Compressive Sensing (CS) is a novel approach for data representation, which can represent signals at a rate below the Nyquist rate with low computation costs on encoder. For these characteristics, CS is very suitable for low power sensor nodes to save power consumption that is a primary problem inWireless Sensor Networks (WSN). But there are many problems when using CS in a real environment. One of these is that pattern of sensor values change dynamically. It decreases the efficiency of power consumption and accuracy of recovery. To solve the problem, we propose Pattern-based Matrix-size Optimization Algorithm (PMOA), which aims to improve the accuracy of exact recovery and power consumption.