Experimental results of compressive sensing on wireless multimedia sensor networks.

Compressed Sensing (CS) was presented as a means to achieve overcome the challenges faced by Wireless Multimedia Sensor Networks. A sensing matrix is crucial to the compressed sensing framework. The sensing matrix can maintain the fidelity of a compressed signal, diminish the sampling rate obligation and improve the strength and performance of the recovery algorithm. A great number of measurement matrices have been proposed to either offer reduced computational complexity or good recovery performance, but only some have managed to accomplish both, and even fewer have been proven in a compelling manner. Experiments were conducted to evaluate the fidelity and energy efficiency of different compressive sensing matrices for applications on Wireless Multimedia Sensor Networks.

压缩感知（Compressed Sensing，简称CS）作为一种克服无线多媒体传感器网络所面临挑战的手段而被提出。感测矩阵在压缩感知框架中扮演着至关重要的角色。感测矩阵能够维持压缩信号的保真度，降低采样率的需求，并提升恢复算法的强度与性能。众多测量矩阵被提出，旨在提供降低计算复杂度或良好的恢复性能，然而，仅有少数矩阵能够实现两者兼顾，而能够以令人信服的方式证明其效果者更是寥寥无几。为了评估不同压缩感知矩阵在无线多媒体传感器网络应用中的保真度和能量效率，进行了相应的实验研究。