一种基于Ad Hoc低能耗QoS多播路由算法

针对Ad Hoc网络特点和节点的剩余能量、延时、延时抖动、带宽等QoS约束问题,提出了基于Ad Hoc的多QoS约束、多播路由算法SEQMRA。该算法把最大剩余能量作为第一度量,同时考虑节点带宽、延时、延时抖动等因素,并对算法进行了复杂性分析。实验表明,算法在路由成功率、多播树费用、能量消耗方面均具有较好特性。     

基于WSN最大剩余能量的QoS多播路由算法

针对无线传感器网络特点和节点的剩余能量、延时、延时抖动、带宽等QoS约束问题,提出了基于WSN节点最大剩余能量的多QoS约束多播路由算法SEQMRA-W(Surplus Energy Qos Multicast Routing Algorithm based on WSN).该算法把最大剩余能量作为第一度量,同时考虑节点带宽、延时、延时抖动等因素.并对算法进行了复杂性分析.仿真实验表明:算法在路由成功率、多播树费用、能量消耗方面均具有较好特性.     

一种节能的无线传感器网络QoS路由算法

针对无线传感器网络中不同业务对QoS的不同要求，提出了一种新的QoS路由算法—EBQoS算法。算法通过记录邻居节点到Sink节点的最小跳数信息建立到Sink节点的多条路径，并根据信息包的QoS要求和节点剩余能量选择合适的路径进行路由。通过仿真实验与顺序分配路由（SAR）算法的比较，结果表明该路由算法在提供差别服务和提高网络生存期方面具有明显的优势。     

基于神经网络的动态路由选择算法

在分析了网络中基于QoS组播路由问题的基础上,文章给出了基于Hopfield神经网络的动态路由选择算法的模型。仿真研究表明该算法具有良好的分布特性和智能决策能力,此方案不仅保证了带宽、端到端延时和延时抖动,优化了路由树的代价,而且有效地控制了算法的复杂性,是一种快速动态组播路由算法,能实现全局网络资源利用的优化,容易扩展到大型网络中应用。     

基于QoS的无线传感器网络路由

综合考虑无线传感器网络的路径节点最小能量、路径能耗、延迟、信道错误率等因素,采用QoS路由对不同的查询业务实现区分服务。通过对传统的定向扩散路由算法的扩展,在保证业务可靠性要求的前提下,组合利用“路径节点最小能量/路径能耗”和“最小跳数”信息,提出了一种针对尽力而为业务和实时业务的QoS路由算法。仿真结果表明,扩展后的算法能满足业务的可靠性要求,随信道错误率的增加,它能更好地均衡网络各节点的能耗。     

无线传感器网络的能量均衡路由算法

针对大部分无线传感器网络路由协议只能实现局部能量均衡的问题,提出一种改进的能量均衡路由算法,把传感器网络构建成按最小跳数分层的网络,利用节点直接传输和逐跳转发相结合的混合传输策略,在多跳传输时,使用改进的基于多路径路由的最大能量路径算法。仿真结果表明,该算法能有效延长网络的生存时间。     

井下无线传感器网络最小跳数路由改进算法研究

针对经典无线传感器网络最小跳数路由算法存在节点能耗高、网络生存周期较短的问题,提出了一种最小跳数路由改进算法。该改进算法在梯度场建立阶段设置节点延时计时器;在数据传输阶段综合考虑节点能耗的有效性和均衡性,依据能量代价函数,动态选择唯一中继节点,构建一条从信源节点到Sink节点能量代价最小的路径。仿真结果表明,该改进算法进一步降低了节点能耗,延长了网络寿命,能够满足井下无线传感器网络路由需要。     

