Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
SENS: A Sensor, Environment and Network Simulator
ANSS '04 Proceedings of the 37th annual symposium on Simulation
Deploying a Wireless Sensor Network on an Active Volcano
IEEE Internet Computing
First experiences using wireless sensor networks for noise pollution monitoring
Proceedings of the workshop on Real-world wireless sensor networks
Subspace pursuit for compressive sensing signal reconstruction
IEEE Transactions on Information Theory
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
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Environmental monitoring aims to describe the state of the environment. It identifies environmental issues to show us how well our environmental objectives are being met. Traditional large-scale sensor networks for environmental monitoring suffers from the problems of high level of resources consumption and complex information management. In this report, we propose a novel environmental monitoring technique, called compressive sensing based monitoring, which employs only a small number of sensors to monitor target environmental signals over a region of interest. The compressive sensing technique is applied to implement our signal construction framework such that a high resolution environmental signal can be accurately reconstructed with under-sampling measurements.