传感器网络中基于多维标度定位算法的改进

Improvement for MDS-based Localization Algorithms in Wireless Sensor Networks

针对基于经典多维标度的MDS-MAP算法在定位精度方面的不足,为提高传感器定位精度,提出一种基于Euclidean算法的改进型多维标度定位算法(Euclidean-based MDS-MAP(P,C))。算法与经典多维标度算法的区别在于,Euclidean算法能够算出每个节点与其两跳邻居节点间的欧氏距离,然后用这个欧氏距离来进行多维标度,显然能提高精度。仿真实验表明基于Euclidean算法的改进型多维标度算法与经典多维标度算法相比具有很低的定位误差以及很高的定位精度。

Considering that the MDS - MAP algorithm based on classic multidimensional scaling has shortages on localization precision, a type of modified algorithm is proposed based on Euclidean multidimensional scaling algorithm （ Euclidean - based MDS - MAP（ P, C ） ）. The difference between the new algorithm and the classical multidimensional scaling algorithm is that, the new algorithm uses Euclidean algorithm to calculate each node with its two - hop neighbor nodes between the Euclidean distance, and then use Euclidean distance to carry out multi - dimensional scaling, which can improve obviously the accuracy. The simulation results show that the improved algorithm based on the Euclidean muhi - dimensional scaling algorithm with the classic multi - dimensional scaling algorithm is very low compared with the positioning error and the positioning accuracy.