论文标题
无线传感器网络中节点特异性信号融合问题的分布式自适应算法
A Distributed Adaptive Algorithm for Node-Specific Signal Fusion Problems in Wireless Sensor Networks
论文作者
论文摘要
无线传感器网络由传感器节点组成，这些传感器节点是物理分布在不同位置的。空间过滤过程利用这些传感器信号的空间相关性将其融合到满足某些最佳条件的过滤信号中。但是，在融合中心收集原始传感器数据以集中的方式解决该问题将导致高能量和通信成本。已经提出了分布式自适应信号融合（DASF）框架，作为一种通用方法，以分布式方式解决这些信号融合问题，从而降低了网络中的通信和能源成本。 DASF框架假定整个节点之间存在一个共同的目标，即在整个网络上共享最佳过滤器。但是，许多应用程序都需要特定于节点的目标，而所有这些特定于节点的目标仍通过共同的潜在数据模型相关。在这项工作中，我们提出了建立在DASF框架上的DANSF算法，并将其扩展以允许特定于节点的空间过滤问题。
Wireless sensor networks consist of sensor nodes that are physically distributed over different locations. Spatial filtering procedures exploit the spatial correlation across these sensor signals to fuse them into a filtered signal satisfying some optimality condition. However, gathering the raw sensor data in a fusion center to solve the problem in a centralized way would lead to high energy and communication costs. The distributed adaptive signal fusion (DASF) framework has been proposed as a generic method to solve these signal fusion problems in a distributed fashion, which reduces the communication and energy costs in the network. The DASF framework assumes that there is a common goal across the nodes, i.e., the optimal filter is shared across the network. However, many applications require a node-specific objective, while all these node-specific objectives are still related via a common latent data model. In this work, we propose the DANSF algorithm which builds upon the DASF framework, and extends it to allow for node-specific spatial filtering problems.
