无线传感器网络中一种拥塞感知的多路径流量分配算法

拥塞控制是无线传感器网络中的一个关键问题,拥塞不但增加丢包率,影响传输可靠性,而且会浪费宝贵的能量资源。本文从保证数据传输可靠性的多路径路由出发,提出了 一个拥塞感知的多路径流量分配算法COTA。COTA从全网出发,基于路径的启发式信息分配流量,避免给潜在的热点区域分配过多负载。此外,繁忙节点使用可靠性相关的报文文调度策略有效保证高可靠报文优先使用带宽。详细的模拟实验表明了COTA在可靠性、吞吐率等方面的优势。     

无线传感器网络的拥塞控制技术

无线传感器网络的多对一通信方式、无线链路的相互干扰、网络的动态变化和资源受限等特性,使得无线传感器网络容易出现拥塞,严重影响网络的QoS传输性能和生存周期,因此拥塞控制成为无线传感器网络服务质量保障机制的关键技术之一.在分析无线传感器网络特点的基础上,说明了拥塞检测和拥塞避免的策略,重点介绍和分析了基于速率控制、流量调度和传输调度等典型的拥塞解除算法,最后对拥塞控制技术的发展趋势进行了展望.     

一种无线传感器网络的优化速率控制方法

拥塞控制是无线传感器网络中的一个关键性问题。从解决拥塞问题出发,提出了一个基于优化速率的拥塞控制算法ORCC。算法构建了一个分布式的分簇网络结构,利用缓冲的占用情况进行拥塞检测,并使用基于优化理论的速率调节策略来保证网络吞吐量的稳定,从而通过求解出的最优解来实现簇内节点效用的最大化。仿真实验表明,ORCC算法不仅能有效缓解网络拥塞,降低平均延迟,还具有较好的网络传输公平性。     

无线传感器网络能源有效的拥塞控制算法

提出了一种能源有效的无线传感器网络拥塞控制算法。该算法基于缓冲队列使用情况进行拥塞检测,通过扩大拥塞反馈消息的覆盖范围来提高拥塞的缓解速度,使用倍数降低、线性增加的速率调节策略来保证网络吞吐量的稳定,并且在节点拥塞时根据一定策略丢弃数据包以提高网络传输的公平性。仿真实验表明,提出的算法不仅能有效地缓解网络拥塞、降低网络丢包率,还具有较好的能源有效性和网络传输的公平性。     

基于云模型的无线传感器网络拥塞及速率控制策略

针对无线传感器网络拥塞带来的网络丢包、能量损耗等问题,提出了一种基于云模型的无线传感器网络拥塞及速率控制策略。节点周期性地计算本地队列拥塞度,利用云模型的模糊随机性控制速率调节因子来决策节点输入速率,再通过公平性策略进行上游节点及本地速率分配。仿真实验结果表明,本文的拥塞及速率控制机制具有良好的公平性和节能性,能够有效缓解网络拥塞,降低网络丢包率,延长网络寿命。     

网络中的拥塞避免控制模型的仿真分析

研究延迟网络中的拥塞避免控制方法.在延迟网络中,随机分配网络资源,造成延迟网络资源的局限性较强,容易造成延迟网络拥塞.传统的拥塞避免控制方法是根据网络链路的反馈回执进行拥塞控制的,但是由于延迟网络的链路均匀性较差,无法建立准确的拥塞反馈回执,造成拥塞避免控制的效果较差.为了避免上述缺陷,提出了一种加权排队控制算法的延迟网络拥塞避免控制模型.对延迟网络参数进行训练,计算延迟网络的带宽,从而为延迟网络的拥塞避免控制提供准确的数据基础.建立加权排队控制模型,能够实现延迟网络的拥塞避免控制.实验结果表明,利用改进算法进行延迟拥塞避免控制,能够有效提高延迟网络的数据传递效率.     

无线传感器网络中一种避免节点拥塞的算法

无线传感器网络节点拥塞导致节点丢弃大量的数据包,这不仅影响了网络服务质量,还浪费了节点宝贵的能量,进而缩短了网络生命周期.提出了一种避免传感器网络节点拥塞的算法.该算法包含了基于发送窗口分配的拥塞避免和基于优先级的数据包调度策略.网络节点首先根据一定策略为上一跳节点分配发送窗口来预防本地发生拥塞,获得发送窗口的上一跳节点每次选择优先级最高的数据包发送以改善网络服务质量.模拟实验表明,提出的算法具有良好的能量有效性,有效地避免了由节点缓冲区溢出造成的网络丢包,同时改善了网络传输的公平性并降低了网络的平均延迟.     

