【作者】 欧阳烨龙；

【导师】 陈祖爵；

【作者基本信息】 江苏大学 ， 计算机应用技术， 2010， 硕士

【摘要】 无线传感器网络以数据为中心形成转发路径,由于节点能量和资源有限,对路由协议的设计提出了高能效、低时延、负载均衡等特殊要求,是无线传感器网络研究的热点。针对大规模节点的传感器网络,论文从无线传感器网络节点负载均衡的角度,提出了两种路由算法(1)栅格错位的负载均衡GAF算法(GAFDG)GAFDG算法是GAF算法基础上提出的。GAFDG算法与GAF算法相比,其最大的区别是对监测区域进行了栅格错位划分。GAFDG算法在各栅格中选择一个簇首,其簇首选择机制采用“最小能量消耗原则”。GAF算法的各簇首可以与上、下、左、右四个栅格的簇首进行通信；而GAFDG算法的各栅格与六个栅格相邻,其各簇首可以和六个相邻簇首进行通信。因此,由各簇首构建的无线传感器网络的骨干网络进行通信时,GAFDG算法在路由选择的方向性上优于GAF算法。理论证明,GAFDG算法的单跳信号覆盖范围比GAF算法扩大了12%。仿真测试结果也表明,GAFDG算法在能耗和网络生存时间等性能均优于GAF算法。(2)层次蜂窝结构的负载均衡GAF算法(GAFHH)GAFHH算法是在蜂窝结构的GAF算法(GAFH)的基础上提出的。GAFHH算法与GAFH算法的主要区别是：GAFH算法的每个蜂窝内的节点组成一簇,而GAFHH算法由几个相邻蜂窝内的节点组成一簇。GAFHH算法对监测区域进行蜂窝栅格划分,在节点单跳通信半径R的约束下,几个相邻的栅格内的节点组成一簇,各簇的栅格按照其在簇中的位置进行统一编号。选择在簇中间的栅格为活跃栅格,并按照“最大剩余能量原则”在活跃栅格中选择一个节点作为簇首,由各簇首构建无线传感器网络的骨干网络进行通信。在下一轮的开始,重新选择活跃栅格,并移动各簇的边界,使得活跃栅格始终位于其簇的中间位置。仿真测试中,GAFHH算法与GAFDG算法和GAFH算法进行对比。仿真结果表明,前者在网络负载均衡和网络吞吐量等性能优越后两者。因此,GAFHH算法的提出是有一定意义的。论文提出的GAFDG和GAFHH两种算法,按照相应规则进行栅格划分及节点组织成簇,并根据各自特点,分别采用“最小能量消耗原则”和“最大剩余能量原则”选择簇首,由各簇首构建无线传感器网络的骨干网络进行通信,合理地从空间上调度了网络的能量资源,延长网络的生存期,达到了网络负载均衡的目的。论文最后阐述了无线传感器网络在煤田火区远程监测中的应用,其中的路由技术采用了类似GAFHH算法,目的是均衡各节点负载。更多还原

【Abstract】 The network layer of wireless sensor network (WSN) forms the transmission path based on data-centric concept. As the node energy and resources are limited, the design of routing algorithm should meet energy-efficient, low latency, load balance and other special requirements which are hot issues in current WSN research. Two routing algorithms are respectively proposed for load balance in large scale sensor networks:(1) the Load-balancing GAF Algorithm of Dislocated Grid for WSN (GAFDG)GAFDG is proposed which based on the GAF. The biggest difference between GAFDG and GAF is that the grids in GAFDG are dislocated arrangement, but the grids in GAF are aligned arrangement. Nodes are selected as cluster heads according to "the principle of minimum energy consumption" in GAFDG. Each cluster head of GAF can communicate with four cluster heads which respective in the up, down, left, and right the four adjacent grids except the cluster head in the edge grid. While each grid has six adjacent grids in GAFDG. Therefore, each cluster head of GAFDG can communicate with six adjacent cluster heads. Therefore, when the backbone network of WSN, which is constructed by all cluster heads, is communicating, GAFDG is better than GAF in orientation of routing. Theoretically proofing, the single-hop coverage area of GAFDG is wider 12% than GAF. Simulation results also show that energy consumption and network lifetime of GAFDG are superior to GAF.(2) the Load-balancing GAF Algorithm of Hierarchical Honeycomb Structure for WSN (GAFHH)GAFHH is proposed which based on the GAF Algorithm of Honeycomb Structure for WSN (GAFH). The major difference between GAFHH and GAFH is:the nodes within the same honeycomb grid construct a cluster in GAFH, while the cluster in GAFHH is constructed by the nodes that belong to several adjacent honeycomb grids. The monitoring area is divided into honeycomb grids in GAFHH, and the nodes belong to several adjacent honeycomb grids construct a cluster which is restricted by single-hop communication radius R of node, and each grid is unified numbered according to its position in the cluster. Selecting a middle grid as a active grid in each cluster, and adopting "the principle of maximum residual energy" to select a active node as a cluster head in each active grid. And all cluster heads construct the backbone network of WSN is using of communicating. At the beginning of the next round, re-selecting the active grid, and moving the boundaries of each cluster to make the active grid is always in the center of the cluster. In the simulation, GAFHH compare with GAFDG and GAFH, and simulation results show that the load balancing and throughput of the former are superior to the latter two. Therefore, the proposing of GAFHH is a certain significance.GAFDG and GAFHH are proposed in the paper, respectively dividing monitoring area into the dislocated square girds and honeycomb grids and making the nodes construct a cluster by the corresponding rules, and respectively adopting "the principle of minimum energy consumption" and "the principle of maximum residual energy" to select cluster heads, and the backbone network of WSN is constructed by all cluster heads. Reasonably dispatching the energy resources from the space, extending the network lifetime, and achieving the purpose of load balancing. Finally, the paper describes the application of WSN in remote monitoring system of Coal-field Fires, whose routing had adopted a similar GAFHH to balance the load of each node.更多还原