Power-efficient dimensionality reduction for distributed channel-aware kalman tracking using WSNs
IEEE Transactions on Signal Processing
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Data acquisition through joint compressive sensing and principal component analysis
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
An application of compressive sensing for image fusion
Proceedings of the ACM International Conference on Image and Video Retrieval
A little feedback can simplify sensor network cooperation
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
Information theoretic bounds for compressed sensing
IEEE Transactions on Information Theory
Compression in wireless sensor networks: A survey and comparative evaluation
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 754.90 |
Power and bandwidth are scarce resources in dense wireless sensor networks and it is widely recognized that joint optimization of the operations of sensing, processing and communication can result in significant savings in the use of network resources. In this paper, a distributed joint source-channel communication architecture is proposed for energy-efficient estimation of sensor field data at a distant destination and the corresponding relationships between power, distortion, and latency are analyzed as a function of number of sensor nodes. The approach is applicable to a broad class of sensed signal fields and is based on distributed computation of appropriately chosen projections of sensor data at the destination - phase-coherent transmissions from the sensor nodes enable exploitation of the distributed beamforming gain for energy efficiency. Random projections are used when little or no prior knowledge is available about the signal field. Distinct features of the proposed scheme include: (1) processing and communication are combined into one distributed projection operation; (2) it virtually eliminates the need for in-network processing and communication; (3) given sufficient prior knowledge about the sensed data, consistent estimation is possible with increasing sensor density even with vanishing total network power; and (4) consistent signal estimation is possible with power and latency requirements growing at most sublinearly with the number of sensor nodes even when little or no prior knowledge about the sensed data is assumed at the sensor nodes.