无线传感器网络容错关键技术和算法研究
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摘要
无线传感器网络可广泛应用于军事、建筑、农业、医疗和环境监测等领域,具有广阔的应用前景和商业价值。将传感器节点采集的环境数据传送到Sink节点,并由网络用户进行分析处理,是无线传感器网络的主要任务之一。因此保障采集数据的完整性和准确性是无线传感器网络的主要Qos需求。
然而,监控环境的恶劣、信道的不稳定、网络拥塞等因素,使数据的感知和传输质量大大下降,导致无线传感器网络具有感知能力和传输能力不对称的特性。感知和传输能力不对称主要表现在两个方面,第一,普通无线传感器网络感应器主要采集易于传输的光强、温度等标量信息,但是感应器长期暴露在恶劣环境中易发生故障；第二,随着半导体技术的发展,多媒体传感器节点采集的数据更加精细,但是大量的多媒体数据难以在低速率的无线通信设备中可靠传输。
本文针对上述“传感失谐”特性,围绕传感器网络的容错机制展开研究,主要工作如下：
(1)研究传感器网络中节点感应器失效的容错问题,提出一种基于点割集的感应失效节点容错算法。算法基于数据空间相关模型建立节点的数据强相关图,依据强相关图筛选出与失效节点具有高度相关性的点割集；利用无线通信的广播特性,失效节点采用无线侦听和路由转发得到的点割集节点观测量,对失效点观测盲区进行正交估值。基于点割集的感应失效节点容错算法不仅可以修复网络的观测盲区,同时可以利用失效节点的剩余能量保持网络连通度。
(2)探讨了不规则感知模型下的感应器失效节点容错问题,提出了基于不规则感知模型的感应失效节点容错算法。算法结合节点的地理位置信息和数据相关性对失效节点的邻居进行分类,然后采用分布式凸包判定算法勾勒出感知模型的边界。根据感知边界筛选出可以进行估值的邻居节点观测量。基于不规则感知模型的感应失效节点容错算法可以在复杂的地理环境下对观测盲区进行精确的修复,使用户对观测区域的全局信息做出更准确的分析。
(3)针对丢包环境下视觉传感器网络带宽利用率不高的问题,提出了基于散度模型的协作式图像压缩算法。算法基于热扩散模型,采用了二值量化压缩算法,每个节点采集灰度图像的像素值可用1比特表示,使视觉传感器节点减少了图像数据量。接着利用视觉传感器网络的协作特性,提出了基于簇结构的正交扩展传输机制,正交扩展生成图像的每字节信息量可由簇内节点的二值量化图像进行均分,降低了图像传输时的错误敏感度。
(4)由于视觉传感器网络经常出现集中式连续丢包,致使所传输图像破损面积过大,Sink节点难以对图像进行修复,我们提出网络能耗均衡的图像交织传输算法以解决该问题。交织算法可以有效地将图像中连续破损的像素分散到图像中各个像素子块中,提高图像的错误消隐效果。但是过大的交织器意味着节点需要传输更多的数据。因此基于视觉传感器网络的二维观测模型,结合已有的图像融合技术,EAPI算法通过选取融合后的图像块,平衡了网络能耗和交织器的丢包分散性能。
(5)结合以上的研究工作基础,本文设计了一个视觉传感器网络图像传输原型系统。系统基于XBOW公司生产的iMote2节点硬件平台,采用嵌入式Linux进行开发。系统实现了图像格式转化、图像副本生成、交织传输等功能。最后基于这些功能模块,我们开发了一个集中式的图像搜索引擎。
Wireless sensor networks(WSN) can be applied in many areas, such as:military, structure health surveillance, medical care and environmental monitoring. Wireless sensor networks aim to collect the data produced by sensor nodes and transmit sensing information to users for processing and making decision. For wireless sensor networks, guarantee of the integrity and accuracy of sensing data is the main demand of Qos.
However, in harsh environments, the quality of data collection and transmission decreases, due to failure sensors, unreliable wireless channel and network congestion. The asymmetry characteristics between sensing and transmission capacities are mainly manifested in two aspects. Firstly, sensors in general wireless sensor networks collect easily transmitted information such as light intensity, temperature and other scalar information, while long-term exposure in harsh environments makes sensors prone to failure. Secondly, with the development of semiconductor technology, reliable transmission of a mass of multimedia data is still difficult to be achieved by the low rate wireless communication devices, even though multimedia sensor nodes are capable of collecting the data more accurately. It's necessary to design fault tolerant mechanism for WSN. This dissertation focuses on the challenges of fault tolerant mechanism in wireless sensor networks. The main works are as follows:
(1) A faulty sensor node tolerant algorithm based on cut point set is proposed in the presence of failure sensor issues, by introducing the concepts of spatial correlation model, strong correlation graph and cut-point set. The algorithm first finds out a cut-point set, which has strong spatial correlation with faulty sensor node. According to the observations of cut-point set, the faulty sensor node is able to predict its missing sensor readings by using orthogonal intersection estimation method. Analytic results show that, the algorithm not only can tolerate the faulty sensor node, but also accurately predicts miss-readings, keeps network connectivity and overload balance. The results of miss-readings estimation, obtained from simulations and a greenhouse monitoring experiments, show that the methodology presented in this paper can successfully predict the missing sensor readings.
