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, CS）最初被提出，作为解决无线多媒体传感器网络（Wireless Multimedia Sensor Networks）所面临各类挑战的有效手段。感知矩阵是压缩感知框架的核心组件，其能够维持压缩信号的保真度、降低采样率需求，并提升恢复算法的稳定性与整体性能。目前已有大量测量矩阵被提出，此类矩阵要么可降低计算复杂度，要么具备优异的信号恢复性能，但仅有少数能够同时兼顾这两项优势，而能以极具说服力的方式验证其有效性的则更为稀少。本研究开展了相关实验，针对无线多媒体传感器网络的应用场景，对不同压缩感知矩阵的保真度与能效表现进行了评估。