Backcasting: adaptive sampling for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Universal distributed sensing via random projections
Proceedings of the 5th international conference on Information processing in sensor networks
Joint segmentation of the wind speed and direction
Signal Processing
Design considerations for solar energy harvesting wireless embedded systems
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Distributed sparse random projections for refinable approximation
Proceedings of the 6th international conference on Information processing in sensor networks
IEEE Transactions on Parallel and Distributed Systems
Power management in energy harvesting sensor networks
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient gathering of correlated data in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Design and evaluation of a hybrid sensor network for cane toad monitoring
ACM Transactions on Sensor Networks (TOSN)
Energy conservation in wireless sensor networks: A survey
Ad Hoc Networks
FloodNet: coupling adaptive sampling with energy aware routing in a flood warning system
Journal of Computer Science and Technology
IEEE Transactions on Signal Processing
Emerging techniques for long lived wireless sensor networks
IEEE Communications Magazine
Ear-phone: an end-to-end participatory urban noise mapping system
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
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Due to non-homogeneous spread of sunlight, sensing nodes typically have non-uniform energy profiles in rechargeable Wireless Sensor Networks (WSNs). An energy-aware work load distribution is therefore necessary for good data accuracy while ensuring an energy-neutral operation. Recently proposed signal approximation strategies, in form of Compressive Sensing, assume uniform sampling and thus cannot be deployed to facilitate energy neutral operation in rechargeable WSNs. We propose a sparse approximation driven sensing technique (EAST) that adapts sensor node sampling workload according to solar energy availability. To the best of our knowledge, we are the first to propose sparse approximation for modeling energy-aware work load distribution in order to improve signal approximation from rechargeable WSNs. Experimental result, by using data from an outdoor WSN deployment, suggests that EAST significantly improves the approximation accuracy while supporting approximately 50% higher sensor on-time compared to an approach that assumes uniform energy profile of the nodes.