基于LIP和RSC的超立方体网络单播容错路由算法*

基于LIP和RSC的概念,提出了一个有效的超立方体网络单播容错路由算法.该算法不仅能容纳指数级的错误节点,而且算法效率也很高.     

基于事件空间划分的高效发布订阅路由算法*

传统的逆向路径转发的路由效率是O(N),基于事件空间划分的贪婪路由技术将效率提高到O(N1/d).在此基础上,采用祖先队列的路由数据结构,建立虚拟层叠网络中不同路由域之间的相邻关系,并通过祖先队列记录域间代理的相邻关系,实现了分层分路由域的代理之间的分级跨跳路由,称为Spanhop路由.通过性能分析表明,使用该路由算法,路由的平均路径减少到O(ln N),同时取消了事件空间维度d对路由效率的影响.这种方法通过增加少量的存储代价,提高了在大规模的面向广域网的发布订阅系统当中的路由效率.     

路径长度受限的随机需求VRP的模型和算法

路径长度受限的随机需求VRP在生产、生活中有着广泛的应用。给出了路径长度受限的随机需求VRP问题的线性整数规划模型,在不允许部分服务和仅能服务失败一次的策略下设计了一个启发式算法。该算法以离散优化中广泛应用的扫描算法为基础生成服务的一个初始可行方案,然后利用模拟退火算法改进得到近似最优解。对需求为二项分布的50个结点、1个服务中心的问题进行了数值试验,数值结果表明该算法对求解路径长度受限的随机需求VRP是有效的。     

基于能量优化的LEACH路由协议改进

针对LEACH路由算法中簇头选举随机性和簇头与基站直接通信导致能量消耗过快且不平衡的特点,提出新的改进算法,以达到降低能耗目的。在改进算法中,簇头剩余能量高于网络平均能量。根据簇头节点与基站的相对位置划分不同区域,簇头节点发送数据采用多跳方式,避免簇头节点能量消耗过快,达到平衡网络能量消耗的目的。仿真表明,通过改进簇头选举条件和采用多跳路由的方式,即使在数据通信量增加的情况下,依然能够延长网络通信时间。     

On reactive routing protocols in ZigBee wireless sensor networks

ZigBee is the primary standard solution for wireless sensor networks, implementing the Ad hoc On‐Demand Distance Vector (AODV) protocol in the network layer and supported by the standard IEEE 802.15.4. This study is focused on mesh topologies and the critical problems encountered when AODV is executed in conjunction with the Carrier Sense Multiple Access with Collision Avoidance protocol. These problems are mainly related to the packet overhead required to carry out route creation. To perform preliminary experiments to be able to implement AODV in a real network, a new metric is proposed herein. This metric uses fuzzy logic to help in the decision‐making process. The objective of the fuzzy routine is to determine, during the route‐discovery process, the best node to forward request/reply packets, with the aim of reducing packet overhead and energy consumption. Moreover, minor changes are also added to the discovery procedure of AODV to improve the performance of the route‐creation process. This intelligent version of AODV has provided promising experimental results, greatly reducing the number of packets required, with the consequent energy saving while selecting the best nodes to be part of the routes.     

Joint routing and location-based service in VANETs

Geographic routing protocols use location information when they need to route packets. In the meantime, location information are maintained by location-based services provided by network nodes in a distributed manner. Routing and location services are very related but are used separately. Therefore, the overhead of the location-based service is not considered when we evaluate the geographic routing overhead. Our aim is to combine routing protocols with location-based services in order to reduce communication establishment latency and routing overhead.     

基于MIKE FLOOD的洪泽湖周边滞洪区洪水演进模拟研究

基于实测断面资料建立了研究区的一维水动力模型,基于高精度DEM以及1∶10000地形图建立了研究区的二维水动力模型,并用MIKE FLOOD将一维模型和二维模型进行耦合,构建了洪泽湖周边滞洪区一、二维耦合的洪水演进数学模型。利用2003年历史洪水资料对模型参数进行了率定,并以2007年历史洪水资料进行了验证。以洪泽湖百年一遇设计洪水为模型上边界,二河闸、三河闸以及高良涧闸的现行调度方案的水位-流量关系为模型下边界,对洪泽湖百年一遇设计洪水方案进行模拟计算,当蒋坝水位达到14.33m时,洪泽湖周边滞洪区开始滞洪,得到开始滞洪后不同时段研究区内各类洪水风险要素的动态分布情况以及最大淹没水深、淹没历时,验证了模型的合理性,可用于蓄滞洪区洪水演算分析。     

面向车载网的基于AODV优化路由协议的研究

车载网VANETs(Vehicular ad hoc networks)属于新型的通信模型,其可实现车间通信和车与基础设施通信。由于VANETs节点密度动态变化,节点的快速移动、移动模型的局限性等特点,为VANETs的数据传输提出挑战。鉴于此,提出优化的按需距离矢量AODV(Ad Hoc On demand Distance Vector)方案IAODV(Improved AODV)。在城市环境中,AODV在端到端传输时延、数据包丢失率方向性能较差。为此,IAODV采用限定源节点的路由跳数,降低AODV的端到端传输时延,同时为源节点提供备份路由,从而改善数据包丢失率。仿真结果表明,与AODV协议相比,IAODV在端到端传输时延、数据包丢失率得到有效提升。     

A robust optimization approach for pollution routing problem with pickup and delivery under uncertainty

Organizations have recently become interested in applying new approaches to reduce fuel consumptions, aiming at decreasing green house gases emission due to their harmful effects on environment and human health; however, the large difference between practical and theoretical experiments grows the concern about significant changes in the transportation environment, including fuel consumptions, carbon dioxide (CO₂) emissions cost and vehicles velocity, that it encourages researchers to design a near-reality and robust pollution routing problem. This paper addresses a new time window pickup-delivery pollution routing problem (TWPDPRP) to deal with uncertain input data for the first time in the literature. For this purpose, a new mixed integer linear programming (MILP) approach is presented under uncertainty by taking green house emissions into consideration. The objective of the model is to minimize not only the travel distance and number of available vehicles along with the capacity and aggregated route duration restrictions but also the amount of fuel consumptions and green house emissions along with their total costs. Moreover, a robust counterpart of the MILP is introduced by applying the recent robust optimization theory. Computational results for several test problems indicate the capability and suitability of the presented MILP model in saving costs and reducing green house gases concurrently for the TWPDPRP problem. Finally, both deterministic and robust mathematical programming are compared and contrasted by a number of nominal and realizations under these test problems to judge the robustness of the solution achieved by the presented robust optimization model.     

改进蚁群算法在应急VRP中的应用及收敛性分析

应急条件(地震、泥石流、洪水等)下的路况不稳定,随时会发生意外的问题.为了解决应急条件下的物资分配,在研究蚁群算法解决传统VRP(vehicle routing problem)的基础上,通过加入动态路况子对VRP数学模型进行改进,提出蚁群算法对改进后的车辆路路径问题的数学模型,并利用陕西省21个城市的实际经纬度作为应急情况下的模拟货物需求地点和仓库进行系统仿真,对影响改进算法收敛性的参数进行分析.通过理论研究和MATLAB仿真,证明了这种改进的算法能够有效解决实际情况中的应急物流分配问题.