用PSO优化基于QoS的WSN路由协议

在过去的10年中,提供服务质量(QoS)保证的路由协议很好的用在了有线网络中,随着无线传感网络(WSN)的广泛使用,就需要考虑如何在无线传感网络中实现QoS的效率.很多QoS度量值都需要被考虑,如数据包时延、带宽效率、能量消耗等.同时QoS也受路由的影响,由于路由解空间随着网络的规模变大而呈指数级的增长,研究路由协议算法的效率就成为一个必然.在研究了智能粒子群最优算法(PSO)后,提出了一个基于PSO的路由算法,不仅考虑了QoS的需求同时也有一个不错的搜索能力.仿真结果表明,与一些典型QoS常规机制比较基于粒子群最优路由算法是有效的.     

Improvement of battery lifetime in software-defined network using particle swarm optimization based cluster-head gateway switch routing protocol with fuzzy rules

The software-defined network (SDN) is one of the network architectures, in which the data plane and control plane is divided from each other, and the network can be handled using a sensibly centralized controller and this method is adopted to reconfigure the wireless sensor network automatically. In this article, to implement the SDN in MANET, in which control nodes can be chosen in SDN dynamically for the activation of MANET function to allocate the works to other mobile nodes to the base station. However, in the field of mobile ad hoc networks, the network lifetime, and battery lifetime is one of the major problems and the energy consumption can play a significant rule for the transmission of data in the SDN. Therefore, in this article, particle swarm optimization (PSO) based CGSR (cluster-head gateway switch routing protocol) algorithm with fuzzy rules is proposed to increase the network lifetime of battery powered mobile nodes by reducing the energy consumptions of each node in software-defined MANET. In this proposed method, a routing method that can permit various mobile nodes with low battery power to transmits the data from source node to base station. We design a PSO based CGSR routing protocol by selecting the routing mobile nodes using fuzzy rules for packet transmission. In CGSR process, the formation of cluster and selection of cluster head is executed depending on the particle swarm optimization method. This proposed routing protocol can be used to enhance the battery lifetime by extension of the network lifetime with numerical analysis for efficient route node selection.     

基于蚁群遗传混合算法的QoS组播路由

具有延迟、延迟抖动、带宽、丢包率等服务质量约束的组播路由问题具有NP完全的复杂度。基于蚁群优化算法和遗传算法,提出解决QoS约束组播路由问题的混合算法。利用遗传算法和蚁群优化算法各自的优点,使用蚁群优化算法选择种群,遗传算法优化蚂蚁遍历所得到的解。仿真实验结果表明,该算法可满足各个约束条件,且全局寻优性能好,能够满足网络服务质量要求。     

Bio-inspired self-aware fault-tolerant routing protocol for network-on-chip architectures using Particle Swarm Optimization

This research paper proposes a bio-inspired self-aware fault-tolerant routing protocol for network-on-chip architecture using particle swarm optimization (PSO), which considers synchronous, asynchronous, and self-organizing communication mechanisms to intelligently load-balance the traffic on the entire network in the presence of faulty components. By way of experimentation and simulation, this study demonstrates that the proposed scheme can converge to a global optimum, minimal routing path in real time, in the presence of network congestion and faulty routers and links. The basic PSO algorithm was improved to implement the proposed routing scheme, named bio-inspired self-aware fault-tolerant routing protocol (BISFTRP). This scheme uses the synchronous, asynchronous, and self-organizing features of PSO to create a global routing table and intelligent adaptation, which gives rise to scalable, real-time, and dynamic routing decisions with high throughput, low latency, and minimum power consumption. A cycle-accurate simulation system to demonstrate the flexibility and efficiency of the proposed scheme is used. Comparison results with state-of-the-art fault-tolerant routing algorithms show that the BISFTRP routing protocol achieves high routing performance without routing oscillations and throughput degradation. Furthermore, the hardware implementation results show that the BISFTRP router achieves an efficient area and power utilization, compared with state-of-the-art routers.     

