WSN中基于压缩网络编码的数据汇集技术研究
发布时间：2019-02-11 09:57
【摘要】：作为21世纪的前沿技术,无线传感器网络(Wireless Sensor Network,WSN)在工农业控制、生物医疗、环境质量监测、抢险救灾以及国防军事等领域都扮演着重要角色。它融合了传感技术、微电子工艺以及无线网络技术,为人类认识世界和改变世界提供了一种很有效的工具。在WSN中,各分布式节点分别采集到局部数据后进行一定的处理,然后将处理过的数据通过某指定的路由路径发送给汇聚节点,最后由汇聚节点对处理后的数据进行还原后得到区域数据。不同的路由路径选择方法以及中间节点对数据的处理方法,形成了各种不同的数据汇集方案。目前应用在WSN的数据汇集方案主要有两种:基于特定路由的多跳存储转发、基于网络编码的数据传输与处理。其中前者设计简单,可以渐进地感知数据,但是会使得网络中各节点处于能耗不均衡状态,严重影响网络寿命;后者虽然可以提高的无线网络的能耗均衡性,但解码端却存在“全有或全无”(All-or-Nothing,AON)问题,严重影响了网络的可靠性。本文介绍了WSN的结构以及典型的数据汇集技术,并说明其存在的不足;分别对网络编码(Network Coding,NC)以及压缩感知(Compressed Sensing,CS)的数学模型进行了说明,包括随机线性网络编码(RLNC,Random Linear Network Coding)以及压缩感知的非相干测量和重构算法;分析两者之间的内在联系,利用WSN各节点感知数据的相关性以及无线传输的广播特性,建立了一个渐进感知的高能效的WSN数据汇集方案——压缩网络编码方案(Compressed Network Coding,CNC),简单来说就是由于WSN中感知数据存在相关性,对于不满秩的网络编码矩阵可以利用CS解码算法重构出来一定的信息。在该方案的基础上,分析了汇聚节点对感知数据的重建过程,结果表明,汇聚节点对于数据的重建是渐进的,CNC方案精确重构数据的成功率比一般的网络编码方案高了约15%以上,从而很好地解决了AON问题。然后通过定性和定量分析网络各节点的能耗情况,说明该方案可以大幅改善WSN的能耗均衡问题以及减少网络总能耗,从而提高了网络寿命。在每个无线节点的能量都相同的情况下,采用了CNC方案的WSN的网络寿命是采用了传统的汇聚树路由协议(Collection Tree Protocol,CTP)WSN的2.5倍以上。
[Abstract]:As a frontier technology in the 21st century, wireless sensor network (Wireless Sensor Network,WSN) plays an important role in the fields of industrial and agricultural control, biomedicine, environmental quality monitoring, emergency relief and defense and military. It combines sensing technology, microelectronics technology and wireless network technology, and provides a very effective tool for people to understand and change the world. In WSN, each distributed node collects local data and processes it to a certain extent, and then sends the processed data to the convergent node through a specified routing path. Finally, the region data is obtained after the data is restored by the convergent node. Different routing path selection methods and data processing methods of intermediate nodes form a variety of data aggregation schemes. At present, there are two kinds of data collection schemes used in WSN: multi-hop storage and forwarding based on specific route, data transmission and processing based on network coding. The former design is simple and can gradually perceive the data, but it will make the nodes in the network energy imbalance, which seriously affect the network life. Although the latter can improve the equalization of energy consumption in wireless networks, there exists the problem of "all or nothing" (All-or-Nothing,AON) in the decoder, which seriously affects the reliability of the network. This paper introduces the structure of WSN and the typical data collection technology, and explains its shortcomings. The mathematical models of network coding (Network Coding,NC) and compressed sensing (Compressed Sensing,CS) are described, including random linear network coding (RLNC,Random Linear Network Coding) and incoherent measurement and reconstruction algorithms of compressed sensing. Based on the analysis of the internal relationship between the two, a progressive perceptual WSN data aggregation scheme, compressed network coding scheme (Compressed Network Coding,CNC), is established by using the correlation of WSN nodes' perceptual data and the broadcast characteristics of wireless transmission. Simply speaking, because of the correlation of perceptual data in WSN, the network coding matrix with unsatisfactory rank can be reconstructed by CS decoding algorithm. On the basis of the proposed scheme, the process of data reconstruction is analyzed. The results show that the convergent node is progressive for data reconstruction. The success rate of accurately reconstructing data in CNC scheme is more than 15% higher than that in general network coding scheme, which solves the problem of AON well. Then through qualitative and quantitative analysis of the energy consumption of each node in the network, it is shown that the proposed scheme can greatly improve the energy balance problem of WSN and reduce the total energy consumption of the network, thus increasing the network lifetime. Under the condition that the energy of each wireless node is the same, the network lifetime of WSN using CNC scheme is more than 2.5 times that of WSN using traditional convergent tree routing protocol (Collection Tree Protocol,CTP).
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP212.9;TN929.5
本文编号：2419624
[Abstract]:As a frontier technology in the 21st century, wireless sensor network (Wireless Sensor Network,WSN) plays an important role in the fields of industrial and agricultural control, biomedicine, environmental quality monitoring, emergency relief and defense and military. It combines sensing technology, microelectronics technology and wireless network technology, and provides a very effective tool for people to understand and change the world. In WSN, each distributed node collects local data and processes it to a certain extent, and then sends the processed data to the convergent node through a specified routing path. Finally, the region data is obtained after the data is restored by the convergent node. Different routing path selection methods and data processing methods of intermediate nodes form a variety of data aggregation schemes. At present, there are two kinds of data collection schemes used in WSN: multi-hop storage and forwarding based on specific route, data transmission and processing based on network coding. The former design is simple and can gradually perceive the data, but it will make the nodes in the network energy imbalance, which seriously affect the network life. Although the latter can improve the equalization of energy consumption in wireless networks, there exists the problem of "all or nothing" (All-or-Nothing,AON) in the decoder, which seriously affects the reliability of the network. This paper introduces the structure of WSN and the typical data collection technology, and explains its shortcomings. The mathematical models of network coding (Network Coding,NC) and compressed sensing (Compressed Sensing,CS) are described, including random linear network coding (RLNC,Random Linear Network Coding) and incoherent measurement and reconstruction algorithms of compressed sensing. Based on the analysis of the internal relationship between the two, a progressive perceptual WSN data aggregation scheme, compressed network coding scheme (Compressed Network Coding,CNC), is established by using the correlation of WSN nodes' perceptual data and the broadcast characteristics of wireless transmission. Simply speaking, because of the correlation of perceptual data in WSN, the network coding matrix with unsatisfactory rank can be reconstructed by CS decoding algorithm. On the basis of the proposed scheme, the process of data reconstruction is analyzed. The results show that the convergent node is progressive for data reconstruction. The success rate of accurately reconstructing data in CNC scheme is more than 15% higher than that in general network coding scheme, which solves the problem of AON well. Then through qualitative and quantitative analysis of the energy consumption of each node in the network, it is shown that the proposed scheme can greatly improve the energy balance problem of WSN and reduce the total energy consumption of the network, thus increasing the network lifetime. Under the condition that the energy of each wireless node is the same, the network lifetime of WSN using CNC scheme is more than 2.5 times that of WSN using traditional convergent tree routing protocol (Collection Tree Protocol,CTP).
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP212.9;TN929.5
【参考文献】
相关期刊论文 前1条
1 熊志强;黄佳庆;刘威;杨宗凯;;无线网络编码综述[J];计算机科学;2007年03期
，本文编号：2419624
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