基于邻居表的ZigBee网络树路由改进算法

针对ZigBee网络树路由算法路由跳数多、数据传输延时长等问题,提出一种基于邻居表的ZigBee网络树路由改进算法。借助一跳邻居节点地址信息,建立邻居节点选择策略,在节点的一跳邻居节点中,选择到达目的节点树路由跳数最少的邻居节点作为下一跳转发节点。在树路由跳数相同时,选取LQI值大的节点为下一跳转发节点。理论分析结果表明,该算法路由路径优于树路由算法和ITRA算法路由路径;实验结果表明,该算法能很好地减少转发节点个数,提高了网络数据传输的可靠性,达到网络性能提高的目的。     

DMRMQ:基于多QoS的动态组播路由算法

提出了一种基于多QoS的动态组播路由算法-DMRMQ，该算法采用Bellman-Forei算法作为路径搜索算法，能在满足带宽、延迟、延迟抖动和丢包率的前提下确定具有最小跳数和开销的动态组播路由．针对音频和普通数据流的仿真实验表明，DMRMQ能在基于多QoS的前提下建立动态组播路由，算法在路由请求平均成功率、路由平均延迟和平均跳数等衡量指标上比不考虑QoS的动态组播路由算法具有更好的性能．     

基于QPSO的QoS组播路由算法

对带宽、延时、延时抖动约束最小代价的QoS组播路由问题进行了研究，提出一种基于量子行为微粒群优化（QPSO）算法来设计路由优化算法。该算法采用一种节点序列编码方案，将路由优化问题转化成一种准连续优化问题，并采用罚函数处理约束条件。应用QPSO算法求解QoS组播路由问题的算例，并与遗传算法和改进后的遗传算法进行比较。计算机仿真实验证明，该算法可以更有效地求得QoS组播路由问题的优化解，可靠性较高。     

双重约束条件下的WSN路由选择

经典的基于QoS（Quality of Service,服务质量）的WSN（Wireless Sensor Network,无线传感器网络）路由算法往往只考虑了单一的条件限制,如能量、通信跳数、延时等。而在一个复杂的WSN环境中,对于QoS 的需求通常综合了多种条件。这使得现有的路由算法难以选择出实际情况下的最优路由。一种基于动态规划问题的算法被提出,用来解决这一问题。当算法选择最小延时路由时,节点能量和链路丢失率将被作为约束条件。经过证明,算法具有最优性。     

多类别业务流的局部化QoS多路径路由算法

传统的全局QoS路由需要获知精确的全局网络QoS状态．由于不可忽略的传播延时和处理开销，节点所获得的QoS信息总是不精确的．当节点只了解部分的网络状态信息时，通过引入多类别业务流的虚带宽模型，本文提出了一种多类别业务流的局部化QoS路由算法．仿真结果验证了算法的有效性．     

Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in Taiwan

Supplying industrial firms with an accurate method of forecasting the production value of the mechanical industry to facilitate decision makers in precise planning is highly desirable. Numerous methods, including the autoregressive integrated-moving average (ARIMA) model and artificial neural networks can make accurate forecasts based on historical data. The seasonal ARIMA (SARIMA) model and artificial neural networks can also handle data involving trends and seasonality. Although neural networks can make predictions, deciding the most appropriate input data, network structure and learning parameters are difficult. Therefore, this article presents a hybrid forecasting method that combines the SARIMA model and neural networks with genetic algorithms. Analytical results generated by the SARIMA model are inputted as the input data of a neural network. Subsequently, the number of neurons in the hidden layer and the number of learning parameters of the neural network architecture are globally optimized using genetic algorithms. This model is subsequently adopted to forecast seasonal time series data of the production value of the mechanical industry in Taiwan. The results presented here provide a valuable reference for decision makers in industry.     