基于虚拟网格的拥塞控制及能耗均衡路由协议

本文提出了一种基于虚拟网格划分的拥塞控制和能量平衡路由协议(congestion control and energy balance,CCEB),该协议利用多主节点、多路径地选取和更新方法,有效地均衡了网络能耗,延长了网络生命周期.在路径选择过程中,该协议综合考虑了下一跳节点的剩余能量、队列占有率以及前向度量指标,使靠近Sink节点的网格内拥有更多的转发主节点.在数据转发阶段,利用预测的方法对各节点进行拥塞检测和能量判断,避免使用发生拥塞或者剩余能量较少的节点转发数据.最后,通过NS2仿真平台实验,验证了CCEB协议在均衡能耗和缓解拥塞方面的有效性.     

WSN导航中的拥塞控制算法

针对无线传感器网络导航中出现的拥塞问题,提出一种拥塞检测和缓解算法。每个节点周期检测其导航的用户数量,根据用户数量是否超过上限判断其所在路径是否发生拥塞,若发生拥塞,采用拥塞缓解算法,导航相应用户到其他路径,从而使用户更加快速、安全地逃离危险区域。仿真实验结果表明,该方法在导航时能有效控制网络拥塞,使路径负载更加平衡。     

一种实用的传感器网络拥塞控制算法*

传感器网络的拥塞不仅导致丢包率增加,影响传输性能,还浪费宝贵的能量资源,因此,有效控制拥塞是传感器网络中需要解决的一个关键问题。提出了一种拥塞控制机制（CMCS）。与现有工作不同,CMCS采用基于节点的缓存队列长度并结合拥塞增长系数来判断拥塞的变化趋势,以拥塞节点为中心的共享信道区域内进行拥塞反馈和速率控制,并采用基于剩余价值的丢包策略。仿真结果表明,CMCS明显降低了通信能耗,提高了网络的价值吞吐量。     

基于累积时延的流媒体传输模糊拥塞控制

为保证网络流媒体传输质量,在流媒体的传输中需要采用有效的拥塞控制策略.结合流媒体数据对时延敏感的特点,提出了一种基于累积时延的模糊拥塞控制算法,该算法在流媒体数据流传输过程中检测和跟踪其时延,在转发分组数据前,根据容忍时延阈值,丢弃超时数据包,减少不必要的带宽浪费,并且对所到达的数据流按照累积时延进行优先级分类,把全局性缓冲区和各队列的局部性缓冲区按照正常、拥塞避免和拥塞的规则划分为3个具有交叉过渡域的阶段,然后采用整体和局部相结合的拥塞控制方法,实现队列调度过程中的模糊处理,从而对网络拥塞进行有效的控制.理论分析和实验结果表明,使用基于累积时延的模糊拥塞控制算法,能有效改善流媒体的传输性能,是解决流媒体传输拥塞控制的有效途径,并能对提高网络性能起到重要作用.     

基于ARMA预测的无线监控系统可靠传输机制

无线监控网络易受衰减、多径、盲区等不利因素影响,经常出现局部节点负载过大而导致拥塞现象的发生,导致数据可靠传输得不到保障.从拥塞控制角度,提出了一种基于拥塞预知的改进的拥塞控制算法.该算法基于主动避免拥塞的设计思想,在选取路由时考虑链路质量指标LQI,并依据ARMA预报算法获得未来时刻的网络流量预报值,由此判断节点的拥塞程度以便预先采取措施,最终实现拥塞自适应控制.实验结果表明,该算法能提升无线监控系统数据传输的可靠性,并能显著提高吞吐量.     

一种改进的主动拥塞控制算法

在已有的主动网络拥塞控制机制(ACC)中,通过丢包的方式缓减拥塞。TCP拥塞控制机制起主导作用,但不能满足现有网络环境要求。该文提出一种改进的ACC算法,该算法将拥塞控制参数嵌入到每个主动包中,通过驻留在主动路由器中的拥塞控制代码,对参数进行相应修改,从而达到拥塞预防和控制的目的。仿真结果表明,该方法具有较好的网络带宽利用率和公平性,丢包概率接近0。     

一种基于模糊神经网络的可靠流量控制模型

为控制P2P流量，本文从数据缓冲区使用的实时状态出发，提出了一种基于模糊神经网络的拥塞控制模型，该模型把缓冲区划分为两个队列分别存放P2P和非P2P的数据包，通过模糊神经网络预测评估缓冲区队列的拥塞状况，并建立一个评估函数对各队列的空间分配作出指导，使得能够控制各队列的拥塞状况，并动态的调整缓冲区队列的分配，在缓冲区溢出前主动丢包，避免缓冲区锁定。模拟实验的结果表明，该模型在保证网络资源分配的公平性方面取得了较好的效果，它降低了数据包排队延时和丢包率，提高了路由器处理网络拥塞的能力。     

An FEC scheme combined with weighted scheduling to reduce multicast packet loss in IPTV over PON

