【作者】 刘壮；

【导师】 房至一；

【作者基本信息】 吉林大学 ， 计算机系统结构， 2012， 博士

【摘要】 随着计算机技术、无线通信技术、网络技术、微机技术、电子技术、无线分布式信息处理技术的发展，由于传感器节点的廉价、低功率等特点，无线传感器网络能够很容易的被部署，并且可以通过传感器感知周围环境的各种实时的真实数据，包括温度、亮度、声音强度、各种尺寸等，传感器网络能够适应很多应用情况，例如恶劣的环境的监测工作、敌对情况发生时的战场监测、交通运输、医疗卫生、工业生产时的自动控制等。随着无线传感器网络的发展，其民用价值也逐步体现，随着IPV6技术和物联网技术的不断发展，无线传感器网络在智能家居、楼宇监控等应用中叶扮演着不可替代的作用，人们依靠无线传感器节点获取生活环境中的各种信息，以便提升人们对各种生活环境的控制，例如远程控制家用电器、远程接收家庭中儿童或者宠物状态的报警灯。无线传感器网络提供给人们崭新的获取与处理信息的方式，成为当今的热门研究领域。本文首先介绍了无线传感器网络的发展状态、研究目的、应用意义，然后简述了无线传感器网络的发展历史、体系结构和适用领域。由于无线传感器节点部署的特点是随机散布，许多情况下是随机抛洒，传感器节点散落的环境很可能是人无法到达的环境，所以对已经部署的传感器节点的后期维护和电量的供给基本不可能做到，传感器节点的体积也不能过大，这使得成本控制和能量高效成为必须面对的问题；另外，由于大量随机部署、成本的限制，大部分无线传感器节点不能够自己拥有自定位能力，而无线传感器应用中，传感器节点感知到信息后，传递出去，必须使信息接收对象明确信息的发出位置，这样的信息才有意义，所以定位问题也是无线传感器网络中的重要问题。接下来，本文分别针对定位和节能问题，对传感器节点定位算法进行详细介绍，对比了主要定位算法的异同，然后对无线传感器网络能耗分布情况做了详细介绍。本文针对定位与节能问题提出若干算法。针对目前定位算法的不足，本文提出了坐标系在虚拟移动时，所有节点定位误差变化的性质，通过这个性质，并根据跳数控制调整节点定位误差，提高节点定位精度，同时，控制跳数可以降低能耗，实验证明该算法能够有效提高节点定位精度。然后本文提出一个基于优选信标节点集合和部分区域射线扫描的无线传感器网络定位算法，该算法参考一定跳数以内的信标节点之间与信标节点到未知节点之间的信号强度相互对比的方式，利用多个信标节点将一个未知节点确定在一个相对较小的区域内，将无线传感器网络的部署空间用虚拟的，起点为圆点的射线扫描空间，找出之前确定的区域与射线扫描的空间的交点，确定交点集合，然后找出交点集合中权重最大的所有交点，最后，确定某子交点集合权重最大，那么这个交点集合的质心就是该未知节点的坐标，实验结果表明，该算法在能耗较低的情况下提升了定位精度。然后本文提出基于肾上腺素控制的GA(GeneticAlgorithm)的无线传感器网络定位算法，该算法借鉴前苏联遗传学家Dmitri Belyaev的实验启发，将肾上腺素控制参数与GA应用到定位算法中，选择质心算法作为参照算法，找出肾上腺素控制参数，使节点定位坐标的种群快速收敛，提高了无线传感器网络的节点定位精度。针对无线传感器网络能量受限的问题，本文提出了一种能耗转移的节能办法，将网络生命周期中能耗较高的路径上的能耗转移到其他路径上，分散整个网络的能耗，延长网络生命周期。本文提出在无线传感器网络生命周期内，节点传递数据的数量与发生这个传递数据量的节点数量之间有稳定对应关系，由于数据传递引发的通信能耗占能耗的比重最大，所以依靠上述关系本文获取能耗从一个路径转移到另一个路径的阈值，在能耗转移的过程中为了将额外的能耗降至最低，本文提出节点作用力的概念，利用这个概念降低新算法产生的额外能耗，实验证明该算法降低的无线传感器网络的能耗，延长了网络的生命周期。本文最后总结了提出的四个算法，分别介绍了三个定位算法的应用环境条件，说明了三个定位算法与一个节能修正算法相互结合的方法，指出了四个算法的不足与局限性，指出了未来的工作方向。更多还原

【Abstract】 Wireless sensor networks are made up of massive sensor nodes that communicate witheach other through wireless communication. Sensor nodes can monitor a lot of changes in thesurrounding environment, such as temperature, brightness, sound intensity, humidity, pressure,target size, target mobile, sensitive substances and so on. After being monitored and processed,these data are passed to the sink node, or the base station or the central node by wirelesscommunications, and then these data are delivered to the users for the follow-up operations.Spreading process is called the node deployment of sensor nodes, which includes the regulartype and the random type. After deployment, these nodes locate themselves firstly and carryon autonomic wireless connection in accordance with their physical locations and thereby awireless network is formed. Because of miniaturizating, integrating, and networking, thewireless sensor networks are developing into an important route of access to the information.With the development of society and people’s requirements, wireless sensor networks aredeveloping from the military fields to civilian areas. Therefore, the aeroamphibiousequipments constantly develop and the applications of wireless sensor networks continue toexpand. The wireless sensor networks are extensively used for civilian areas, such asenvironmental monitoring, medical monitoring, traffic monitoring, security monitoring,production monitoring, agricultural monitoring, underwater monitoring, and space explorationand so on.Wireless sensor network is a network system that wireless sensor nodes are organizedthrough wireless communications. There are a lot of related disciplines, includingmicro-electro-mechanism system, wireless communication technology, the sensor technology,data processing and distribution technology, and after data monitoring, data collection, anddata processing, sensor nodes spread the information and form a network eventually. Wirelesssensor networks form the connections of the physical world, the information world and the human world, and become an important way by which human beings can access toinformation. For setting up the communication bridge between environmental informationand people, wireless sensor networks have a profound impact on the development of thesociety and human progress. Especially in rugged environment, or even hostile environmentthat people can’t reach, the application of wireless sensor