摘要
针对无线传感器网络的节点能量有限,且在进行信息传输时存在数据冲突、传输延时等问题,提出并设计了基于最大生存周期的无线传感器网络数据融合算法。该算法将整个网络中的节点分成多个簇,并根据节点的传输范围,将每个簇中的节点均匀分布,每个节点根据自己的本地信息和剩余能量选择通信方式向簇头节点传输数据,从而形成传输数据的最短路径;并根据集中式TDMA(时分多址)调度模型,运用基于微粒群的Pareto优化方法,使得网络在完成规定的信息传输时每个节点耗费的平均时隙和平均能耗最优。仿真结果表明,上述算法不但可以最大化网络的生存时间,还可以有效的降低数据融合时间,减少网络延时。
Aiming at the wireless sensor networks problems such as limited node energy, information transmission data conflict and transmission delay, the data aggregation algorithm for wireless sensor networks based on maximum survive period was proposed and designed.The algorithm divides the nodes of the entire network into multiple clusters, and according to the transmission range of nodes, each cluster node evenly distributed, each node selects communication method to transmit data to the cluster head node according to their own local information and the residual energy, and thus the shortest path of the data transmission is formed. Pareto Optimization method based on particle swarm is designed according to the centralized TDMA （Time Division Multiple Access） scheduling model, which makes each node average time slot and average energy consumption optimal after the network completes the information transmission. The simulation results show that the above algorithm can not only maximize the network lifetime, but also can effectively reduce data aggregation time and reduce network time delay.
出处
《电子设计工程》
2013年第7期47-50,54,共5页
Electronic Design Engineering
基金
2011年总装备部基金资助项目(2011485)
关键词
无线传感器网络
数据融合
能耗
延时
时分多址
微粒群
生存时间
PARETO优化
Wireless Sensor Network （WSN）
data aggregation
energy consumption
delay
TDMA （Time Division Multiple Access）
particle swarm
survive period
Pareto optimization
