基于云网格集成调度的防拥堵车辆路径规划算法

在道路交通路网中,车辆拥堵问题是流量与路网结构之间相互作用的一个复杂动态过程,通过车辆路径规划,实现对路网网格集成调度,从而提高路网通行吞吐量。传统方法采用并行微观交通动态负载平衡预测算法实现车辆拥堵调度和车辆路径规划,不能准确判断路面上的车辆密度,路径规划效益不好。提出一种基于云网格集成调度的防拥堵车辆路径规划算法,即构建基于Small-World模型的云网格路网模型,采用RFID标签信息进行路况信息采集,实现交通网络拥堵评估信息特征的提取,采用固有模态函数加权平均求得各车道的车辆拥塞状态函数,对所有车道内车辆密度取统计平均可获得簇内的车辆密度。设计交通路网拥堵检测算法来对当前个体道路信息进行一维邻域搜索,从而实现车辆路径规划控制目标函数最佳寻优。通过动态博弈的方式求得车辆防拥堵路径的近似最优轨迹,实现路径规划算法的改进。仿真结果表明,该算法能准确规划车辆路径,实现最优路径控制,从而提高严重拥堵路段的车流速度和路网吞吐性能,性能优越。     

Congestion estimation for hexagonal routing

Interconnect planning is an important issue in VLSI designs. Routing congestion is an important problem during placement. This paper presents a novel approach to estimating the routing congestion on the hexagonal model with bounded-length detours. It is the first probabilistic estimation work on hexagonal topologies. A combinatorial estimation algorithm is presented. The experimental results demonstrate the effectiveness of the method.     

Competitive energy consumption under transmission constraints in a multi-supplier power grid system

Power grid architectures need to be revised in order to manage the increasing number of producers and, more generally, the decentralisation of energy production and distribution. In this work, we describe a multi-supplier multi-consumer congestion model of a power grid, where the costs of consumers depend on the congestion in nodes and arcs of the power supply network. The consumer goal is both to meet their energy demand and to minimise the costs. We show that the methods of non-atomic routing can be applied in this model in order to describe current distribution in the network. We formulate a consumer cost minimisation game for this setting, and discuss the challenges arising in equilibrium search for this game.     

Enhanced Simulated Annealing Technique for the Single-Row Routing Problem

This paper presents ESSR (Enhanced Simulated annealing for Single-row Routing) model for solving the single-row routing problem. The main objective in this problem is to produce a realization that minimizes both the street congestion and the number of doglegs. Simulated annealing (SA) is a stochastic, hill-climbing and gradient-descent technique based on the statistical properties of particles undergoing thermal annealing. By performing slow cooling, the nets in the single-row routing problem align themselves according to a configuration with the lowest energy. The model has been known to produce reasonably good solutions for many NP-complete optimization problems, such as the single-row routing problem. In ESSR, our strategy is to minimize both the street congestion and the number of interstreet crossings (doglegs) by expressing a single energy function as their collective properties. This objective is achieved by representing the energy as the absolute sum of the heights of the net segments. To speed up convergence, we pivot the street congestion value while having the energy drops directly proportional to the number of doglegs. This action has the effect of minimizing the number of doglegs as the energy stabilizes. Our simulation work on ESSR produces optimal results in most cases for both the street congestion and the number of doglegs. Our experimental results compare well against results obtained from our earlier model (SRR-7) and two other methods reported in the literature.     

集成电路物理设计中的快速可布性评估算法

集成电路可布性评估在集成电路物理设计中针对布局结果进行有效的评估,作为对布局的反馈信息,并指导后续布线阶段的工作,避免了当后续布线无法完成时再回到前面布局阶段进行重新布局的被动局面,减少了物理设计的迭代周期.提出一种快速可布性评估算法,采用新的基于概率模型的估计算法,利用边界框进行拥挤度的预估,并在概率指导下进行实际布线.文中算法可以在很短的运行时间内对拥挤情况进行较为准确、客观的分析,线长较短.     