Congestion is one of the most important challenges in optical networks. In a Passive Optical Network (PON), the Optical Line Terminal (OLT) is a bottleneck and congestion prone. In this paper, a framework is proposed with Forward Error Correction (FEC) at the IP layer combined with Weighted Round Robin (WRR) at the scheduling level to overcome packet-loss due to congestion in the OLT in order to achieve efficient video multicasting over PON. In the FEC scheme, Reed-Solomon (RS(n,k)) with erasure coding is used, where (n−k) erroneous symbols per n symbol blocks can be corrected. In our framework, an Internet Protocol TeleVision (IPTV) service provider uses the mentioned RS coding and generates redundant packets from regular IPTV packets in such a way that an Optical Network Unit (ONU) can recover lost packets from received packets, thus resulting in a better video quality. Simulation results show that using the proposed framework, an ONU can recover many lost packets and achieve better video quality under different traffic loads for its users. For instance, the proposed method can reduce packet loss rate by almost 55% and 10% under traffic load 0.9, respectively, compared with the Round Robin (RR) and WRR methods under symmetric traffic load. When High Receivers Queue (HRQ) traffic (i.e., traffic received by many users) is twice Low Receivers Queue (LRQ) traffic (i.e., traffic received by a small number of users), this reduction is almost 86% and 30% under traffic load 0.9. Finally, when LRQ traffic is twice HRQ traffic, the reduction in packet loss rate is almost 70% and 91% at traffic load 0.5.     

基于全分布式分组无线网络的蚂蚁路由算法

该文介绍了全分布式分组无线网络中路由算法。当前广泛运用的OSPF路由算法在网络的链路发生阻塞时，不能及时探测到链路状况。当链路发生拥塞时，在发包率继续增大的时候，只能简单地丢弃数据包。就此该文在分布式的无线网络中应用了蚂蚁算法。该算法由发送探测包来担任“觅食蚂蚁”在短时间内找出最优路径，分散流量，避免网络的拥塞。通过仿真结果表明：该算法在网络利用率和数据包传送时延上有较好的改善。     

Congestion control of packet-switched networks with three types of input buffer limits

Congestion in a store-and-forward data communications network manifests itself by a rapid degradation of network throughput and a fast increase in the average transit delay. A study of a congestion control mechanism in a packet-switched data network is presented. Buffer limits are placed on the input, transit and exit packets in each switching node. By a judicious choice of the buffer limits, it is shown that the network throughput can be maintained at a constant level, while the average transit delay remains within acceptable bounds for offered loads beyond the congestion point.     

Congestion control in wireless sensor networks based on bird flocking behavior

This paper proposes that the flocking behavior of birds can guide the design of a robust, scalable and self-adaptive congestion control protocol in the context of wireless sensor networks (WSNs). The proposed approach adopts a swarm intelligence paradigm inspired by the collective behavior of bird flocks. The main idea is to ‘guide’ packets (birds) to form flocks and flow towards the sink (global attractor), whilst trying to avoid congestion regions (obstacles). The direction of motion of a packet flock is influenced by repulsion and attraction forces between packets, as well as the field of view and the artificial magnetic field in the direction of the artificial magnetic pole (sink). The proposed approach is simple to implement at the individual node, involving minimal information exchange. In addition, it displays global self-1 properties and emergent behavior, achieved collectively without explicitly programming these properties into individual packets. Performance evaluations show the effectiveness of the proposed Flock-based Congestion Control (Flock-CC) mechanism in dynamically balancing the offered load by effectively exploiting available network resources and moving packets to the sink. Furthermore, Flock-CC provides graceful performance degradation in terms of packet delivery ratio, packet loss, delay and energy tax under low, high and extreme traffic loads. In addition, the proposed approach achieves robustness against failing nodes, scalability in different network sizes and outperforms typical conventional approaches.     

FLRED: an efficient fuzzy logic based network congestion control method

The number of applications running over computer networks has been increasing tremendously, which increased the number of packets running over the network as well leading to resource contention, which ultimately results in congestion. Congestion increases both delay and packet loss while reducing bandwidth utilization and degrading network performance. Network congestion can be controlled by several methods, such as random early detection (RED), which is the most well-known and widely used method to alleviate problems caused by congestion. However, RED and its variants suffer from linearity and parametrization problems. In this paper, we proposed a new method called fuzzy logic RED (FLRED), which extends RED by integrating fuzzy logic to overcome these problems. The proposed FLRED method relies on the average queue length (aql) and the speculated delay (D _Spec ) to predict and avoid congestion at an early stage. A discrete-time queue model is used to simulate and evaluate FLRED. The results showed that FLRED outperformed both RED and effective RED (ERED) by decreasing both delay and packet loss under heavy congestion. Compared with ERED and RED, FLRED decreased the delay by up to 1.5 and 4.5% and reduced packet loss by up to 6 and 30%, respectively, under heavy congestion. These findings suggest that FLRED is a promising congestion method that can save network resources and improve overall performance.