Ant colony algorithms in MANETs: A review

Mobile ad-hoc networks (MANETs) consist of special kind of wireless mobile nodes which form a temporary network without using any infrastructure or centralized administration. MANETs can be used in wide range of future applications as they have the capability to establish networks at anytime, anywhere without aid of any established infrastructure. It is a challenging task to find most efficient routing due to the changing topology and the dynamic behavior of the nodes in MANET. It has been found that ant colony optimization (ACO) algorithms can give better results as they are having characterization of Swarm Intelligence (SI) which is highly suitable for finding the adaptive routing for such type of volatile network. ACO algorithms are inspired by a foraging behavior of group of ants which are able to find optimal path based upon some defined metric which is evaluated during the motion of ants. ACO routing algorithms use simple agents called artificial ants which establish optimum paths between source and destination that communicate indirectly with each other by means of stigmergy. Keeping in view of the above, in this paper we provide a taxonomy of various ant colony algorithms with advantages and disadvantages of each others with respect to various metrics.     

基于群集智能算法的移动机器人路径规划研究

本文提出一种新的群集智能算法,在用Dijkstra算法基于链接图建模的地图中得到一个最优解的可行空间后,再用粒子群算法或蚂蚁算法优化得到全局的最优路径。因为群集智能算法是一种概率搜索算法,没有集中控制约束条件,不会因为个别个体的故障影响整个问题的求解,具有较强的鲁棒性,所以在机器人全局路径规划应用中具有较显著的优点。仿真结果表明了算法的有效性,是机器人路径规划的一个较好的方法。     

Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization

A mobile ad hoc network (MANET) is dynamic in nature and is composed of wirelessly connected nodes that perform hop-by-hop routing without the help of any fixed infrastructure. One of the important requirements of a MANET is the efficiency of energy, which increases the lifetime of the network. Several techniques have been proposed by researchers to achieve this goal and one of them is clustering in MANETs that can help in providing an energy-efficient solution. Clustering involves the selection of cluster-heads (CHs) for each cluster and fewer CHs result in greater energy efficiency as these nodes drain more power than noncluster-heads. In the literature, several techniques are available for clustering by using optimization and evolutionary techniques that provide a single solution at a time. In this paper, we propose a multi-objective solution by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in an ad hoc network as well as energy dissipation in nodes in order to provide an energy-efficient solution and reduce the network traffic. In the proposed solution, inter-cluster and intra-cluster traffic is managed by the cluster-heads. The proposed algorithm takes into consideration the degree of nodes, transmission power, and battery power consumption of the mobile nodes. The main advantage of this method is that it provides a set of solutions at a time. These solutions are achieved through optimal Pareto front. We compare the results of the proposed approach with two other well-known clustering techniques; WCA and CLPSO-based clustering by using different performance metrics. We perform extensive simulations to show that the proposed approach is an effective approach for clustering in mobile ad hoc networks environment and performs better than the other two approaches.     

A virtual subnet scheme on clustering algorithms for mobile ad hoc networks

Resolving the broadcast storm problem is an important issue in mobile ad hoc networks (MANETs). In this paper, we propose an approach for constructing a virtual subnet whose nodes are logically related. The virtual subnet can be spread upon clusters of a MANET. An intelligent agent with a routing filtering table is proposed to assist the best known clustering algorithms, the original Least ID algorithm and the original Highest Connection Cluster (HCC) algorithm, to improve group communication efficiency. Our simulation covers the network factors of hop count, deprave rate, and delay time. The simulation results show that when the proposed intelligent agent is used with the HCC algorithm, the delay time was reduced by 81.84% as compared with flooding, and by 49.25% as compared with the Ad Hoc On-Demand Distance Vector (AODV) routing algorithm. The delay time for the Least ID algorithm assisted by the proposed agents reduced by 81.84% compared to that of flooding and by 50% compared to that of AODV.     

