基于分层视图的WSN溯源压缩算法的研究

发布时间：2018-03-18 07:49

本文选题：无线传感网络　切入点：多级分簇　出处：《江苏大学》2017年硕士论文　论文类型：学位论文

【摘要】：无线通信技术、传感器技术与嵌入式技术的不断进步,促进了低成本、低功耗、多功能的传感器节点快速发展,从而这种由微型传感器节点组成的无线传感网络(Wireless Sensor Networks——WSN)迅速普及。在WSN中,传感器节点采集监控区域内感知对象的信息,然后通过无线通信将这些信息发送给基站(Base Station——BS),然而由于WSN所处的环境复杂、且涉及的传感器种类数量众多,所以通常需要对BS接收的数据进行可信性评估,只有通过评估的数据才能用于关键决策。一般使用溯源(Provenance)评估数据的可信性。Provenance记录了一个数据从产生、转发至到达BS涉及的所有节点,以及在这些节点上对该数据实施的操作。因此Provenance会随着数据传输链路长度的增加而快速膨胀,当数据量较大时通常采用分段传输以克服Provenance的数据量过载问题。针对现有的Provenance分段传输方法要求BS必须正确收到全部分段数据之后才能实施Provenance解压缩、且平均压缩比较低的问题,本文设计了一种基于逐级重建的Provenance增量压缩方法,即通过对WSN进行多级分簇管理并应用整数具有唯一的素因子分解这一性质,实现了在编码时计算节点之间ID的增量信息,在解码时BS能够按粒度从粗到细逐级精化重建Provenance,由此较为完善地解决了上述问题。理论分析和实验数据均表明,与传统的Provenance分段传输方法相比,该方法不仅具有更高的Provenance压缩比,可有效节省传输能耗,而且由于引入了增量传输,所以鲁棒性也更好。
[Abstract]:The continuous progress of wireless communication technology, sensor technology and embedded technology has promoted the rapid development of low-cost, low-power, multi-function sensor nodes. Thus, this wireless sensor network composed of micro-sensor nodes, Wireless Sensor Networks, is rapidly popularized. In WSN, sensor nodes collect information about perceived objects in monitoring areas. Then the information is sent to the base station by wireless communication. However, due to the complexity of the environment and the large number of sensors involved in the WSN, it is usually necessary to evaluate the credibility of the data received by the BS. Only evaluated data can be used in key decisions. (generally speaking, traceability is used to assess the credibility of the data .Provenance records a data that is generated, forwarded to all nodes involved in the BS, And the operation of the data on these nodes. Therefore, the Provenance expands rapidly as the length of the data transmission link increases, In order to overcome the problem of Provenance data overload, the existing Provenance segmental transmission methods require BS to receive all segmented data correctly before Provenance decompression is implemented. And the average compression is low. In this paper, we design a Provenance incremental compression method based on stepwise reconstruction, that is, by multi-level clustering management of WSN and the application of integer, we have the property of unique prime factor decomposition. The incremental information of the ID between nodes is calculated in the coding process, and the BS can reconstruct the Provenance from coarse to fine granularity step by step in decoding. The above problems are solved perfectly. The theoretical analysis and experimental data show that, Compared with the traditional Provenance segmented transmission method, this method not only has higher Provenance compression ratio, but also has better robustness because of the introduction of incremental transmission.
【学位授予单位】：江苏大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9;TN929.5

【相似文献】