Pattern-based Matrix-size Optimization Algorithm for Compressive Sensing in Real-world Body Sensor Networks

概要

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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 in Wireless Sensor Networks (WSN). But there are many problems when using CS in a real environment, especially in Body Sensor Network which aims to monitor human health and detect context. We attack the dynamics of sensor data problem that decrease 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 consump- tion. We performed experiments both in real world and simulation and the result show our approach is effective in energy consumption and reliable. The result shows our approach can achieve the improvement of battery life- time by 11.7%.
2013-07-10