Research on WSN lifetime optimization game algorithm combined power and channel

Due to the problem of shortening the network lifetime which was caused by the big energy consumption for wireless sensor network (WSN) whose energy and computing power was limit,a lifetime optimization game algorithm combined power control and channel allocation (LOAPC) was proposed.The influence of node power and residual energy on the node interference was explored to construct an interference affection measurement model.Then,expected transmission times was introduced to establish a novel node lifetime model.Finally,LOAPC aimed at reducing interference and prolonging lifetime,and the node power was limited by an optional power set which ensured the network connectivity and economized energy consumption,so as to prolong the network lifetime effectively.At the same time,the simulation results show that the algorithm has the characteristics of low interference,low energy consumption and effectively prolonging the lifetime of the network.     

密集异构网络中基于多目标优化的资源分配策略

密集异构网络(Dense Heterogeneous Network, DHN)通过部署小基站可以提升网络容量和用户速率,但小基站的密集部署会产生巨大的能耗和严重的干扰,进而影响系统的能量效率(Energy Efficiency, EE)和频谱效率(Spectral Efficiency, SE)。在保证用户服务质量(Quality of Service, QoS)需求的前提下,为了联合优化系统的能量效率和频谱效率,研究了密集异构网络中下行链路的资源分配(Resource Allocation, RA)问题。首先,将频谱和小基站发射功率分配问题建模为联合优化系统能量效率和频谱效率的多目标优化问题;其次,提出了基于单策略多目标强化学习(Single-strategy Multi-objective Reinforcement Learning, SMRL)的资源分配算法求解所建立的多目标优化问题。仿真结果表明,与基于单目标强化学习的资源分配算法相比,所提算法可以实现系统能量效率和频谱效率的联合优化,与基于群体智能算法的资源分配算法相比,所提算法的系统能量效率提高了1%～1.5%,频谱效率...     

一种联合双时隙的资源分配算法

针对功率最小化问题,提出了一种联合双时隙的资源分配算法,利用相邻两个时隙信道状态整体一致而又具有差异的性质,根据第1时隙的信道状态,将信道状态好的用户的第2时隙的部分或全部速率提前分配,从而使信道增益大的子载波承载了较多的速率,最终降低了系统功率消耗。仿真结果表明,所提双时隙资源分配算法既能保证用户双时隙内的目标速率,又能够有效降低系统的功率消耗。     

Network energy optimization and intelligent routing in WSN applicable for IoT using self-adaptive coyote optimization algorithm

The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self-adaptive meta-heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self-adaptive coyote optimization algorithm (SA-COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA-COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA-COA. From the simulation findings, the best value of the designed SA-COA-LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self-adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C-ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.     

无线mesh网络中的信道分配问题研究

在多接口无线mesh网络中使用多信道可以减少碰撞和干扰,提高系统吞吐量。因此,合理的信道分配是无线mesh网络中多信道技术的关键。用图论理论建立信道分配数学模型以及用图着色理论研究信道分配问题是无线网络中解决信道分配问题的有效方法。因此针对无线mesh网络中多接口多信道(multi-radio and multi-channel)的特点,重点介绍了无线mesh网络中信道分配的基本理论、主要约束和图论模型等,最后提出应用图着色理论解决信道分配问题的一般途径。     

基于PSO优化模糊C均值的WSN分簇路由算法

针对无线传感器网络节点能量有限、负载不均衡的问题,提出了一种基于粒子群优化模糊C均值的分簇路由算法POFCA.POFCA分别从成簇阶段和数据传输阶段进行了优化.成簇阶段,首先使用粒子群优化算法优化模糊C均值算法,克服了模糊C均值对初始聚类中心的敏感,并根据节点剩余能量和相对距离动态更新簇首,平衡簇内负载.数据传输阶段,...     