(2) We further propose a faulty sensor node tolerant algorithm based on irregular sensing model. At first, the failure sensor node classifies its neighbor nodes, by utilizing the geographic information and spatial correlation among neighbor nodes. Therafter, the boundary of irregular sensing model is sketched based on a distributed convex hull deciding algorithm. According to the boundary of irregular sensing model, some neighbor nodes' measurements are selected to predict failure sensor's miss-reading. In complex geographic environments, the algorithm presented in this paper can rstore the blind spot accurately.
(3) Aiming at improving the bandwith utilization in visual sensor networks, an image compression algorithm based on divergence model is proposed. First, by using divergence model, a bi-level gray image is produced in each node. Because a gray scale pixel can be represented by1bit in bilevel image, the amount of image data is reduced. Thereafter, utilizing the feature of cooperation in visual sensor networks, an orthogonal extention mechanism is proposed. Due to the information represented by one byte is shared by several cluster nodes'bi-level pixels, the degradation of received images is controlled. Comparing with the traditional image compression algorithm, the quality of received image measured by PSNR is higher, as the average packet loss rate increases.
(4) Considering the effect of transmission losses on the visual quality of images is always varying and depending on the burst loss length, an energy aware interleaving algorithm is presented in this paper. Among the existing transmission error control techniques, interleaving can improve the visual quality of images without redundant data incurred. Conventionally a larger interleaving data size will be more effective in converting long burst loss into isolated losses. This is at the cost of transmitting more pixels. But how to effectively reduce individual sensor's data load in an energy-constrained distributed transmission network is still an unsolved issue. An energy-aware packet interleaving algorithm(EAPI) is proposed in this paper to regulate burst loss effects by spreading out packets according to each image region's pre-calculated transmission income. Experimental results demonstrate that the proposed scheme can not only improve the end-to-end image transmission quality, but also prolong the lifetime of visual sensor network.
(5) Based on the above achievements, we implement an image transmission prototype for visual sensor networks. The hardware platform model is iMote2, which is produced by Xbow enterprise. And we design our software based on Linux. Some functional modules are implemented, such as:image format convertion, image copies generation, interleaving transmission. Utilizing these functional modules, a centralized image search engine is completed.
然而,监控环境的恶劣、信道的不稳定、网络拥塞等因素,使数据的感知和传输质量大大下降,导致无线传感器网络具有感知能力和传输能力不对称的特性。感知和传输能力不对称主要表现在两个方面,第一,普通无线传感器网络感应器主要采集易于传输的光强、温度等标量信息,但是感应器长期暴露在恶劣环境中易发生故障；第二,随着半导体技术的发展,多媒体传感器节点采集的数据更加精细,但是大量的多媒体数据难以在低速率的无线通信设备中可靠传输。
本文针对上述“传感失谐”特性,围绕传感器网络的容错机制展开研究,主要工作如下：
(1)研究传感器网络中节点感应器失效的容错问题,提出一种基于点割集的感应失效节点容错算法。算法基于数据空间相关模型建立节点的数据强相关图,依据强相关图筛选出与失效节点具有高度相关性的点割集；利用无线通信的广播特性,失效节点采用无线侦听和路由转发得到的点割集节点观测量,对失效点观测盲区进行正交估值。基于点割集的感应失效节点容错算法不仅可以修复网络的观测盲区,同时可以利用失效节点的剩余能量保持网络连通度。
(2)探讨了不规则感知模型下的感应器失效节点容错问题,提出了基于不规则感知模型的感应失效节点容错算法。算法结合节点的地理位置信息和数据相关性对失效节点的邻居进行分类,然后采用分布式凸包判定算法勾勒出感知模型的边界。根据感知边界筛选出可以进行估值的邻居节点观测量。基于不规则感知模型的感应失效节点容错算法可以在复杂的地理环境下对观测盲区进行精确的修复,使用户对观测区域的全局信息做出更准确的分析。
(3)针对丢包环境下视觉传感器网络带宽利用率不高的问题,提出了基于散度模型的协作式图像压缩算法。算法基于热扩散模型,采用了二值量化压缩算法,每个节点采集灰度图像的像素值可用1比特表示,使视觉传感器节点减少了图像数据量。接着利用视觉传感器网络的协作特性,提出了基于簇结构的正交扩展传输机制,正交扩展生成图像的每字节信息量可由簇内节点的二值量化图像进行均分,降低了图像传输时的错误敏感度。
(4)由于视觉传感器网络经常出现集中式连续丢包,致使所传输图像破损面积过大,Sink节点难以对图像进行修复,我们提出网络能耗均衡的图像交织传输算法以解决该问题。交织算法可以有效地将图像中连续破损的像素分散到图像中各个像素子块中,提高图像的错误消隐效果。但是过大的交织器意味着节点需要传输更多的数据。因此基于视觉传感器网络的二维观测模型,结合已有的图像融合技术,EAPI算法通过选取融合后的图像块,平衡了网络能耗和交织器的丢包分散性能。
(5)结合以上的研究工作基础,本文设计了一个视觉传感器网络图像传输原型系统。系统基于XBOW公司生产的iMote2节点硬件平台,采用嵌入式Linux进行开发。系统实现了图像格式转化、图像副本生成、交织传输等功能。最后基于这些功能模块,我们开发了一个集中式的图像搜索引擎。
Wireless sensor networks(WSN) can be applied in many areas, such as:military, structure health surveillance, medical care and environmental monitoring. Wireless sensor networks aim to collect the data produced by sensor nodes and transmit sensing information to users for processing and making decision. For wireless sensor networks, guarantee of the integrity and accuracy of sensing data is the main demand of Qos.
