摘要
为优化无线传感器网络(Wireless Sensor Network,WSN)中各节点的部署与定位,常常需要采取一定的自适应轮换调度算法。传统的调度算法大都采用基于信息度增益控制的节点定位技术进行设计,算法性能随着迭代次数的增加而下降。为此提出了一种基于遗传控制的传感器节点自适应轮换调度算法,该算法利用遗传适应度函数对群体中的每个特征量进行自适应计算,结合变异遗传散布控制量与LSSVM训练模型对传感器节点进行优化部署,实现了对传感器节点的自适应轮换调度。经过实验仿真,结果表明,采用该算法实现对传感器网络节点的自适应轮换调度,能有效提高传感器网络定位的准确性,并提高传感器网络的生命周期,节省能量开销,提高网络的生存能力。
In order to optimize the deployment and location of each node in wireless sensor networks, it often needs to take certain adaptive rotation scheduling algorithm. Most of the traditional scheduling algorithms are designed using the node localization technology control based on information gain, which leads to the decrease of the algorithm performance with the increase of the number of iterations. For this, a sensor node adaptive rotation scheduling algorithm based on genetic control is proposed. The algorithm uses the genetic fitness function to adaptively compute each feature in the group, and combines with genetic spread control and LSSVM training model to optimize the sensor node deployment, realizing the adaptive rotation scheduling of sensor node. The simulation results show that the adaptive algorithm can improve the localization accuracy and life cycle of the sensor network, save energy cost, and increase the survivability of the network.
出处
《控制工程》
CSCD
北大核心
2016年第2期254-258,共5页
Control Engineering of China
基金
四川省教育厅自筹科研项目(15ZB0276)
关键词
传感器
遗传控制
自适应
轮换调度
Sensor
genetic control
adaptive
rotation scheduling
