基于协同路由算法的无线传感器网络能量空洞避免

在多跳的无线传感器网络中,靠近sink的节点由于需要转发来自外层网络的数据,其能量消耗速度快于离sink较远的节点,从而导致了"能量空洞"的出现,最终导致网络过早死亡.本文在分析现有路由算法的基础上,针对EEUC算法的不足,对其进行了改进,提出了多跳无线传感器网络中的协同路由算法.在路由节点选择时不局限于簇头作为路由节点,还可以将成员节点作为路由节点,让其分担了簇头的能量消耗.算法引入了距离因子和能量因子,既考虑候选路由节点相对于sink的距离,同时还考虑候选路由节点的剩余能量,以达到选择最佳路由节点的目的.仿真结果表明,所改进路由算法在网络生存时间,能耗均匀程度方面优于现有算法.     

基于能量均衡的无线传感网络路由算法

有效的簇首选择策略可提高无线传感器网络（WSN）的负载均衡和簇首均匀分布程度,延长网络生存时间.现有算法在选取簇首时未充分考虑节点剩余能量和能耗速率,导致负载不均衡,部分节点因耗能过度而提前消亡.为此,本文提出一种基于能量均衡的WSN路由算法,该算法使用节点剩余能量、能量消耗速度、与Sink节点的距离作为簇首选择参数,并通过回退机制实现节点回退等待时间的自适应调整,从而保证高能量节点有更高机率成为簇首.实验表明,该算法可有效避免“热区”的出现,延长网络寿命     

内容发布订阅系统的路由优化和负载均衡技术

研究在非均匀数据分布情况下的负载均衡和大规模系统中的事件路由效率问题.使用K-D树划分结构实现了内容发布/订阅系统的静态负载均衡.在此基础上,利用K-D树的索引机制,实现了系统事件代理之间的分层路由机制,将路由效率提高到O(lnN).基于区域匹配和路由2种负载特征,采用区域分裂和复制方法实现代理之间的负载调度.通过转移加入和动态探测的方法解决了动态负载均衡问题.性能分析和仿真实验表明,上述方法解决了基于事件空间划分的内容发布/订阅系统的高效路由和负载均衡问题.     

Bounding the inefficiency of logit-based stochastic user equilibrium

Bounding the inefficiency of selfish routing has become an emerging research subject. A central result obtained in the literature is that the inefficiency of deterministic User Equilibrium (UE) is bounded and the bound is independent of network topology. This paper makes a contribution to the literature by bounding the inefficiency of the logit-based Stochastic User Equilibrium (SUE). In a stochastic environment there are two different definitions of system optimization: one is the traditional System Optimum (SO) which minimizes the total actual system travel time, and the other is the Stochastic System Optimum (SSO) which minimizes the total perceived travel time of all users. Thus there are two ways to define the inefficiency of SUE, i.e. to compare SUE with SO in terms of total actual system travel time, or to compare SUE with SSO in terms of total perceived travel time. We establish upper bounds on the inefficiency of SUE in both situations.     

A tabu search heuristic for the split delivery vehicle routing problem with production and demand calendars

The purpose of this article is to propose a tabu search heuristic for the split delivery Vehicle Routing Problem with Production and Demand Calendars (VRPPDC). This new problem consists of determining which customers will be served by a common carrier, as well as the delivery routes for those served by the private fleet, in order to minimize the overall transportation and inventory costs. We first model this problem and then propose a simple decomposition procedure that can be used to provide a starting solution. Next, we introduce a new tabu search heuristic and we describe two new neighbor reduction strategies. Finally, we present the results of our extensive computational tests. According to these tests, our reduction strategies are efficient not only at reducing computing time but also at improving the overall solution quality.     

An adaptive memory methodology for the vehicle routing problem with simultaneous pick-ups and deliveries

This paper deals with a routing problem variant which considers customers to simultaneously require delivery and pick-up services. The examined problem is referred to as the Vehicle Routing Problem with Simultaneous Pick-ups and Deliveries (VRPSPD). VRPSPD is an NP-hard combinatorial optimization problem, practical large-scale instances of which cannot be solved by exact solution methodologies within acceptable computational times. Our interest was therefore focused on metaheuristic solution approaches. In specific, we introduce an Adaptive Memory (AM) algorithmic framework which collects and combines promising solution features to generate high-quality solutions. The proposed strategy employs an innovative memory mechanism to systematically maximize the amount of routing information extracted from the AM, in order to drive the search towards diverse regions of the solution space. Our metaheuristic development was tested on numerous VRPSPD instances involving from 50 to 400 customers. It proved to be rather effective and efficient, as it produced high-quality solutions, requiring limited computational effort. Furthermore, it managed to produce several new best solutions.     

On the benefits of co-collection: Experiments with a multi-compartment vehicle routing algorithm

The Multi-Compartment Vehicle Routing Problem involves clients with a demand for different products and vehicles with several compartments to co-transport these commodities. We present a local search procedure that explores well-known moves (2-opt, cross, exchange, relocate), and exploits the mechanisms of neighbour lists and marking to speed up the searches. We combine the procedure with the Guided Local Search meta-heuristic to improve solution quality. Extensive computational results are reported to uncover when co-distribution by vehicles with multiple compartments is better than separate distribution with un-partitioned trucks. Sensitivities in key problem parameters including, client density and location of the depot, vehicle capacity, client demand and number of commodities are investigated.     

车载自组织网络通信协议

 车载自组织网络（VANET）被认为是未来智能交通系统（ITS）的重要组成部分,可有效提高行车效率、安全性和舒适性。VANET属于移动自组织网络（MANET）,不依赖固定设施,其节点高速移动、网络拓扑变化频繁,具有与MANET不同的构架、特征、应用和挑战。本文综述了VANET网络构架、通信协议、建模与仿真、信息管理和云计算等方面的研究进展,展望了未来的发展方向。     

基于甚高频数据交换系统的船舶编队通信网络最优路由方案

甚高频数据交换系统(VHF data exchange system,VDES)是国际海事组织(International Maritime Organization,IMO)主导的e-航海战略与水面自主航行船舶的基本通信方式之一,计划2021年左右发布正式技术标准;其可提供船-船之间自组织高速数据交换,将有效推进船舶编队通信联网技术发展.为此,在分析海上真实船舶运动特征基础上,提出了一种船舶编队群组移动模型.以此为基础,研究了船舶编队通信网络特征,重点对基于VDES联网编队的自组织网络路由协议性能进行了仿真分析.结果 表明,相对DSR(dynamic source routing)协议及DSDV(destination-sequenced distance-vector)协议,AODV(Ad Hoc on-demand distance vector)协议在路由开销及分组投递率方面具有显著的性能优势,能更好地适应基于VDES的船舶编队通信网络,满足船舶编队通信应用需求.     

一种改进的基于Bluetrees的蓝牙散射网形成算法

论述了蓝牙散射网的发展现状及网络协议,指出Bluetrees协议算法存在的问题:Bluetrees网组建不均衡时影响系统传输均衡性、未考虑设备的移动性对散射网的影响及可靠性问题.接着对形成Bluetrees的算法进行了改进,增强了系统以及Bluetrees的均衡性,考虑了设备的移入与移出对散射网的影响以及散射网的重组,增强了散射网中数据传输的可靠性.通过对该算法的改进和完善,减小了设备的移动对系统性能的影响,提高了系统的性能.最后以车队的相互通信和指挥控制为例说明了本文算法的应用.