基于最优连通的WSNs的传输功率控制研究

Research on optimal connectivity based transmission power control for wireless sensor networks

在维持节点间最优连通的情况下,通过调节传输功率降低能耗,提出基于学习自动机的功率控制(LAPC)算法。在LAPC算法中,每个节点装备了学习自动机,并将节点的传输功率范围作为自动机的连续动作集。每个阶段,在保证网络最优连通条件下,学习自动机尽可能选择低的传输功率,降低能耗,进而延长网络寿命。仿真结果表明,与同类算法相比,提出的LAPC算法在传输功率、归一化信噪比、控制消息开销和平均剩余能量四个方面的性能得到有效的提升。

Under the condition of maintaining the optimal connectivity among nodes,the transmission power is adjusted to reduce the power consumption. A learning automata-based power control（LAPC）algorithm is proposed while guaranteeing the network optimal connectivity. In LAPC algorithm,each node is equipped with a learning automation,and the range of transmission power of the node is defined as the continuous action set of the automata. At each stage,low transmission power for the learning automata is possibly selected to reduce the power consumption and prolong the network lifetime while guaranteeing the network optimal connectivity. The simulation results show that,in comparison with similar algorithms,the performances of LAPC algorithm is more effectively improved in the aspects of transmission power,normalized signal-to-noise ratio,control message overhead and average residual energy.