一种适合Underlay认知无线传感网络的跨层路由算法

在Underlay认知无线传感网络（Underlay Cognitive Radio Sensor Network,Underlay CRSN）中,由于每个信道被主用户所使用的状态随机变化,设计路由算法不仅需要考虑网络中节点的能量消耗以及延长网络寿命的需求,还要考虑路由路径上各链路的信道选择策略对路径数据传输可靠性的影响。该文研究了Underlay认知无线电模型下的CRSN路由问题,提出了一种适合该路由问题的链路Utility函数,并将这类问题建模为以最小化Utility为目标的最优化问题。在讨论了几种传统的无线传感器网络（Wireless Sensor Network,WSN）路由算法之后,提出了一种适合Underlay CRSN的跨层路由算法,并给出了该算法的分布式实现方案。计算机仿真表明所提出的路由算法能很好地适应UnderlayCRSN中信道可用状态的随机变化。     

多信道无线网状网跨层优化研究

针对多网卡多信道无线Mesh网络容量问题,基于无线信道干扰模型,在给定各节点物理层发射功率的条件下,联合考虑无线Mesh网络传输层的流速控制、网络层的路由算法和MAC层的信道分配等问题,通过采用二次路由计算策略,提出了一个跨层联合优化算法,仿真结果表示,提出的算法能提高网络吞吐量。     

认知无线Ad Hoc网络干扰约束和能量高效路由算法

为了减少认知无线Ad Hoc网络的传输中断概率,实现频谱和能量高效,提出一种干扰约束和能量高效（Interference Constraints and Energy-Efficient,ICEE）的路由算法。信道检测除了基于认知节点（Cognitive Radio,CR）对主用户（Primary Users,PU）的干扰约束外,还增加了CR节点的数据传输所需持续时间约束,以保证CR节点在有效利用空闲信道的同时减少传输中断事件的发生,减少故障重传所损耗的能量。在设计路由算法时采用了链路能耗和节点寿命作为度量,通过联合最优的链路选择方程实现网络能量高效,并延长网络的生命周期。实验仿真结果表明,相比较认知Ad hoc网络的自适应路由协议,基于联合信道分配和自适应功率控制的路由协议,ICEE算法在数据包平均能耗上分别减少了41.2%和24.5%,并且有效地延长了网络生命周期。     

智能配电网通信多信道调度策略

为了有效提高基于无线传感网的配电网服务质量,进一步增强配电网中数据实时性,减少延迟,提出了一种基于优先级的多信道调度策略。首先,根据实时的信道链路状态信息,提出了基于最小跳数生成树的链路路由算法（LB-MHST）,克服了无线射频干扰,保证智能电网的服务质量;然后,针对配电网中不同数据包对于延迟要求的不同,考虑了数据优先级传输,有效地提高了感知节点的数据传输效率,进一步满足了配电网中的QoS性能要求。实验结果表明,与最小跳数生成树（MHST）算法相比,尤其是在多信道延迟感知传输中,提出算法在单信道、8信道和16信道时分别将高负载流量延迟性能提高了12%,15.2%和18%。     

一种动态传感网络中的新型路由算法

随着无线传感网络技术和其应用领域的不断发展,网络模型趋于动态化,网络节点具有移动性,这给路由算法设计带来新的挑战。该文介绍动态叶子树网络模型,针对没有定位信息的场景提出适合动态网络的DLR路由算法。该算法包含路径相似度计算以及最佳路径选择2个步骤。仿真模拟表明,DLR路由算法能够在动态网络中保证超过90%的通信可靠性。     

移动自组织认知网络中的路由选择与信道分配方法

针对移动自组织认知网络中路由不稳定的问题,提出了一种路由选择与信道分配方法。首先,设计数据传输花费度量,兼顾路由稳健性和信道干扰;其次,依据认知节点的位置和速度信息计算链路剩余时间,预测路由稳健性;再次,针对不同的信道干扰模式实施信道分配,规避主节点干扰;最后,通过路由发现和路由确认步骤,选择最佳链路。仿真实验表明,与 经典的AODV方法相比,所提方法丢包率低、传输时延小。     