Estimating congestion zones and travel time indexes based on the floating car data

Efficiently predicting traffic congestion benefits various traffic stakeholders, from regular commuters and logistic operators to urban planners and responsible authorities. This study aims to give a high-quality estimation of traffic conditions from a large historical Floating Car Data (FCD) with two main goals: (i) estimation of congestion zones on a large road network, and (ii) estimation of travel times within congestion zones in the form of the time-varying Travel Time Indexes (TTIs). On the micro level, the traffic conditions, in the form of speed profiles were mapped to links in the road network. On the macro level, the observed area was divided into a fine-grained grid and represented as an image where each pixel indicated congestion intensity. Spatio-temporal characteristics of congestion zones were determined by morphological closing operation and Monte Carlo simulation coupled with temporal clustering. As a case study, the road network in Croatia was selected with spatio-temporal analysis differentiating between the summer season and the rest of the year season. To validate the proposed approach, three comparisons were conducted: (i) comparison to real routes' travel times driven in a controlled manner, (ii) comparison to historical trajectory dataset, and (iii) comparison to the state-of-the-art method. Compared to the real measured travel times, using zone's time-varying TTIs for travel time estimation resulted in the mean relative percentage error of 4.13%, with a minor difference to travel times estimated on the micro level, and a significant improvement compared to the current Croatian industrial navigation. The results support the feasibility of estimating congestion zones and time-varying TTIs on a large road network from FCD, with the application in urban planning and time-dependent routing operations due to: significant reduction in the data volume without notable quality loss, and meaningful reduction in the pre-processing computation time.     

一种无线传感器网络以数据为中心的QoS路由协议

提出一种以数据为中心的QoS路由协议(DDQP).DDQP支持两种QoS度量:可靠性和传输延迟;采用交叉层优化技术将传感器网络无线信道通信模型作为路由协议设计的依据,有效的节约了网络能源消耗;采用反压力重新路由机制在满足业务QoS的前提下尽可能均匀使用网络中节点的能源,不仅延长了网络生命期,而且有效的控制网络拥塞;采用以数据为中心的数据分发模式,具有良好的可扩展性.描述了DDQP设计的理论依据,并通过仿真实验验证了DDQP的高效可行性.     

顾及节点聚集能力的引力场动态路由方法

研究高效的动态路由选择方法对提高网络吞吐量及缓解交通拥塞程度至关重要。为此,从引力理论角度,本文深入分析了网络交通传输过程中节点对交通流的聚集作用,考虑节点自身及其邻居节点的畅通程度及传输路径长度,建立节点对交通流的引力模型,进而提出一种顾及节点聚集能力的引力场动态路由方法。为检验方法有效性,对不同路由策略进行对比仿真实验。结果表明,与最短路由算法相比,本文路由算法较大地提高了网络的吞吐量,算法具有较好地稳定性和可靠性。     

基于改进蚁群算法的拥塞规避QoS路由算法

服务质量(QoS)路由算法缺少拥塞规避机制,会影响业务QoS。为此,提出一种基于Ant-Pub&Enco的信息素模型,根据蚂蚁行进方向,利用相反的信息素引导模式以及不同的信息素更新策略进行路由搜索。基于改进蚁群算法设计一种规避拥塞算法求解QoS单播路 由问题。仿真结果表明,该算法能够有效规避网络中的拥塞链路,均衡网络负载。     

ACO-Steiner: Ant Colony Optimization Based Rectilinear Steiner Minimal Tree Algorithm

The rectilinear Steiner minimal tree （RSMT） problem is one of the fundamental problems in physical design, especially in routing, which is known to be NP-complete. This paper presents an algorithm, called ACO-Steiner, for RSMT construction based on ant colony optimization （ACO）. An RSMT is constructed with ants＇ movements in Hanan grid, and then the constraint of Hanan grid is broken to accelerate ants＇ movements to improve the performance of the algorithm. This algorithm has been implemented on a Sun workstation with Unix operating system and the results have been compared with the fastest exact RSMT algorithm, GeoSteiner 3.1 and a recent heuristic using batched greedy triple construction （BGTC）. Experimental results show that ACO-Steiner can get a short running time and keep the high performance. Furthermore, it is Mso found that the ACO-Steiner can be easily extended to be used to some other problems, such as rectilinear Steiner minimal tree avoiding obstacles, and congestion reduction in global routing.     