基于Ad Hoc多QoS约束的多播路由协议

针对移动Ad Hoc网络特点，研讨了Ad Hoc网络中其有多QoS约束的多播路由问题，其中主要包含延迟、延迟抖动、带宽、代价等QoS约束。描述了一种适应于研究Ad Hoc网络QoS多播路由的网络模型，提出了Ad Hoc网络中一种具有多QoS约束的多播路由协议。给出了MQAP的路由实现过程，进行了正确性证明和复杂性分析。仿真实验结果表明，MQAP为Ad Hoc网络多QoS约束多播路由提供了一种新的有效途径。     

AdHoc网络中基于能量的QoS路由协议的研究

在AdHoc网络中,QoS路由是提供QoS保证的一种重要手段。大多数QoS路由都是建立在单径的基础上,没有充分利用AdHoc网络资源。此外,网络中节点的能量也是影响网络性能的关键因素。然而,现有的QoS路由协议却很少考虑网络中节点．能量有限这一问题。对此,文中在DSR路由协议的基础上提出一种基于能量约束的多径QoS路由协议（MPQE）。该协议主要考虑带宽和剩余能量来选择多条QoS路由。仿真结果表明,MPQE协议提高了吞吐率,降低了时延并延长了网络的生命周期。     

基于遗传算法的多约束QoS多播路由优化算法

随着大量新型的多媒体在高性能网络、移动网络及Internet中的应用，满足QoS约束的多播路由问题成为越来越重要，它吸引了许多爱好者．本文讨论了多约束QoS多播路由问题，主要包含延迟、延迟抖动、带宽和分组丢失率等QoS约束，文中描述了一种在动态网络环境及不确定参数下适应于研究QoS多播路由的网络模型．提出了一种在网络规模、可行性方面为Imernet、移动网络和高性能网络下基于遗传算法的多约束QoS多播路由优化算法（MQMRGA）．仿真结果表明该算法收敛速度快、可靠性高．MQMRGA为QoS多播路由提供了一种新的有效途径．     

Using artificial intelligence in routing schemes for wireless networks

For the latest 10 years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore the performance of two very well-known routing paradigms, directed diffusion and Energy-Aware Routing, and our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes.     

Ant based Pareto optimal solution for QoS aware energy efficient multicast in wireless networks

Most of the group communication technologies support real-time multimedia applications such as video conferencing and distributed gaming. These applications require quality-of-service (QoS) aware multicast routing protocol to deliver the same data stream to a predefined group of receivers. Since nodes in wireless networks are severely energy constrained due to finite battery source, hence it is of paramount importance that QoS aware multicast routing protocol be energy efficient. Transmission power control is one of the methods used to save energy. In this method, the nodes dynamically adjust the transmission power so that energy consumption in the tree is minimized. However, reduction in the transmission power increases the number of forwarding nodes in the multicast tree. This negatively impacts the QoS in terms of propagation delay, delay jitter, and packet loss etc. In wireless networks, there is a trade-off between the energy consumption and the QoS guarantees provided by the network. We unify these requirements into a multiobjective framework referred to as Energy Efficient QoS Multicast Routing (E²QoSMR). The goal is to simultaneously optimize the total power consumption and the QoS parameters in the multicast tree. We extend two algorithms based on metaphor of swarm intelligence for finding an energy efficient multicast tree satisfying the QoS guarantees. Extensive simulations have been conducted to validate the correctness and efficiency of the algorithms. The simulation result of the algorithms is compared with the nondominated sorting genetic algorithm, NSGA-III. The experimental results are consolidated by statistical analyses that demonstrate the ability of the algorithms to generate the Pareto optimal solution set.     

mesh网络中基于效用转发的网络编码算法

在已有的无线Mesh网络路由算法的基础上,针对网络的动态变化性和差异性等因素带来的问题进行了研究,提出一种基于效用转发的网络编码算法.首先利用节点间的历史通信数据,动态获取不同网络环境下影响效用值的各因素所占的权重值;然后在簇头节点和汇聚节点处进行二维随机线性网络编码,结合节点的剩余能量、效用值和丢包率判断节点能否进行编码,并且采用编码包优先传输的策略转发数据包;最后综合利用网络时延、效用值和节点间的跳数,选择最优的下一跳转发节点.仿真结果表明,该算法能更好地提升数据包的转发效率,降低传输时延,提高网络的性能.