networks can guarantee the safety ofpeople.When wireless sensor networks have been deployed, node’s localization is theprerequisite in the follow-up works. Aiming at the localization in wireless sensor network,three algorithms are proposed in this paper to promote the accuracy of node’s localization.Because of limited energy in wireless sensor networks, an energy-efficiency correctionalgorithm is proposed in this paper to reduce energy consumption of the network and extendthe network’s life cycle.VDCSHC localization algorithm is proposed in this paper to enhance the localizationaccuracy in wireless sensor networks. Two-dimensional coordinate system in monitored areais considered as one that can make virtual moves. The calculation of the estimated coordinateof beacon nodes is also involved in the process of localization, when Centroid algorithm orDV-Hop algorithm is used. There will be some differences between the calculated values andthe real values of the coordinates. A new coordinate system is set up by a vertical andhorizontal virtual movement respectively according to the size of the errors. The positions ofall the nodes in monitored area are unchanged in this process. Thus, the moved value of thenew coordinate system will counteract the error generated by the localization algorithm. Eachbeacon node is in a virtual coordinate system newly built up after moving, through the controlof the Hop range, and then the surrounding unknown nodes are selected. These unknownnodes will refer to the new virtual coordinate system built according to the beacon nodes andcalculate their location information. All these unknown nodes will refer to this beacon nodeon the new virtual coordinate system to calculate their locations. And the error generated byoriginal localization algorithm is counteracted, and then the accuracy of location algorithmcan be improved.From the analysis, the unknown node should use the virtual dynamic coordinate system which is based on the nearest beacon node. When the unknown node and the beacon nodechoose the other three beacon nodes in the same way by trilateration, the only parameter thatcould affect localization accuracy is the distance between the measured node and thereference node, and the parameter is determined by the product of two parameters which arethe hop distance of each node and the hop quantity between itself and reference node. It ishard to determine the value. The huge difference between the hop quantities of two nodes maybe compensated by the huge error of each hop’s distance. Nevertheless, it is certain that theerrors of two nearest nodes are the most similar. This paper proposes VDCSHC algorithmwith the idea of hop quantity control, which suggests that when the unknown node choosesvirtual reference coordinate system, it must choose the virtual coordinate system informationsent by beacon node in one hop. If the unknown node can not find the beacon node in one hop,it should ask for the related virtual coordinate system from other unknown nodes in its onehop, and if it still can not find the information, it adds the hop control size until it gets thevirtual coordinate system information successfully. If there is no beacon node or unknownnode in one hop, this unknown node is a meaningless node, and the localization of this nodeshould be given up. The simulation experiments show that the algorithm can enhance thelocalization accuracy efficiently.A new localization algorithm which is based on optimizing beacon nodes assembly andray scan is proposed in this paper to enhance the localization accuracy in wireless sensornetworks. OAARS is for short. The idea is that we need to optimize the selection of beaconnodes which assists the localization of unknown nodes at first. This could be performed bycomparing the signal intensity or the time of signal arrival. Firstly, every beacon node sendsits location to the nearby beacon nodes. The beacon node receives the signal records, thelocation and the signal intensity or the time of signal arrival, in order to gain the relativedistances between other beacon nodes and itself. Beacon nodes don’t transmit the message.The beacon node monitors