Competitive routing over time

Congestion games are a fundamental and widely studied model for selfish allocation problems like routing and load balancing. An intrinsic property of these games is that players allocate resources simultaneously and instantly. This is particularly unrealistic for many network routing scenarios, which are one of the prominent application scenarios of congestion games. In many networks, load travels along routes over time and allocation of edges happens sequentially. In this paper, we consider two frameworks that enhance network congestion games with a notion of time. We introduce temporal network congestion games that are based on coordination mechanisms — local policies that allow to sequentialize traffic on the edges. In addition, we consider congestion games with time-dependent costs, in which travel times are fixed but quality of service of transmission varies with load over time. We study existence and complexity properties of pure Nash equilibria and best-response strategies in both frameworks for the special case of linear latency functions. In some cases our results can be used to characterize convergence properties of various improvement dynamics, by which the population of players can reach equilibrium in a distributed fashion.     

Capacity-aware state aggregation for interdomain QoS routing

Quality-of-service (QoS) routing between domains is an essential component for providing service differentiation in the Internet. State aggregation is a technique that makes QoS routing scalable to large internetworks, by presenting a concise and accurate representation of the domain to the routing process. The concept of using a domain's routing capacity as a bandwidth aggregate has been in existence for some time. However, no methodology has been suggested in the literature for its estimation and usage. Also, the impact of a domain's routing capacity on the routing performance has not been studied before. This paper aims to fill these voids by presenting a framework based on "network flows" for estimating a domain's routing capacity and evaluating its efficacy on the routing performance. The routing capacity is used in conjunction with the conventional widest path bandwidth as the domain aggregate. Analytical and experimental results show that appropriate use of routing capacity along with the widest path bandwidth reduces the tendency of the advertised aggregate to overestimate bandwidth availability, and makes the routing process more robust to the frequency of domain state updates. During periods of congestion, the use of routing capacity can improve the bandwidth admitted into the network by as much as 20%.     

Fuzzy-based Adaptive Routing Algorithm for Networks-on-Chip

In this paper, we propose two adaptive routing algorithms to alleviate congestion in the network. In the first algorithm, the routing decision is assisted by the number of occupied buffer slots at the corresponding input buffer of the next router and the congestion level of that router. Although this algorithm performs better than the conventional method, DyXY, in some cases the proposed algorithm leads to non-optimal decisions. Fuzzy controllers compensate for ambiguities in the data by giving a level of confidence rather than declaring the data simply true or false. To make a better routing decision, we propose an adaptive routing algorithm based on fuzzy logic for Networks-on-chip where the routing path is determined based on the current condition of the network. The proposed algorithm avoids congestion by distributing traffic over the routers that are less congested or have a spare capacity. The output of the fuzzy controller is the congestion level, so that at each router, the neighboring router with the lowest congestion value is chosen for routing a packet. To evaluate the proposed routing method, we use two multimedia applications and two synthetic traffic profiles. The experimental results show that the fuzzy-based routing scheme improves the performance over the DyXY routing algorithm by up to 25% with a negligible hardware overhead.     

On the implications of routing metric staleness in delay tolerant networks

Delay Tolerant Network (DTN) routing addresses challenges of providing end-to-end service where end-to-end data forwarding paths may not exist. The performance of current DTN routing protocols is often limited by routing metric “staleness”, i.e., routing information that becomes out-of-date or inaccurate because of long propagation delays. Our previous work, ParaNets, proposed a new opportunistic network architecture in which the data channel is augmented by a thin end-to-end control channel. The control channel is adequate for the exchange of control traffic, but not data. In this paper we present Cloud Routing, a routing solution for the ParaNets architecture. We motivate the need for such a solution, not only because of stale routing metrics, but also because of congestion that can occur in DTNs. Unable to use up-to-date routing metrics to limit congestion, existing DTN routing solutions suffer from low goodput and long data delivery delays. We show how Cloud Routing avoids congestion by smart use of forwarding opportunities based on up-to-date routing metrics. We evaluate our solution using extensive OPNET simulations. Cloud Routing extends network performance past what is currently possible and motivates a new class of globally cognizant DTN routing solutions.     

