【作者】 薛亮；

【导师】 关新平；

【作者基本信息】 燕山大学 ， 控制科学与工程， 2012， 博士

【摘要】 无线传感器网络位置信息获取技术主要包含网内目标位置估计(目标定位)和传感器节点自身位置获取(节点自定位)。本文主要研究层次型网络拓扑中的单目标定位问题，基于时间序列预测方法，设计任务相关节点的调度与唤醒策略，并给出跟踪失效的恢复办法。对于节点自定位问题，当使用移动信标时，未知节点的位置估计建立为一类无约束优化问题，目标为求解超球面/(平面)上的最短距离。此外，路由设计在无线传感器网络协议设计中具有重要地位。依研究对象不同，路由问题分为：路由发现、中继选择、链路速率分配等。近年来，跨层设计因简单灵活的设计思路，以及对无线网络结构和应用环境的普适性等优点，跨层设计理念与无线网络形成天然结合。本文4-6章设计出适用于无线传感器网络的路由协议。特别地，第6章根据跨层设计思想，研究了认知无线网络中资源重构与鲁棒路由的联合设计，该方法同样适用于频谱灵捷的无线传感器网络。主要成果总结如下：首先，目标定位问题中网内部署节点依功能划分为簇首节点与成员节点，并定义了任务相关节点集。根据目标历史轨迹信息，使用时间序列预测机制动态唤醒探测节点。簇首负责调度成员节点的感知任务，根据目标运动状态，任务簇首在上/(下)游簇首间切换。其次，前述网内目标位置估计问题，帮助解决传感器网络中的节点定位问题。通过将前一问题中的移动目标视作移动信标，成员节点视作未知节点，建立了移动信标与未知节点的信息交互模式。通过设置信标阈值，该算法自适应调节移动信标速度。进一步地，根据移动信标的随机运动行为，证明算法引入消息回复机制的节能有效性。最后，给出算法流程和数值仿真结果，验证不同参数条件下的算法性能。最后，对于无线传感器网络中的路由问题而言，研究了具有QoS保证的启发式路由算法，实现网络在多性能需求下的路由设计。基于拓展的能量代价指标定义两跳邻居的中继候选概率，并依据最小容忍速率更新一/(两)跳邻居集。对于传输可靠性而言，链路质量指示(LQI)与传输可靠性的关系预测了可行链路接收端的数据正确接收率。当路由问题引入规划方法求解时，研究了无线传感器网络中层次型拓扑控制与簇内链路速率分配的联合设计。该问题使用离散变量刻画从属关系，簇内链路速率描述为连续变量，目标为网络寿命最大化。通过顺次固定法求解组合优化问题，得出双向链路条件下的次优从属。通过仿真对比，进一步验证该次优解逼近全局最优解。针对认知无线网络，频谱共享可有效提高资源利用率，抑制干扰，扩大系统容量。针对自扰和串扰规避，本文在空域和频域设计相应的频带占用机制。算法中会话速率定义为随机变量。为降低链路中断概率，提高服务质量，经济数学中风险投资策略约束链路中断的不确定性。资源重构与鲁棒路由问题建立为一类具有不确定约束的混合整数非线性规划问题，仿真实验给出CR网络的状态演进过程。更多还原

【Abstract】 The acquirement of location information in wireless sensor networks (WSNs) mainlyconsists of both the estimation of target location and acquisition of nodes’ own locations innetworks. The dissertation mainly addresses the single target tracking problem inhierarchical network topology. Based on the prediction technology for time series data, thesensor nodes relevant to tracking are designed with scheduling strategy and awakeningscheme, and the corresponding recovery approach is also supplied when target tracking isfailed. As to the node localization problem, when mobile anchor is used, estimation ofunknown node’s location is converted to an unconstrained optimization problem withobjective of finding the shortest distance to the hyperspheres or hyperplanes.In addition, routing design plays an important role in designing protocols for WSNs.According to different research objects, the data centric routing problems in WSNs can beclassified into route discovery, relay selection, and link rate allocation, etc. In recent years,cross-layer design is incorporated with wireless networks naturally, because cross-layerdesign is simple and flexible to the philosophy of routing design, and also as it isapplicable to wireless network structure and application environment. In the paper, routingprotocols applicable in WSNs are proposed in chapter4to chapter6. Particularly, inchapter6, joint design of resource reconfiguration and robust routing is studied throughcross-layer design, and the designing is also adapted to the spectrum agile wireless sensornetworks. The main results are summarized as follows:Firstly, in problem of target localization, node set relevant to tracking is defined atfirst. According to the historical trajectories of target, high order exponential smoothingforecasting model is applied to waken the detection nodes dynamically. The cluster headsare responsible for scheduling sensing tasks of their member nodes, while operations ofon-site cluster head can be transferred to its downstream cluster head.Secondly, the aforementioned approach of target localization assists us in obtainingsolution to the node localization problem. Similar to the stated target localization problem, herein the mobile target is regarded as mobile anchor, while member nodes arecorrespondingly deemed as unknown nodes. Under the described assumptions, interactivemode of communications between mobile anchor and unknown nodes are hencedeveloped. In the mean time, by setting the beacon threshold, speed of mobile anchor canbe automatically adjusted. Furthermore, according to the mobile anchor’s randommovement behavior, it has been proved that the localizing algorithm can still be energyefficient. At last, the program algorithm flow and simulation results are used to verify thelocalization performances as parameters vary.Finally, as to the routing problems in WSNs, we study the heuristic routing problemunder guarantee of QoS to achieve the multi-objective routing design. The selectionprobability of potential relay in two-hop range is defined based on the extended energycost metric. Further, node set of one/(two)-hop neighbors is updated according to theminimum value of tolerable delivery velocity. As to transmission reliability, relationsbetween link quality indicator and transmission reliability predict the packet reception rateat the receiver on the available link.When routing is introduced into the programming methods, we study the joint designproblem of hierarchical topology control and link rate allocation in cluster. In theformulated problem, affiliation relationships are modeled as the discrete variables, whilethe link rates in cluster are represented by continuous variables, and both of which aresubject to the objective of network lifetime maximization. Sequential fixing approach isused to solve the formulated combinatorial optimization problem, by which sub-optimalaffiliation relationships are hence obtained. By compared with the simulation results, it isfurther confirmed that the sub-optimal solutions well approximate to the global optimalsolutions.As to the wireless cognitive radio networks, spectrum sharing can effectively raisethe utilization rate of resources, suppress interferences, and expand the network capacity.Considering the avoidance of self-interferences and co-channel interferences, the paperdesigns band occupancy scheme in both the space and frequency domains. In thealgorithm, session rates are defined as random variables. In order to decrease theprobability of link outage and improve quality of service, portfolio of financial instruments in economic mathematics is applied to regulate the uncertainty of link outage.The joint consideration of resource reconfiguration and robust routing can be formulatedas a mixed integer non-linear programming problem with uncertain constraint, andevolutionary process of CR network states is given in the simulation experiment.更多还原