基于最大生存周期的无线传感器网络数据融合

Data aggregation for wireless sensor networks based on maximum survive period

针对无线传感器网络节点能量有限并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.该算法将均匀分布或非均匀的整个网络中的节点分成多个簇,并根据节点的位置、分布密度和剩余能量等信息选择传输数据的方式,从而形成传输数据的最短路径.根据集中式TDMA(时分多址)调度模型并运用基于微粒群的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,该算法不但可以最大化网络的生存时间,还可以有效地降低数据融合时间,减少网络延时.

In order to solve the problems that the node energy of wireless sensor networks is limited, and the phenomenon of both data conflict and transmission delay exit in the information transmission, a data aggregation algorithm for wireless sensor networks based on maximum survive period was proposed and designed. The homogeneously and inhomogeneously distributed nodes in entire networks were divided into multiple clusters with the proposed algorithm. In addition, the data transmission mode was selected according to such information as the location, distribution density and residual energy of nodes, and thus, the shortest path for data transmission was generated. According to the centralized time division multiple access （TDMA） scheduling model and particle swarm based Pareto optimization method, the optimum average time slot and average energy consumption for each node in the networks after completing the prescriptive information transmission could be obtained. The simulated results show that the proposed algorithm can not only maximize the survive period of networks, but also effectively reduce the data aggregation time and decrease time delay of networks.