However, in harsh environments, the quality of data collection and transmission decreases, due to failure sensors, unreliable wireless channel and network congestion. The asymmetry characteristics between sensing and transmission capacities are mainly manifested in two aspects. Firstly, sensors in general wireless sensor networks collect easily transmitted information such as light intensity, temperature and other scalar information, while long-term exposure in harsh environments makes sensors prone to failure. Secondly, with the development of semiconductor technology, reliable transmission of a mass of multimedia data is still difficult to be achieved by the low rate wireless communication devices, even though multimedia sensor nodes are capable of collecting the data more accurately. It's necessary to design fault tolerant mechanism for WSN. This dissertation focuses on the challenges of fault tolerant mechanism in wireless sensor networks. The main works are as follows:
(1) A faulty sensor node tolerant algorithm based on cut point set is proposed in the presence of failure sensor issues, by introducing the concepts of spatial correlation model, strong correlation graph and cut-point set. The algorithm first finds out a cut-point set, which has strong spatial correlation with faulty sensor node. According to the observations of cut-point set, the faulty sensor node is able to predict its missing sensor readings by using orthogonal intersection estimation method. Analytic results show that, the algorithm not only can tolerate the faulty sensor node, but also accurately predicts miss-readings, keeps network connectivity and overload balance. The results of miss-readings estimation, obtained from simulations and a greenhouse monitoring experiments, show that the methodology presented in this paper can successfully predict the missing sensor readings.
(2) We further propose a faulty sensor node tolerant algorithm based on irregular sensing model. At first, the failure sensor node classifies its neighbor nodes, by utilizing the geographic information and spatial correlation among neighbor nodes. Therafter, the boundary of irregular sensing model is sketched based on a distributed convex hull deciding algorithm. According to the boundary of irregular sensing model, some neighbor nodes' measurements are selected to predict failure sensor's miss-reading. In complex geographic environments, the algorithm presented in this paper can rstore the blind spot accurately.
(3) Aiming at improving the bandwith utilization in visual sensor networks, an image compression algorithm based on divergence model is proposed. First, by using divergence model, a bi-level gray image is produced in each node. Because a gray scale pixel can be represented by1bit in bilevel image, the amount of image data is reduced. Thereafter, utilizing the feature of cooperation in visual sensor networks, an orthogonal extention mechanism is proposed. Due to the information represented by one byte is shared by several cluster nodes'bi-level pixels, the degradation of received images is controlled. Comparing with the traditional image compression algorithm, the quality of received image measured by PSNR is higher, as the average packet loss rate increases.
(4) Considering the effect of transmission losses on the visual quality of images is always varying and depending on the burst loss length, an energy aware interleaving algorithm is presented in this paper. Among the existing transmission error control techniques, interleaving can improve the visual quality of images without redundant data incurred. Conventionally a larger interleaving data size will be more effective in converting long burst loss into isolated losses. This is at the cost of transmitting more pixels. But how to effectively reduce individual sensor's data load in an energy-constrained distributed transmission network is still an unsolved issue. An energy-aware packet interleaving algorithm(EAPI) is proposed in this paper to regulate burst loss effects by spreading out packets according to each image region's pre-calculated transmission income. Experimental results demonstrate that the proposed scheme can not only improve the end-to-end image transmission quality, but also prolong the lifetime of visual sensor network.
(5) Based on the above achievements, we implement an image transmission prototype for visual sensor networks. The hardware platform model is iMote2, which is produced by Xbow enterprise. And we design our software based on Linux. Some functional modules are implemented, such as:image format convertion, image copies generation, interleaving transmission. Utilizing these functional modules, a centralized image search engine is completed.
引文
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