基于刚分簇与鸡群优化的深井无线传感网络定位算法

针对矿井环境因素对无线传感器网络定位的制约，提出一种基于刚性分簇与鸡群优化的无线传感器网络定位算法(RCCSO). 首先，以传感网络中均匀分布的锚点为簇头，基于刚性图理论提出分簇算法对整个网络进行分簇并保证每个簇都是全局刚性的；其次，利用鸡群算法对簇内进行相对定位，求得簇内最优相对位置解集；再次，不同簇以锚点为旋转中心旋转不同角度，并利用鸡群算法求出旋转角度的最优解集，进而求得全局节点最优位置；最后，仿真结果显示，与多维标度MDS-MAP算法及自适应局部区域循环搜索DALSA相比，所提算法在精度上有较明显的提高. 

Localization Algorithm for Mine Wireless Sensor Network Based on Rigid Cluster and Chicken Swarm Optimization

In order to adapt to the restrain resulting in environment factor of mine to localization of wireless sensor network, a novel localization algorithm based on rigid cluster and chicken swarm optimization (RCCSO) is proposed. First, the clusters are set up centring on the uniformly distributed anchor nodes, expand the clusters based on rigid theory and get several clusters which are all globally rigid. Second of it, optimize the best relative position of the nodes in the same cluster by chicken swarm optimization, and get the solution sets of relative position. Then, centring on the anchor nodes, all the solution ratate for some different angle, and optimal the best solution set of ratation angles by chicken swarm optimization, and get the globally position of all the unknown nodes. Finally, Simulation comparison demonstrated that the accuracy of the new localization algorithm RCCSO is more precise than the MDS-MAP algorithm and DALSA algorithm.