Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Distributed sparse random projections for refinable approximation
Proceedings of the 6th international conference on Information processing in sensor networks
Receiver-oriented load-balancing and reliable routing in wireless sensor networks
Wireless Communications & Mobile Computing - Distributed Systems of Sensors and Actuators
Spatially-Localized Compressed Sensing and Routing in Multi-hop Sensor Networks
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Very low bit-rate video coding based on matching pursuits
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Information Theory
BER reduction in signal receivers towards greening of digital communications
International Journal of Communication Networks and Distributed Systems
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Compressed sensing (CS), as a novel theory based on the fact that certain signals can be recovered from a relatively small number of non-adaptive linear projections, is attracting ever-increasing interests in the areas of wireless sensor networks. However, the applications of traditional CS in such settings are limited by the huge transport cost caused by dense measurement. To solve this problem, we propose several ameliorated random routing methods executed with sparse measurement based CS for efficient data gathering corresponding to different networking topologies in typical wireless sensor networking environment, and analyse the relevant performances comparing with those of the existing data gathering schemes, obtaining the conclusion that the proposed schemes are effective in signal reconstruction and efficient in reducing energy consumption cost by routing. Our proposed schemes are also available in heterogeneous networks, for the data to be dealt with in CS are not necessarily homogeneous.