改进人工免疫算法优化的DV-Hop节点定位算法

由于无线传感器网络连通性不合理，导致计算待测节点与已知节点间距离时存在误差。为此，提出一种改进的人工免疫算法(AIA)优化DV-Hop未知节点坐标。首先对原平均跳距加权，其次利用网络中信标节点间距离产生的偏差构造跳距校正值得到最终的全网平均跳距。最后在计算待测节点坐标时引入AIA，针对AIA易陷入局部最优以及收敛速度过慢的问题，在局部搜索过程中采用高斯变异方法对AIA进行改进，扩大搜索范围，得到优化的待测节点坐标。经Matlab仿真证明，与原DV-Hop算法相比，改进后的算法在节点总数、信标节点比例以及通信半径三方面平均定位误差降低了近15%左右，具有较高的定位精度和较好的定位稳定性，同时也改善了算法的收敛性。

DV-Hop node location algorithm optimized by improved artificial immune algorithms

The unreasonable connectivity of the Wireless Sensor Network(WSN) would cause an error in calculating the distance between the node to be tested and the known node. An improved Artificial Immune Algorithm(AIA) is proposed to optimize DV-Hop unknown node coordinates. Firstly, the original average hop distance is weighted, and then the deviation value generated by the distance between the beacon nodes in the network is utilized to construct the hop distance correction value to obtain the final average network hop distance. Finally, AIA is introduced to the calculation of the coordinates of the nodes to be tested. Because the AIA is easy to fall into local optimum and the convergence speed is too slow, the Gaussian variation method is adopted to improve the AIA in the local search process, and the scope of search is expanded to get optimized node coordinates to be tested. The Matlab simulation proves that compared with the original DV-Hop algorithm, the average positioning error of the improved algorithm in the total number of nodes, the proportion of beacon nodes and the communication radius is reduced by about 15%. The improved algorithm has higher positioning accuracy, better stability and convergence.