
AU - Sayeed, Akbar
AU - Nowak, Robert
PY - 2006/12/1
Y1 - 2006/12/1
N2 - Compressive Sampling is an emerging theory that is based on the fact that a relatively small number of random projections of a signal can contain most of its salient information. In this paper, we introduce the concept of Compressive Wireless Sensing for sensor networks in which a fusion center retrieves signal field information from an ensemble of spatially distributed sensor nodes. Energy and bandwidth are scarce resources in sensor networks and the relevant metrics of interest in our context are 1) the latency involved in information retrieval; and 2) the associated power-distortion trade-off. It is generally recognized that given sufficient prior knowledge about the sensed data (e.g., statistical characterization, homogeneity etc.), there exist schemes that have very favorable power-distortion-latency trade-offs. We propose a distributed matched source-channel communication scheme, based in part on recent results in compressive sampling theory, for estimation of sensed data at the fusion center and analyze, as a function of number of sensor nodes, the trade-offs between power, distortion and latency. Compressive wireless sensing is a universal scheme in the sense that it requires no prior knowledge about the sensed data. This universality, however, comes at the cost of optimality (in terms of a less favorable power-distortion-latency trade-off) and we quantify this cost relative to the case when sufficient prior information about the sensed data is assumed.
AB - Compressive Sampling is an emerging theory that is based on the fact that a relatively small number of random projections of a signal can contain most of its salient information. In this paper, we introduce the concept of Compressive Wireless Sensing for sensor networks in which a fusion center retrieves signal field information from an ensemble of spatially distributed sensor nodes. Energy and bandwidth are scarce resources in sensor networks and the relevant metrics of interest in our context are 1) the latency involved in information retrieval; and 2) the associated power-distortion trade-off. It is generally recognized that given sufficient prior knowledge about the sensed data (e.g., statistical characterization, homogeneity etc.), there exist schemes that have very favorable power-distortion-latency trade-offs. We propose a distributed matched source-channel communication scheme, based in part on recent results in compressive sampling theory, for estimation of sensed data at the fusion center and analyze, as a function of number of sensor nodes, the trade-offs between power, distortion and latency. Compressive wireless sensing is a universal scheme in the sense that it requires no prior knowledge about the sensed data. This universality, however, comes at the cost of optimality (in terms of a less favorable power-distortion-latency trade-off) and we quantify this cost relative to the case when sufficient prior information about the sensed data is assumed.
KW - Compressive sampling
KW - Uncoded communications
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=34247357879&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34247357879&partnerID=8YFLogxK
U2 - 10.1145/1127777.1127801
DO - 10.1145/1127777.1127801
M3 - Conference contribution
AN - SCOPUS:34247357879
SN - 1595933344
SN - 9781595933348
T3 - Proceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '06
SP - 134
EP - 142
BT - Proceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '06
T2 - Fifth International Conference on Information Processing in Sensor Networks, IPSN '06
Y2 - 19 April 2006 through 21 April 2006
ER -