Optimal sensor scheduling in nonlinear filtering of diffusion processes
SIAM Journal on Control and Optimization
Measurement scheduling for recursive team estimation
Journal of Optimization Theory and Applications
Approximate stochastic dynamic programming for sensor scheduling to track multiple targets
Digital Signal Processing
Compressive data gathering for large-scale wireless sensor networks
Proceedings of the 15th annual international conference on Mobile computing and networking
Suelo: human-assisted sensing for exploratory soil monitoring studies
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
A fast approach for overcomplete sparse decomposition based on smoothed l0 norm
IEEE Transactions on Signal Processing
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
Algorithms for optimal scheduling and management of hidden Markovmodel sensors
IEEE Transactions on Signal Processing
Decoding by linear programming
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
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
IEEE Communications Magazine
Efficient background subtraction for real-time tracking in embedded camera networks
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Just send me the summary!: analyzing sensor data for accurate summary reports in indoor environments
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
SVD-based hierarchical data gathering for environmental monitoring
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Real-time classification via sparse representation in acoustic sensor networks
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
| Hi-index | 0.00 |
We consider the problem of monitoring soil moisture evolution using a wireless network of in-situ underground sensors. To reduce cost and prolong lifetime, it is highly desirable to rely on fewer measurements and estimate with higher accuracy the original signal (soil moisture temporal evolution). In this paper we explore results from the compressive sensing (CS) literature and examine their applicability to this problem. Our main challenge lies in the selection of two matrices, the measurement matrix and a representation basis. The physical constraints of our problem make it highly non-trivial to select these matrices, so that the latter can sufficient sparsify the underlying signal while at the same time be sufficiently incoherent with the former, two common pre-conditions for CS techniques to work well. We construct a representation basis by exploiting unique features of soil moisture evolution. We show that this basis attains very good tradeoff between its ability to sparsify the signal and its incoherence with measurement matrices that are consistent with our physical constraints. Extensive numerical evaluation is performed on both real, high-resolution soil moisture data and simulated data, and through comparison with a closed-loop scheduling approach. Our results demonstrate that our approach is extremely effective in reconstructing the soil moisture process with high accuracy and low sampling rate.