基于弹簧粒子模型的大规模WSN定位算法的衍生算法

Derivative Localization Algorithms Based on a Spring Particle Model(LASPM) for Large Scale Wireless Sensor Networks

为了提高基于弹簧粒子模型的大规模无线传感器网络定位算法(LASPM定位算法)的鲁棒性,将对LASPM基本定位算法进行优化及改进,并提出一系列的改进衍生算法。针对弱节点将设计简单的迭代定位方法,提出了3个补丁算法,分别用于处理局部极值、剔除坏节点和处理节点动态变化等问题。仿真实验结果表明,新算法的节点计算复杂度、通信复杂度在网络规模增大时仍然保持常量,节点计算步数不随网络规模变化而变化,时间复杂度也保持常量。实验研究结果表明,本文的定位算法具有良好的鲁棒性。

To improve the robustness of localization algorithm based on a spring particle model（ LASPM） for largescale wireless sensor networks,some derivative algorithms based on basic LASPM algorithm are proposed. A simpler iterative algorithm is proposed for weak particles. Three patches of the basic LASPM algorithm are proposed to avoid local optimization,kick out bad nodes and deal with node variation. Simulation results show that the computational and communication complexity are almost constant despite the increase of the scale of the network. The time consumption has also been proven to remain almost constant since the calculation steps unrelated to the scale of the network. The proposed localization algorithms are shown with sound robustness.