whether its record of other beacon nodes exceeds a constant. If not,there is a lack of beacon nodes within one hop and the request will continue.Secondly, beacon nodes send their records to the nearby unknown nodes. The laterreceive the message and record signal intensity of the beacon nodes, based on which a beacon node is chosen as reference node. Based on the record of the reference beacon node, theunknown node chooses the beacon node with the weakest signal intensity among those whosesignal intensity between the reference beacon nodes is stronger than that between thereference beacon node and the chosen unknown node as well as the beacon node with thestrongest signal intensity among those whose signal intensity between the reference beaconnode is weaker than that between the reference beacon node and the chosen unknown node.We mark those three beacon nodes, and then we can find several similar beacon nodes asoptimized beacon nodes set. Simulating the radar, we draw a ray from the original point. Theray scans in the first quadrant of the monitored two-dimensional Euclidean space. Wecalculate the intersection points’ sets of the ray and the arc formed by these beacon nodes. Theintersection points’ sets are filtered through certain method and then we calculate centroid ofthe left intersection points as the estimated coordinates of the unknown node. Simulationexperiments show significant improvement in localization accuracy.A new localization algorithm based on the adrenaline control of Genetic Algorithm (GA)in wireless sensor networks is proposed in this paper to enhance the localization accuracy,GACAL is for short. GACAL algorithm uses the GA to locate the nodes, and designs thefitness generation in GA. In this paper, the fitness corresponding to the dog world is"submission", and dogs have more biodiversity and species thriving than any other creatures,because of mankind’s intervention, through choosing the individual breeding "looksobedience". People can intervene successfully in such a short time by the adrenaline controltheory.This paper proposes GACAL algorithm, and finds out the adrenaline control parametersin order to assist and guide the fitness function to make it faster to find the optimal solution.This paper introduces the design of GA steps and adrenaline control parameters for Centroidalgorithm in wireless sensor networks, the simulation experiments show that the newalgorithm can improve the localization accuracy effectively.A new energy efficiency algorithm is proposed in this paper based on energyconsumption transference and node gravitation in wireless sensor networks, ETCDGalgorithm is for short. This paper finds network characteristics when failed nodes come up, and makes data transference avoid these nodes in the network. We can avoid the path with thebiggest energy consumption in network by this way, and the energy consumption can bescattered into the whole network by performing this periodically in order to reduce thenetwork energy consumption and increase network lifecycle. Then how to determine thenodes with the highest energy consumption is the key to the problem. Through a large numberof experimental analyses, this paper finds that there is a certain relationship between thenumber of data transference and the number of nodes with the amount of data transference inthe whole network lifecycle. From this point of view, this paper describes this relationship,and gains the distribution features through experiments. According to this feature, ETCDG isput forward in data-transference stage to set strategy to promote energy efficiency of therouting protocol. Nevertheless, during this process, ETCDG produces extra energyconsumption. A new method based on node gravitation is proposed to reduce this extra energyconsumption. Experiments show that ECTDG algorithm can reduce the energy consumptionand prolong the lifecycle of wireless sensor network effectively.Furthermore, the application conditions of the three localization algorithms are analyzedin this paper, and then the shortcomings and the combining method of four algorithms aregiven. Finally, the future research direction is described.更多还原