A MANET accessing Internet routing algorithm based on dynamic gateway adaptive selection

Based on the study on communication situation of mobile ad hoc network (MANET) accessing Internet and taking the gateway important function of accessing network into account, a MANET accessing Internet routing algorithm based on dynamic gateway adaptive selection (MRBDAS) is presented. It considers candidate gateways’ connecting degree, load degree, residual energy, and movement rate synthetically and uses the idea of group decision-making method for reference. The algorithm employs the methods of multipaths and query localization technique based on old path information to maintain routing adaptively. Compared with the existing accessing routing algorithm based on dynamic gateway, the algorithm demonstrates in its simulations that by bringing dynamic gateways colony function, the MRBDAS can improve network throughput, reduce average transmission delay of data packets and routing overhead, and prolong accessing network life. The validity of MRBDAS has been proven.     

基于遗传算法的移动自组网路由优化

对移动自组网的拓扑结构进行分析,建立其路由网络模型．将遗传算法的基本原理和移动自组 网的路由模型结合起来,提出了一种求解无线网络最优路径的算法．该方法采用可变长度染色体编码,利用 遗传算法优化路由,可以在满足系统开销最小的约束条件下寻找到最优路径．     

A MANET accessing Internet routing algorithm based on dynamic gateway adaptive selection

Based on the study on communication situation of mobile ad hoc network (MANET) accessing Internet and taking the gateway important function of accessing network into account, a MANET accessing Internet routing algorithm based on dynamic gateway adaptive selection (MRBDAS) is presented. It considers candidate gateways’ connecting degree, load degree, residual energy, and movement rate synthetically and uses the idea of group decision-making method for reference. The algorithm employs the methods of multipaths and query localization technique based on old path information to maintain routing adaptively. Compared with the existing accessing routing algorithm based on dynamic gateway, the algorithm demonstrates in its simulations that by bringing dynamic gateways colony function, the MRBDAS can improve network throughput, reduce average transmission delay of data packets and routing overhead, and prolong accessing network life. The validity of MRBDAS has been proven.     

异步粒子群优化算法在QoS组播路由中的应用

QoS组播路由问题是一个非线性的组合优化问题,已证明了该问题是NP完全问题。为适应下一代IP网络对实时信息传输的要求,在异步模式粒子群优化算法基础上,给出包含延迟、延迟抖动、带宽、丢包率和最小花费5个约束条件在内的QoS组播路由算法。该算法首先给出数学模型,设计适应度函数,再给出受限的网络模型,通过粒子群优化(PSO)算法最大化适应度函数来求解最优Steiner树。算法仿真实验结果表明:与遗传算法和同步模式的粒子群优化算法相比,该算法有较好的收敛速度和寻优效果。     

基于蚁群优化的无线传感器网络能耗均衡路由算法

针对无线传感器网络中节点能量受限的特点,将蚁群优化算法(ACO)应用于无线传感器网络,同时考虑了通信路径长度和节点剩余能量等因素,提出了具有能量意识的无线传感器网络路由算法,从多方面解决了节点间的能耗不均衡问题。该算法在OMNET++平台下仿真结果表明,与Ant-Net、ACRA算法相比在能耗不均衡和传输延迟等方面有了较大改进,实现了全网节点的能耗均衡,有效延长了网络生命期,减小了传输时延。     

基于蚁群优化的无线传感器网络路由算法

如何在资源受限的无线传感器网络中进行高效的数据路由是无线传感器网络研究的热点之一。基于群智能优化技术的蚁群优化算法被广泛应用于网络路由算法。提出一种无线传感器网络蚁群优化路由算法,能够保持网络的生存时间最长,同时能找到从源节点到基站节点的最短路径;采用的多路数据传输也可提供高效可靠的数据传输,同时考虑节点的能量水平。仿真结果表明:提出的算法延长了无线传感器网络的寿命,实现无线传感器网络在通信过程中快速、节能的路由。     

基于粒子群优化的蚁群算法在TSP中的应用

结合粒子群算法的问题,提出用混合蚁群算法来求解著名的旅行商问题.问题的核心是应用粒子群算法对蚁群算法的控制参数:启发式因子、信息素挥发系数、随机性选择阈值进行优化,以及运用蚁群系统算法寻找最短路径.新算法对于蚂蚁算法中的参数调整大大减低,减少了大量盲目的实验,力求在开发最优解和探究搜索空间上找到平衡点.对旅行商问题的仿真实验表明,新算法的优化质量和效率都优于传统蚁群算法和遗传算法,接近理论最佳值.新算法也可推广用于其他NP问题的求解.     

