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Compressive Sensing (CS), a new sampling paradigm, has recently found several applications in Wireless Sensor Networks (WSNs). In this paper, the authors investigate the design of novel sensing matrices which lead to good expected-case performance - a typical performance indicator in practice - rather than the conventional worst-case performance that is usually employed when assessing CS applications. In particular, they show that tight frames perform much better than the common CS Gaussian matrices in terms of the reconstruction average Mean Squared Error (MSE). They also showcase the benefits of tight frames in two WSN applications, which involve: robustness to data sample losses; and reduction of the communication cost.
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