关于无线传感器网络节能的若干关键问题研究
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摘要
近年来,无线传感器网络技术得到了飞速发展和广泛关注。作为影响传感器网络性能的一个核心方面,传感器节点的节能问题已成为当前的研究热点之一。本论文重点研究了与无线传感器网络节能相关的四个方面的问题:节点定位、网络分簇与簇间路由、感知区域覆盖以及网络瓶颈节点判别,提出了相应的模型和算法,并进行了仿真验证与分析。论文工作的创新点主要体现在如下方面:
1)提出了一种分布式WSN节点定位方法,称为MDS-MAP(D)。该方法以Ordinal MDS理论为基础,使用节点间的跳数作为度量,结合局部网络划分机制,获得局部网络内的节点间的最短路径,在不需要高精度的测距方法和设备的支持下,完成节点相对位置的计算,降低了节点定位的计算复杂度。明确给出了融合局部网络的方法。利用有限的锚点绝对坐标,该方法可以获得全部节点的绝对坐标,且定位精度较类似方法有所提高。
2)提出了一种非均匀分簇和建立簇间路由的算法,称为EER。EER通过一个给出的非线性函数控制节点的竞争半径,调整非线性函数行为的参数与节点到sink点的跳数有关。在该函数的控制下,WSN中建立的节点簇的大小呈现非均匀的状态,距离sink点越近的区域,簇的面积越小,簇中含有的节点越少。节省簇头汇聚簇内节点数据的能量,可使更多的能量用于簇间的数据转发。EER的非均匀分簇方法还使得临近sink点的区域能够产生更多的簇,有利于簇头节点的能耗平衡。
3)提出了一种能耗平衡的WSN感知区域连通覆盖模型。该模型基于Voronoi划分和Delaunay三角剖分理论,包含跳数测量、节点分层、网络三角剖分和可休眠节点查找等环节,利用节点分层机制,在保证各个节点到sink点的连通性的同时,寻找可同时进入休眠状态的冗余节点,从而得到感知区域的连通覆盖集。
4)提出了一种WSN网络瓶颈节点的判别方法,称为节点瓶颈系数法。节点瓶颈系数法包含两个核心概念:节点的路由节点概率和下层邻居平均节点出度。节点瓶颈系数很好地表征了节点的瓶颈程度。计算瓶颈系数仅需要了解节点的一跳邻居信息。
In recent years, wireless sensor network technologies have developed rapidly and aroused wide public concern. As one of the core aspects about the performance of wireless sensor networks, energy-saving issue of sensor node has become a hot spot of present research. In this dissertation, four aspects relating to energy-saving in wireless sensor networks are studied, which include node locating, network clustering and routing between clusters, sensing region coverage, and network bottleneck distinguishing. Also corresponding models and algorithms are presented, and analysis and simulation results show their effectiveness. The main innovations in the dissertation are outlined as following:
1) A distributed method of WSN node locating, named MDS-MAP(D), is presented. The method based on the theory of Ordinal MDS uses hops as the metric of path length to calculate the shortest path between nodes in local networks derived by local network partition mechanism. Without support of high accuracy ranging technologies and equipments, the method can calculated the relative node location with lower calculating complexity. Furthermore, a method of merging local networks is clearly put forward, which can gets absolute coordinates of all nodes by utilizing the absolute coordinates of limited anchors and gain an increase in locating precision comparing with other similar methods.
2) An algorithm for partitioning WSN into non-uniform clusters and building route between clusters, called EER, is introduced. EER controls the node competing radius by a given non-linear function whose behaviors are adjusted by a parameter relating to hops from node to the sink. Under the control of the non-linear function, clusters built in WSN have non-uniform size, i.e., the nearer the sink, the smaller the cluster. The smaller cluster contains less nodes and can save more cluster header energy used for data aggregation for transmitting data between clusters. The non-uniform clustering method can build more clusters near the sink region that helps to balance the energy consumption over different cluster headers.
3) A model, which can generate connected coverage with balance energy consumption for sensing region of WSN, is presented. The model based on the Voronoi diagram and the Delaunay triangulation theories includes several links such as hops measurement, nodes layer, network triangulation, sleepable node search and so on. In detail, the model seeks nodes that can sleep together by nodes layer mechanism to obtain the coverage of a sensing region, and ensure the connectivity of nodes at the same time.