基于GPS的启发式Ad hoc路由算法研究*

为了使移动Ad hoc网络中的节点在寻找路径时能获得较高的可用性和更低的开销,提出了一个新的路由算法,应用全球定位系统（GPS）提供的信息作为启发式信息,节点使用不同的概率转发路由信息,从而大大减少网络维护路由信息的总量,算法选择一个或两个路径记录在本地路由表中以提高其鲁棒性,当遇到连接断开,采取修复机制创建新的路径以提高数据包传输的成功率。仿真结果表明,该算法取得了较好的数据包传输成功率与较低的通信延迟。     

一种可扩展动态混合移动自组网路由协议

现有的主动式路由协议和反应式路由协议在节点数目多、节点密度高的场景下性能不够理想,可扩展性较差,而混合路由协议可扩展性相对较好.提出了一种可扩展动态混合移动自组网路由协议--SDHRP(Scalable Dynamic Hybrid Routing Protocol),该协议基于一个分布式动态最大独立集算法.与ZRP的对比实验结果表明,在保证网络吞吐量的前提下,SDHRP的路由控制开销减小了30%左右,具有较好的可扩展性.     

NGI中一种混合智能QoS组播路由算法

针对满足多个约束条件的服务质量（Quality of Service,QoS）组播路由的特点,提出了一种下一代互联网（Nem Generation Internet,NGI）中基于粒子群优化（Particle Swarm Optimization,PSO）和遗传算法（Genetic Algorithm,GA）的混合智能QoS组播路由算法。给出了QoS组播路由问题模型及其数学描述,结合PSO的快速搜索和GA的全局寻优能力,解决了多约束QoS组播路由问题,并在多个实际的和虚拟的网络拓扑上进行了仿真实现与性能评价。仿真结果表明,本文提出的算法是可行和有效的。     

A novel two-stage hybrid swarm intelligence optimization algorithm and application

This paper presents a novel two-stage hybrid swarm intelligence optimization algorithm called GA–PSO–ACO algorithm that combines the evolution ideas of the genetic algorithms, particle swarm optimization and ant colony optimization based on the compensation for solving the traveling salesman problem. In the proposed hybrid algorithm, the whole process is divided into two stages. In the first stage, we make use of the randomicity, rapidity and wholeness of the genetic algorithms and particle swarm optimization to obtain a series of sub-optimal solutions (rough searching) to adjust the initial allocation of pheromone in the ACO. In the second stage, we make use of these advantages of the parallel, positive feedback and high accuracy of solution to implement solving of whole problem (detailed searching). To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems from TSPLIB are tested to demonstrate the potential of the proposed two-stage hybrid swarm intelligence optimization algorithm. The simulation examples demonstrate that the GA–PSO–ACO algorithm can greatly improve the computing efficiency for solving the TSP and outperforms the Tabu Search, genetic algorithms, particle swarm optimization, ant colony optimization, PS–ACO and other methods in solution quality. And the experimental results demonstrate that convergence is faster and better when the scale of TSP increases.     

Particle swarm optimization based clustering algorithm with mobile sink for WSNs

Wireless sensor networks with fixed sink node often suffer from hot spots problem since sensor nodes close to the sink usually have more traffic burden to forward during transmission process. Utilizing mobile sink has been shown as an effective technique to enhance the network performance such as energy efficiency, network lifetime, and latency, etc. In this paper, we propose a particle swarm optimization based clustering algorithm with mobile sink for wireless sensor network. In this algorithm, the virtual clustering technique is performed during routing process which makes use of the particle swarm optimization algorithm. The residual energy and position of the nodes are the primary parameters to select cluster head. The control strategy for mobile sink to collect data from cluster head is well designed. Extensive simulation results show that the energy consumption is much reduced, the network lifetime is prolonged, and the transmission delay is reduced in our proposed routing algorithm than some other popular routing algorithms.