On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Atomic Decomposition by Basis Pursuit
SIAM Review
Prediction-based monitoring in sensor networks: taking lessons from MPEG
ACM SIGCOMM Computer Communication Review - Special issue on wireless extensions to the internet
Modeling spatially correlated data in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Time synchronization methods for wireless sensor networks: A survey
Programming and Computing Software
Consistency of Trace Norm Minimization
The Journal of Machine Learning Research
Energy conservation in wireless sensor networks: A survey
Ad Hoc Networks
Probing the Pareto Frontier for Basis Pursuit Solutions
SIAM Journal on Scientific Computing
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
SIAM Journal on Matrix Analysis and Applications
A Matrix Completion Approach to Reduce Energy Consumption in Wireless Sensor Networks
DCC '10 Proceedings of the 2010 Data Compression Conference
The power of convex relaxation: near-optimal matrix completion
IEEE Transactions on Information Theory
Matrix completion from a few entries
IEEE Transactions on Information Theory
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
SIAM Journal on Imaging Sciences
COLT'05 Proceedings of the 18th annual conference on Learning Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
Primate-Inspired Communication Methods for Mobile and Static Sensors and RFID Tags
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Energy consumption monitoring for sensor nodes in SNAP
International Journal of Sensor Networks
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The energy cost of a sensor network is dominated by the data acquisition and communication cost of individual sensors. At each sampling instant it is unnecessary to sample and communicate the data at all sensors since the data is highly redundant. We find that, if only (random) subset of the sensors acquires and transmits the sample values, it is possible to estimate the sample values at all the sensors under certain realistic assumptions. Since only a subset of all the sensors is active at each sampling instant, the energy cost of the network is reduced over time. When the sensor nodes are assumed to lie on a regular rectangular grid, the problem can be recast as a low-rank matrix completion problem. Current theoretical work on matrix completion relies on purely random sampling strategies and convex estimation algorithms. In this work, we will empirically show that better reconstruction results are obtained when more sophisticated sampling schemes are used followed by non-convex matrix completion algorithms. We find that the proposed approach gives surprisingly good results.