基于萤火虫算法的多中继功率分配

为了充分实现中继协作,降低多中继协作通信系统功率分配优化问题的计算复杂度,提出了基于萤火虫算法的多中继功率分配方案。在一定的总功率和节点功率约束下,以最大化平均信噪比为优化目标函数,建立了多中继协作系统的功率分配最优化模型。选取该目标函数作为萤火虫的适应度函数,用向量表示萤火虫的状态,该向量的维数为待分配源节点和中继节点的个数,通过萤火虫聚集得到种群中最好的萤火虫,即可获得渐进最优功率分配。仿真结果表明,与平均功率分配相比,基于萤火虫算法的功率分配方案能降低2.44%~6.17%的比特差错率,提高了系统性能。     

无线传感器网络任务分配动态联盟模型与算法研究

为了延长网络生命周期,减少网络能量消耗和均衡网络负载,引入了动态联盟思想,构造了无线传感器网络任务分配的动态联盟模型,继而提出了一种基于离散粒子群优化的任务分配算法.该算法根据任务总完成时间、能量损耗以及网络负载状况,建立代价函数,结合粒子群优化算法,实现优化任务分配策略.引入了变异算子,在很好地保持了种群的多样性的同时提高了算法的全局搜索能力.仿真实验结果表明了该分配算法在局部求解与全局探索之间取得了较好的平衡,能有效减少无线传感器网络的计算时间和网络能耗,并有效地均衡网络负载.     

无线传感器网络中带复杂联盟的自适应任务分配算法

针对无线传感器网络任务调度的实时性及节点计算及能量受限的特点,根据任务截止期赋予任务优先级,优先考虑高优先级任务,设计了一个无线传感器网络中带复杂联盟的自适应任务分配算法。为尽最大努力确保任务在截止期前完成,对截止期较为紧迫的任务采用历史信息生成历史联盟,并执行快速子任务分配算法;而对截止期较为宽裕的任务,在满足任务截止期约束条件下,以节点能耗和网络能量分布平衡为优化目标,采用矩阵的二进制编码形式,设计了一种离散粒子群优化算法以并行生成联盟,并执行基于负载和能量平衡的子任务分配算法。仿真实验结果表明所构造的自适应算法是有效的,在局部求解与全局探索之间能够取得较好的平衡,并能够在较短的时间内取得满意解。     

Clustering routing protocol based on improved PSO algorithm in WSN

Aiming at the problem that the location distribution of cluster head nodes filtered by wireless sensor network clustering routing protocol was unbalanced and the data transmission path of forwarding nodes was unreasonable,which would increase the energy consumption of nodes and shorten the network life cycle,a clustering routing protocol based on improved particle swarm optimization algorithm was proposed.In the process of cluster head election,a new fitness function was established by defining the energy factor and position equalization factor of the node,the better candidate cluster head node was evaluated and selected,the position update speed of the candidate cluster head nodes was adjusted by the optimized update learning factor,the local search and speeded up the convergence of the global search was expanded.According to the distance between the forwarding node and the base station,the single-hop or multi-hop transmission mode was adopted,and a multi-hop method was designed based on the minimum spanning tree to select an optimal multi-hop path for the data transmission of the forwarding node.Simulation results show that the clustering routing protocol based on improved particle swarm optimization algorithm can elect cluster head nodes and forwarding nodes with more balanced energy and location,which shortened the communication distance of the network.The energy consumption of nodes is lower and more balanced,effectively extending the network life cycle.     

基于服务质量的层次化结构资源分配算法

无线传感器网络(wireless sensor network,WSN)具有节点体积小、成本低、感知能力强等优势,被广泛应用于物联网(internet of things,IoT)场景中。如何在保证WSN负载平衡的前提下,提高业务服务质量(quality of service,QoS)成为众多学者关注的问题。在研究WSN中基于启发式的资源分配方法和基于层次化结构的资源分配方法基础上,提出了一种基于服务质量的层次化结构资源分配算法(quality of service based hierarchical resource allocation algorithm,QoSHRA)。首先,利用基于低能耗自适应聚类层次(low-energy adaptive clustering hierarchy,LEACH)协议的资源分配方法使整个网络形成层次化结构;然后,利用QoSHRA进行资源分配。仿真结果表明,QoSHRA在保证网络负载平衡的前提下,进一步节约了网络传输能耗,保障了业务分配的有效性,提高了网络资源的QoS需求满足率。     

Self-attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

WSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self-attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi-Objective Cluster Head Selection and Energy Aware Routing (SabGAN-AqOA-EgAwR-WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN-AqOA-EgAwR-WSN method is activated in NS2 simulator. Finally, the proposed SabGAN-AqOA-EgAwR-WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.     

