【摘要】 为了解决无线传感器网络(WSN)部分覆盖中的能耗问题,提出一种基于广义学习自动机(GLA)和休眠机制的部分覆盖方法。首先,将WSN网络构建成一个连通图模型。然后,通过GLA算法从中选择一定数量的节点构成主干网络。最后,检查主干网络是否满足部分覆盖要求,并根据各节点的覆盖性能来选择合适的休眠节点进行激活,从而以最少数量的节点来满足覆盖要求,并保持节点之间的连通性。仿真结果表明:该方法能选择传感器节点来满足覆盖条件,减少了工作节点数量,提升了WSN的寿命。更多还原

【Abstract】 To solve the problem of energy consumption in partial coverage of wireless sensor networks( WSN),a partial coverage method based on generalized learning automata( GLA) and sleep mechanism was proposed. First,the WSN network was constructed into a connected graph model.Then,GLA algorithm was used to select a number of nodes to form backbone network. Finally,it checked whether the backbone network met the partial coverage requirements,and selected the appropriate sleep nodes to activate according to the coverage performance of each node,so as to meet the coverage requirements with the smallest number of nodes,and maintain the connectivity between nodes. The simulation results show that this method can reasonably select sensor nodes to meet the coverage conditions,effectively reduce the number of working nodes and improve the lifetime of WSN.更多还原