中文摘要：

基于连通支配集（Connected dominating set,CDS）的区域覆盖算法大都采用休眠节点数量的最大化机制来实现节能,这将给无线传感器网络中的活动节点带来沉重的负担。活动节点电能的迅速耗尽将导致CDS失效,产生覆盖盲区。不断激活其他休眠节点,会出现频繁的网络拓扑变化,导致网络收敛性出现问题。提出了一种基于学习自动机的WSN区域覆盖算法。采用受度限制的连通支配集d-CDS来构造WSN骨干网络,利用学习自动机选择当前节点的最优邻居节点,以此实现对所构造CDS的优化,实现活动节点的负载均衡,改善区域覆盖性能。通过仿真实验对比Gossip、ST-MSN和TMPO等算法,表明本文提出的算法在网络覆盖比率、活动节点的剩余电量等方面均存在优势。

英文摘要：

In the most existing connected dominating set（CDS）based coverage algorithms,the mechanisms of maximizing sleep node numbers is adopted to save energy in WSN.Active nodes of WSN cause rapidly exhausts energy which leads to CDS failure and fade coverage.In addition,frequently activating other nodes speeds up network topology changes,and lead to the network convergence problems.A learning automata-based area coverage algorithm is proposed for WSN.d-CDS is adopted to construct network topology,and learning automata is used to select the optimal node of current sensors.Then the constructed CDS can be optimized,and the load balance of the active nodes is realized to improve network coverage performance.Finally,Simulation experiments are conducted to compare Gossip,ST-MSN and TMPO.The results show that,the proposed algorithm is superior to the three algorithms in network coverage rate and the residual energy of active nodes.