一种联合多信道分配决策的认知Mesh系统数据传输优化算法

在认知Mesh系统进行数据传输的过程中,为了提高数据包投递成功率及网络的吞吐量,减少网络延迟时间,提出一种联合多信道分配决策的认知Mesh系统数据传输优化算法(JCWN)。针对信道的干扰问题,建立了认知Mesh系统的干扰无向图,分析节点链路的网络干扰电平。在节点的路由请求阶段通过提出基于信道干扰电平的路由指标函数,并通过权重阈值来为节点链路分配干扰较小的信道。在路由选择上,联合多路由算法计算每条路由路径的信道干扰程度,为了保障节点传输数据的成功率而选择干扰程度更小的路由。实验仿真结果表明,在数据包投递成功率上,该算法相比POC算法以及基于RL的算法提高了20%以上,在提高网络吞吐量,减少延迟时间上也表现出了更好地效果。     

Genetic algorithms to solve the cover printing problem

Inspired by successful application of evolutionary algorithms to solving difficult optimization problems, we explore in this paper, the applicability of genetic algorithms (GAs) to the cover printing problem, which consists in the grouping of book covers on offset plates in order to minimize the total production cost. We combine GAs with a linear programming solver and we propose some innovative features such as the “unfixed two-point crossover operator” and the “binary stochastic sampling with replacement” for selection. Two approaches are proposed: an adapted genetic algorithm and a multiobjective genetic algorithm using the Pareto fitness genetic algorithm. The resulting solutions are compared. Some computational experiments have also been done to analyze the effects of different genetic operators on both algorithms.     

基于认知无线电的QoS路由算法

一般无线网络的路由协议不适合直接应用于认知无线电网络,需要提出一个新的路由算法以满足认知无线电网络端到端的服务质量(QoS)性能。认知无线电网络中的节点可以自主地进行信道选择和频谱切换,从而最大限度地满足流量需求和避免流内竞争。结合按需路由的基本流程,在充分考虑信道容量和流内竞争所带来的干扰对路由选择的影响下,提出了适用于认知无线电网络的基于容量和干扰的路由算法。仿真结果表明,基于容量和干扰的路由算法比另两种路由算法具有更好的端到端QoS性能。     

The price of forgetting in parallel and non-observable queues

We consider a broker-based network of non-observable parallel queues and analyze the minimum expected response time and the optimal routing policy when the broker has the memory of its previous routing decisions. We provide lower bounds on the minimum response time by means of convex programming that are tight, as follows by a numerical comparison with a proposed routing scheme. The “Price of Forgetting” (PoF), the ratio between the minimum response times achieved by a probabilistic broker and a broker with memory, is shown to be unbounded or arbitrarily close to one depending on the coefficient of variation of the service time distributions. In the case of exponential service times, the PoF is bounded from above by two, which is tight in heavy-traffic, and independent of the network size and heterogeneity. These properties yield a simple engineering product-form approximating tightly the minimum response time. Finally, we put our results in the context of game theory revisiting the “Price of Anarchy” (PoA) of parallel queues: it can be decomposed into the product of the PoA achieved by a probabilistic broker (already well understood) and the PoF.     

Traffic Engineering for wireless connectionless access networks supporting QoS-demanding media applications

This paper focuses on the problem of optimal QoS Traffic Engineering (TE) in Co-Channel Interference (CCI)-affected power-limited wireless access networks that support connectionless services. By exploiting the analytical tool offered by nonlinear optimization and following the emerging “Decomposition as Optimization” paradigm [1], the approach pursued in this paper allows to develop a resource allocation algorithm that is distributed, asynchronous, scalable and self-adaptive. Interestingly, the proposed algorithm enables each node of the network to distribute its outgoing traffic among all feasible next-hops in an optimal way, as measured by an assigned global cost function of general form. This optimal traffic distribution complies with several subjective as well as objective QoS requirements advanced by the supported media flows and involves only minimum information exchange between neighboring nodes. Furthermore, it allows for load-balanced multiple forwarding paths and it is able to self-perform optimal traffic re-distribution (i.e., re-routing) in the case of failure of the underlying wireless links. Finally, actual effectiveness of the overall proposed algorithm is numerically tested via performance comparisons against both DSDV-based single-path routing algorithms and interference-aware multipath routing algorithms.     

