基于帝国主义竞争算法的WSNs定位方案

遗传算法(GA)在无线传感器网络(WSNs)定位时存在收敛速度慢、精度低等弊端,针对以上问题,提出了一种利用帝国主义竞争算法(ICA)优化WSNs定位的方案。首先,使用了采样的方法来估计未知节点的初始位置;其次,依靠信标节点和相邻节点的相关信息建立了以最小化全局误差的三维空间的数学定位模型;最后,使用了最新的社会启发算法—ICA来进行定位优化。实验结果表明:与GA定位相比,ICA在WSNs定位上具有定位精度高、收敛迅速的优势。

WSNs localization scheme based on imperialist competitive algorithm

Aiming at shortcomings such as slow convergence rate and low precision of genetic algorithm（GA） for wireless sensor networks （WSNs） positioning, present a scheme using imperialist competitive algorithm （ICA） to optimize WSNs localization. Firstly, method of sampling is used to estimate initial position of unknown node; Secondly,relevant information of beacon node and adjacent node is rehed to build 3D space mathematical localization model which based on the minimum global error as the objective function ; Finally, ICA, the latest social heuristic algorithm, is used to optimize positioning. Experimental results show that, compared with the GA, the ICA algorithm has advantages of high positioning precision, and fast convergence speed in WSNs positioning.