4) A discriminance method of bottleneck node in WSN, called node bottleneck coefficient, is addressed. The node bottleneck coefficient has two kernel concepts. One is route node probability of nodes and the other is average out-degree of neibour nodes in lower layer. The node bottleneck coefficient characterizes the bottleneck degree of nodes well. And it only needs to know the information of neibours within one hop to calculate the node bottleneck coefficient.
1)提出了一种分布式WSN节点定位方法,称为MDS-MAP(D)。该方法以Ordinal MDS理论为基础,使用节点间的跳数作为度量,结合局部网络划分机制,获得局部网络内的节点间的最短路径,在不需要高精度的测距方法和设备的支持下,完成节点相对位置的计算,降低了节点定位的计算复杂度。明确给出了融合局部网络的方法。利用有限的锚点绝对坐标,该方法可以获得全部节点的绝对坐标,且定位精度较类似方法有所提高。
2)提出了一种非均匀分簇和建立簇间路由的算法,称为EER。EER通过一个给出的非线性函数控制节点的竞争半径,调整非线性函数行为的参数与节点到sink点的跳数有关。在该函数的控制下,WSN中建立的节点簇的大小呈现非均匀的状态,距离sink点越近的区域,簇的面积越小,簇中含有的节点越少。节省簇头汇聚簇内节点数据的能量,可使更多的能量用于簇间的数据转发。EER的非均匀分簇方法还使得临近sink点的区域能够产生更多的簇,有利于簇头节点的能耗平衡。
3)提出了一种能耗平衡的WSN感知区域连通覆盖模型。该模型基于Voronoi划分和Delaunay三角剖分理论,包含跳数测量、节点分层、网络三角剖分和可休眠节点查找等环节,利用节点分层机制,在保证各个节点到sink点的连通性的同时,寻找可同时进入休眠状态的冗余节点,从而得到感知区域的连通覆盖集。
4)提出了一种WSN网络瓶颈节点的判别方法,称为节点瓶颈系数法。节点瓶颈系数法包含两个核心概念:节点的路由节点概率和下层邻居平均节点出度。节点瓶颈系数很好地表征了节点的瓶颈程度。计算瓶颈系数仅需要了解节点的一跳邻居信息。
In recent years, wireless sensor network technologies have developed rapidly and aroused wide public concern. As one of the core aspects about the performance of wireless sensor networks, energy-saving issue of sensor node has become a hot spot of present research. In this dissertation, four aspects relating to energy-saving in wireless sensor networks are studied, which include node locating, network clustering and routing between clusters, sensing region coverage, and network bottleneck distinguishing. Also corresponding models and algorithms are presented, and analysis and simulation results show their effectiveness. The main innovations in the dissertation are outlined as following:
1) A distributed method of WSN node locating, named MDS-MAP(D), is presented. The method based on the theory of Ordinal MDS uses hops as the metric of path length to calculate the shortest path between nodes in local networks derived by local network partition mechanism. Without support of high accuracy ranging technologies and equipments, the method can calculated the relative node location with lower calculating complexity. Furthermore, a method of merging local networks is clearly put forward, which can gets absolute coordinates of all nodes by utilizing the absolute coordinates of limited anchors and gain an increase in locating precision comparing with other similar methods.
2) An algorithm for partitioning WSN into non-uniform clusters and building route between clusters, called EER, is introduced. EER controls the node competing radius by a given non-linear function whose behaviors are adjusted by a parameter relating to hops from node to the sink. Under the control of the non-linear function, clusters built in WSN have non-uniform size, i.e., the nearer the sink, the smaller the cluster. The smaller cluster contains less nodes and can save more cluster header energy used for data aggregation for transmitting data between clusters. The non-uniform clustering method can build more clusters near the sink region that helps to balance the energy consumption over different cluster headers.
3) A model, which can generate connected coverage with balance energy consumption for sensing region of WSN, is presented. The model based on the Voronoi diagram and the Delaunay triangulation theories includes several links such as hops measurement, nodes layer, network triangulation, sleepable node search and so on. In detail, the model seeks nodes that can sleep together by nodes layer mechanism to obtain the coverage of a sensing region, and ensure the connectivity of nodes at the same time.
4) A discriminance method of bottleneck node in WSN, called node bottleneck coefficient, is addressed. The node bottleneck coefficient has two kernel concepts. One is route node probability of nodes and the other is average out-degree of neibour nodes in lower layer. The node bottleneck coefficient characterizes the bottleneck degree of nodes well. And it only needs to know the information of neibours within one hop to calculate the node bottleneck coefficient.
引文
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