中文摘要：

无线传感器节点位置定位正确与否对整个网络传感器起着至关重要的作用．当无线传感器网络暴露在恶意危险环境中时，攻击者会攻击节点定位的过程，使其定位到错误位置，从而导致整个网络应用完全失效．基于最大似然估计的传感器定位概率模型是一种常用的定位模型，但是它有两个缺点：（1）为了降低计算复杂性，通常将RSS（接收信号强度）信号标准差看成常数，影响定位精度；（2）安全性差，在有恶意节点攻击时模型常常会定位失效．文中首先通过拟合测试数据归纳出了RSS信号标准差随距离变化的函数关系，克服了第一个缺点．针对第二个问题，在分析其受攻击时定位失败的具体原因后，对节点定位的概率计算公式进行了改进，设计了一种新的基于变方差特征的传感器节点定位概率模型．该模型属于高度非线性全局优化问题．针对其难以求解的特点，文中设计了一个新的有效的进化算法，并证明了该算法的全局收敛性．最后通过对公开数据集的测试和实际实验，验证了该模型和求解算法能在保证定位精度的前提下，完成节点的安全定位．

英文摘要：

It is crucial that wireless sensor nodes should be properly located to facilitate the oper- ation of the whole network. When wireless sensor network is exposed in malicious and dangerous environment, attackers may attack the nodes in the location process and cause incorrect location results which may lead to the complete breakdown of the entire network. The sensor location probability model based on maximum likelihood estimation is one of the commonly-used location models. However, it has two flaws. First, it usually treats the standard deviation of the received signal strength （RSS） as constant to lower the calculation complexity, which affects the accuracy of location. Second, it is not secure enough. Under malicious node attack, this model usually cannot fulfill its location function. This research generalizes the functional relations between RSS standard deviation and distance through fitting test data and thus fixes the first problem. To tackle the second problem, this research analyzes the reasons behind the failure of location under attack and thus improves the probability formula of node location, and designs a new sensor node location probability model based on the characteristics of variant variance. As this new model is a highly nonlinear characteristic global optimization problem that is difficult to work out, this research has designed a new and effective evolutionary algorithm and proven its global conver- gence. In tests using public datasets and actual experiments, the designed model and algorithm are finally proven to be able to fulfill secure location of the nodes on the premise that the accuracy of location is guaranteed.