一种SRIO网络负载均衡最短路径路由算法

在串行RapidIO传输过程中,路由选路算法是影响传输性能的重要因素之一。针对串行高速输入-输出(SRIO)网络深度优先搜索分配路径非最优问题,提出一种负载均衡最短路径路由算法。通过广度优先搜索对SRIO网络中的节点进行枚举并建立网络拓扑信息,以路由跳数定义路由的成本,根据改进Floyd-WarShall算法计算并保存交换节点间的K最短路径。给出预期负载的概念和链路上的路由路径数量来定义链路的负载,采用负载均衡算法从K最短路径中进行选路,建立SRIO网络最短路径约束的负载均衡路由。实验结果表明,与深度遍历路由算法、最小跳数算法相比,该算法在网络传输平均跳数、链路平均负载和链路负载均衡方面有更好的表现,能够有效提升SRIO路由网络的稳定性。     

基于萤火虫群优化的Ad Hoc网络路由协议

针对Ad Hoc网络拓扑结构多变、网络生存时间受限及数据包分组传输效率低下等问题,本文借鉴萤火虫群优化算法的思想,提出了一种基于萤火虫群优化的Ad Hoc网络路由协议。路由协议用萤火虫优化算法的荧光素强度的更新规则与无线自组网络中的节点移动速度、拥塞程度、节点剩余能量及节点间的距离等因素相互映射,改进萤火虫群优化算法中的搜索萤火虫、驻留萤火虫及回溯萤火虫用于完成Ad Hoc网络中路由协议的路由发现、路由选择及路由维护等过程,整个协议无须传送大量的控制分组,即可实现Ad Hoc网络的稳定。仿真实验结果表明,与AODV及基于蚁群优化的路由算法AntRouting协议相比,本文所提出的路由协议在端到端延时、分组数据传输率及网络生存时间上均有良好的性能。     

结合混合式信道分配的无线MESH多路径路由协议

为了优化无线Mesh网络中的多接口多信道网络资源,对单接口多路径路由协议和单路径信道分配策略进行了深入的分析研究,提出了与混合式信道分配方式相结合的多路径路由协议(AODV-MP),实现了网络中多条路径的并行传输,从而提高网络的整体吞吐率。并根据多路径路由协议自身的特性,结合与邻居节点干扰相关的因素——信噪比(SNR),提出了多路径路由的路由度量判决WCETTSNR,同时将该路由度量作为负载流量分配的比例标准。利用NS2网络仿真平台对该多路径路由协议进行测试,结果显示AODV-MP在网络吞吐率上提高了45%,并在高负载情况下,较大幅度地减少了网络延时。     

Delay efficient scheduling via redundant constraints in multihop networks

We consider the problem of delay-efficient scheduling in general multihop networks. While the class of max-weight type algorithms are known to be throughput optimal for this problem, they typically incur undesired delay performance. In this paper, we propose the Delay-Efficient SCheduling algorithm (DESC). DESC is built upon the idea of accelerating queues (AQ), which are virtual queues that quickly propagate the traffic arrival information along the routing paths. DESC is motivated by the use of redundant constraints to accelerate convergence in the classic optimization context. We show that DESC is throughput-optimal. The delay bound of DESC can be better than previous bounds of the max-weight type algorithms which did not use such traffic information. We also show that under DESC, the service rates allocated to the flows converge quickly to their target values and the average total “network service lag” is small. In particular, when there are O(1) flows and the rate vector is of Θ(1) distance away from the boundary of the capacity region, the average total “service lag” only grows linearly in the network size.     

A high performance optimal dynamic routing algorithm with unicast multichannel QoS guarantee in communication systems

Dynamic routing protocols play an important role in today??s networks. In communication networks, in a current data transmission session, failing nodes and links is a destructor event which loses packets immediately and it can also waste network resources and services seriously. Sometimes failing nodes can disconnect data transmission and, therefore, lost packets must be retransmitted by new session. In this situation, the routing algorithm must discard failed nodes and must repair paths of session by rerouting them. In this case, static routing algorithms and some existing dynamic routing algorithms cannot manage faulty paths fairly and network efficiency is seriously declined. The capability to compensate for topology changes is the most important advantage dynamic routing offers over static routing. An efficient dynamic routing algorithm tries to reroute and change faulty paths without disconnecting sessions and keeps packet transmission in a desirable rate. It is important to tell that a dynamic routing algorithm should provide multi essential parameters, such as acceptable delay, jitter, bandwidth, multichannel paths, virtual channel connections, label switching technology, optimal resource allocation, optimal efficiency in the case of multimedia, and real time applications. This paper proposes a new dynamic framework which transforms static routing algorithms to dynamic routing algorithms. Using the new dynamic framework, this paper constructs an Optimal Dynamic Unicast Multichannel QoS Routing (ODUMR) algorithm based on the Constrained Based Routing (CBR) and Label Switching Technology which is called as ODUMR Algorithm. The performance of ODUMR is analyzed by network simulator tools such as OpNet, MATLAB, and WinQSB. ODUMR produces results better than the existing static and dynamic routing algorithms in terms of necessary parameters.     

