基于移动基站和路由策略WSN寿命的算法

A Method on Jointing Mobility of BS and Routing for Lifetime Optimization in Wireless Sensor Networks

针对无线传感器网络的特点,提出了一种基于移动基站和路由策略优化无线传感器网络寿命的方法.首先给出场景中传感器传输相同信息能耗最小的最佳基站位置,进一步分析了不同基站位置对传感器节点能耗的影响,证明网络中传感器节点传输相同信息的总能耗越小则网络寿命越大.为降低移动基站计算的复杂度以提高采集信息的实时性,应用拉格朗日对偶分解和牛顿法简化均衡节点能量过程中的线性规划问题.当场景中有节点因能量耗尽而无法向基站继续传输信息时,根据场景中的拓扑结构自适应调整基站位置以减少节点的能耗,然后采用简化的线性规划最大最小节点寿命,以提高基站收集信息的有效性.理论分析和仿真研究表明:应用拉格朗日-牛顿法简化线性规划问题能够在保证算法快速收敛的同时大幅度地降低计算量.提出的移动基站策略能够大幅度的延长网络寿命,从而实现增加基站接收信息的数量和提高节点能量使用效率的目的.

A method is proposed to optimize network lifetime based on mobility of base station （BS） and routing of sensors in the light of the features of wireless sensor network. Firstly, the best position of the BS is demonstrated, which can minimize the total energy consumption of all the sensors in the network. Further the influence that the different positions of the BS have upon the total energy consumption of all the sensors in the network is analyzed; and meanwhile it is proved that the less energy consumption of the sensor the longer the network lifetime. In order to diminish the complexity of BS computation so as to ensure real-time data gathering, Lagrange-Newton method is adopted and applied to simplify linear programming.When some sensors could not transmit information to the BS because their energies are exhausted, the BS will adaptively adjust the position of BS in order to decrease the total energy consumption of the sensors based on the update topology. And then the simplified linear programming is adopted and applied to max-min the lifetime of the sensor in order to enhance the efficiency of receiving information of the BS. The theoretical analysis and the simulation show that the linear programming simplified by Lagrange-Newton method could not only ensure the computation convergence fast but also largely decrease the complexity of the computation. And the above-mentioned mobility of BS scheme can, to a large extent, extend the network lifetime, so as to increase the amount of information collected by the network and enhance the energy efficiency of the sensor networks.