消除拥挤的多层总体布线新流程

提出一种在布线前进行层分配的总体布线算法,基于一个多层布线的新流程,使用包含线网所有端点的边界盒来估计线网拥挤度,并基于拥挤度均匀的目标把线网分配到不同层对上.该算法已经实现并进行了测试,实验结果证明了其有效性.     

移动Ad Hoc网络基于路由协议的拥塞控制

为解决Ad Hoc网络的AODV路由协议在通信过程中存在的拥塞问题,提出了改进AODV路由协议的思想。根据网络链路拥塞度的大小采取不同措施和节点路由,建立不相关多径路由分流以避免拥塞。仿真结果表明,改进后的路由协议有效地减少了发生拥塞的几率,从而提高了移动Ad Hoc网络的性能。     

Hybrid particle swarm optimization for parameter estimation of Muskingum model

The Muskingum model is the most widely used and efficient method for flood routing in hydrologic engineering; however, the applications of this model still suffer from a lack of an efficient method for parameter estimation. Thus, in this paper, we present a hybrid particle swarm optimization (HPSO) to estimate the Muskingum model parameters by employing PSO hybridized with Nelder–Mead simplex method. The HPSO algorithm does not require initial values for each parameter, which helps to avoid the subjective estimation usually found in traditional estimation methods and to decrease the computation for global optimum search of the parameter values. We have carried out a set of simulation experiments to test the proposed model when applied to a Muskingum model, and we compared the results with eight superior methods. The results show that our scheme can improve the search accuracy and the convergence speed of Muskingum model for flood routing; that is, it has higher precision and faster convergence compared with other techniques.     

Congestion Adaptive Routing in Mobile Ad Hoc Networks

Mobility, channel error, and congestion are the main causes for packet loss in mobile ad hoc networks. Reducing packet loss typically involves congestion control operating on top of a mobility and failure adaptive routing protocol at the network layer. In the current designs, routing is not congestion-adaptive. Routing may let a congestion happen which is detected by congestion control, but dealing with congestion in this reactive manner results in longer delay and unnecessary packet loss and requires significant overhead if a new route is needed. This problem becomes more visible especially in large-scale transmission of heavy traffic such as multimedia data, where congestion is more probable and the negative impact of packet loss on the service quality is of more significance. We argue that routing should not only be aware of, but also be adaptive to, network congestion. Hence, we propose a routing protocol (CRP) with such properties. Our ns-2 simulation results confirm that CRP improves the packet loss rate and end-to-end delay while enjoying significantly smaller protocol overhead and higher energy efficiency as compared to AODV and DSR     

基于热点簇的Ad hoc网络拥塞回避算法

Adhoc网络由于其节点具有随机移动性和分布密度不均匀的特征,容易引起网络局部的拥塞,但目前常用的Adhoc网络路由算法大多不具有拥塞响应机制。基于簇的网络结构易于实现对网络及节点的管理,论文定义热点簇,并提出基于热点簇的拥塞回避算法(CMHA),能实现对拥塞的快速响应,达到防止拥塞加剧的目的。算法利用多个节点的状态参数(如MAC延迟,或者缓冲区占有量)判断拥塞,并快速找到一条新的路径避开拥塞区域以降低拥塞区域负载。仿真结果表明此算法满足Adhoc网络中分级结构的要求,明显改善端到端的投递率,节省了能量,保证了服务质量。     

Performance evaluation of an intelligent CAC and routing framework for multimedia applications in broadband networks

In order to support multimedia communication, it is necessary to develop routing algorithms which use for routing more than one QoS parameters. This is because new services such as video on demand and remote meeting systems require better QoS. Also, for the Call Admission Control (CAC) of multimedia applications different QoS parameters should be considered. In our previous work, we proposed an intelligent routing and CAC strategy using cooperative agents. However, we only considered the time delay for the routing. The QoS and Congestion Control (CC) parameters were considered as indicators of QoS satisfication and congestion. In this work, we extend our previous work by proposing and implementing new algorithms based on Fuzzy Logic (FL) and Genetic Algorithm (GA) which use for CAC and routing many QoS parameters. The simulation results show that proposed framework has a good performance and is a promising method for QoS routing and CAC decision.