节能感知的无线传感网接入控制与路由优化策略

为降低能耗,延长输电线路监测网络传感器寿命,提出一种新的媒体接入控制与路由联合优化策略。构建无线传感网通信框架,并基于该框架给出一种自适应的簇内调度策略,旨在减少传感器节点的空闲监听,从而降低节点能耗。给出一种按需路由协议,在确保能量等级和信道质量的同时在簇间进行最佳路由选择,基于簇头剩余能量及其到基站的距离,利用非均匀簇技术平衡节点能量分布,延长网络寿命,并构建能耗和延迟模型进行性能评估。实验结果表明,该方案在节能的同时能够显著降低数据传输时延。     

IEEE 802.21-based seamless multicast streaming with dynamic playback control

With the explosion of video streaming available on the Internet, online multimedia applications become more popular in our days and the video quality of the Internet multimedia applications is directly affected by the network transmission state, which will be worse while switching the ongoing network connection from one wireless interface to another heterogeneous wireless technology, such as IEEE 802 families, UMTS (universal mobile telecommunications system) network and 3GPP LTE (3rd generation partnership project-long term evolution). In order to perform seamless handover between heterogeneous wireless networks, IEEE group proposed the “IEEE 802.21 standard” and defined a middleware function called “media independent handover function (MIHF)” to smooth the handover. In this paper, a dynamic playback control for multicasting streaming based on IEEE 802.21 is proposed to reduce the influence of handover between heterogeneous networks. In addition, we evaluate three different rate modes for seamless multicasting streams. The simulation results show that different playback frame rate modes, including the usual, incremental and linear modes, can achieve different video quality and can extend the playing time for handover video playback.     

基于能耗量化传导的WSN路由探测算法

为了降低无线传感器网络(WSN)路由节点的能量损耗,提高网络的寿命周期,需要进行路由节点的优化分布设计。传统方法采用CSMA/CA有限竞争的信道分配模型进行WSN的路由探测算法设计,实现能量均衡,在节点规模较大和干扰较强时,节能的能耗开销较大。提出一种基于能耗量化传导的WSN路由探测算法,首先建立WSN的分簇能耗调度模型,以能量控制开销、丢包率、传输时延等为约束参量指标进行路由探测的控制目标函数的构建,然后采用路由冲突协调机制进行能耗量化分配,结合WSN传输信道的能量传导均衡模型实现WSN路由的优化探测和WSN节点的优化部署。仿真结果表明,采用该方法进行WSN路由探测设计时网络的能效较高,传输时延和误码率等参量指标的表现优于传统方法。     

On the complexity of working set selection

The decomposition method is currently one of the major methods for solving the convex quadratic optimization problems being associated with Support Vector Machines (SVM-optimization). A key issue in this approach is the policy for working set selection. We would like to find policies that realize (as well as possible) three goals simultaneously: “(fast) convergence to an optimal solution”, “efficient procedures for working set selection”, and “a high degree of generality” (including typical variants of SVM-optimization as special cases). In this paper, we study a general policy for working set selection that has been proposed in [Nikolas List, Hans Ulrich Simon, A general convergence theorem for the decomposition method, in: Proceedings of the 17th Annual Conference on Computational Learning Theory, 2004, pp. 363–377] and further analyzed in [Nikolas List, Hans Ulrich Simon, General polynomial time decomposition algorithms, in: Proceedings of the 17th Annual Conference on Computational Learning Theory, 2005, pp. 308–322]. It is known that it efficiently approaches feasible solutions with minimum cost for any convex quadratic optimization problem. Here, we investigate its computational complexity when it is used for SVM-optimization. It turns out that, for a variable size of the working set, the general policy poses an NP-hard working set selection problem. But a slight variation of it (sharing the convergence properties with the original policy) can be solved in polynomial time. For working sets of fixed size 2, the situation is even better. In this case, the general policy coincides with the “rate certifying pair approach” (introduced by Hush and Scovel). We show that maximum rate certifying pairs can be found in linear time, which leads to a quite efficient decomposition method with a polynomial convergence rate for SVM-optimization.