Chaos-based compression sensing on wireless sensor network: enabling a low-power and high-performance system

Abstract

The energy of sensor nodes in operation mode is primarily consumed by the wireless transceivers. Therefore, reducing the transmitted data can lead to significant energy savings. Compressive sensing is a technique that can reproduce an original signal using a smaller number of samples than required by the Nyquist theorem, by exploiting the sparsity of the signal in the represented domain. In Wireless Sensor Networks, compressed sampling is performed at the sensor node, and decompression is performed at the sink node. However, the limited computing and resource constraints in sensor nodes should be taken into consideration when applying the compressed sensing technique. This paper proposes using a non-linear system to generate chaos-based coefficient sequences applied in the sensor nodes of a landslide warning system. The experimental study demonstrated that the sensor node utilizing pseudo-random sampling is faster and less complex in comparison to the sensor node employing random sampling.