基于CS的机载分簇型WSN数据采集方法

CS-based data collection method for airborne clustering WSN

提出一种适用机载分簇型WSN的数据采集方案。该方案一方面采用随机压缩采样的方式,有效地减少了硬件资源受限的簇成员节点的采样数据量,降低了簇成员节点对硬件资源的要求;另一方面,提出一种基于复合混沌—遗传算法的MP重构方法,将混沌理论良好的局部寻优特性与遗传算法强大的全局搜索能力相结合,有效提高了簇头或Sink中信号重构的性能。实验结果表明,该方案在有效减少簇成员节点采样数据量,且采样频率降为原采样频率1/8的基础上,仍能保证10-7数量级的重构精度,为实用型WSN的实现提供了有效借鉴。

A data acquisition scheme which was suitable for airborne clustering WSN was proposed. On the one hand, this scheme adopts the random compressive sampling could reduce the amount of sampling data of the cluster nodes ef- fectively, and greatly reducing the hardware requirements of the cluster nodes; on the other hand, put forward a MP re- construction method based on composite chaotic-genetic algorithm expressly, which combined the excellent local searching characteristics of chaos theory with the powerful global search ability of genetic algorithm, could improve the signal reconstruction performance of the cluster head or Sink effectively. The experimental results show that, by dimin- ishing the sampling frequency to 1/8 of the original sampling frequency, this random compressive sensing scheme can dramatically reduce the sampling quantity, and the reconstruction precision can reach 10 7 magnitude. This random com- pressive sensing scheme provides a useful idea for practical WSN.