基于改进灰狼优化算法的DV-Hop定位算法

针对无线传感器网络节点DV-Hop定位算法由于节点分布不均,距离估计不准确,导致定位精度较低的问题,提出了一种基于改进灰狼优化算法的DV-Hop定位算法,采用先进的灰狼优化算法以寻找最优值的方式得到未知节点、坐标。同时,为进一步提高优化算法的寻优能力,克服可能出现局部最优的情况,将优化算法与免疫算法相结合,提高优化算法中灰狼种群的多样性,进而提高对最优解的搜索能力,达到提高定位精度的目的。实验结果表明,相对于普通的DV-Hop定位算法和普通的灰狼优化算法,改进之后的定位算法精度更高。     

Fairness-oriented routing algorithm joint with power control and channel assignment for multi-radio multi-channel wireless mesh networks

The multi-radio multi-channel wireless mesh network （MRMC-WMN） draws general attention because of its excellent throughput performance, robustness and relative low cost. The closed interactions among power control （PC）, channel assignment （CA） and routing is contributed to the performance of multi-radio multi-channel wireless mesh networks （MRMC-WMNs）. However, the joint PC, CA and routing （JPCR） design, desired to achieve a global optimization, was poor addressed. The authors present a routing algorithm joint with PC and CA （JPCRA） to seek the routing, power and channel scheme for each flow, which can improve the fairness performance. Firstly, considering available channels and power levels, the routing metric, called minimum flow rate, is designed based on the physical interference and Shannon channel models. The JPCRA is presented based on the genetic algorithm （GA） with simulated annealing to maximize the minimum flow rate, an non-deterministic polynomial-time hard （NP-Hard） problem. Simulations show the JPCRA obtains better fairness among different flows and higher network throughput.     

基于多目标的MIMO无线mesh网络信道优化策略

MIMO是一种可有效提高无线网络信道带宽的技术。将MIMO技术应用在无线mesh网络中会遇到信道干扰和无线节点之间无协同策略等问题,导致网络效率降低。基于无线mesh网络中节点的多属性特征,以节点属性和内容分发为约束,提出了基于多目标优化算法与多层分发联合的调度和优化策略。实验结果表明,该算法能有效降低无线mesh网络分发数据过程中的时延,提高网络的吞吐表现和服务质量。     

无线Mesh网络中基于演化博弈的抗振荡信道分配策略

为抑制无线Mesh网络中的信道振荡和提升网络效用,综合分析路由端和用户端信道振荡产生的因素.引入演化博弈理论,联合改进的果蝇优化算法提出基于ESS-PFOA(Evolutionary Stable Status-Promoted Fruit-Flies Opti-mal Algorithm)算法的分布式信道分配策略.实验分析发现当信道振荡宽容因子β≤0.5时网络呈同构特征,β＞0.5时网络向异构转化,β≥0.9时网络呈异构特征;网络效用和信道振荡抑制率与信道振荡宽容因子β紧密相关.仿真结果表明,ESS-PFOA算法的信道振荡率从0.44下降至0.08,在异构网络环境下其网络收益和信道振荡抑制率明显占优,能有效提高网络效用.     

基于量子布谷鸟搜索的认知无线网络频谱分配

为了有效解决认知无线网络频谱分配的离散优化问题,将量子计算引入布谷鸟搜索算法,提出了一种新的组合优化算法——量子布谷鸟搜索算法。该算法使用量子鸟窝表征问题的多维解,通过Lévy flights随机游动方式和量子突变策略快速搜索到全局最优位置。通过使用基准函数验证了算法的高效性,并提出了一种基于量子布谷鸟搜索的认知无线网络频谱分配方法。然后与经典频谱分配算法在不同的网络效益函数下进行仿真性能比较。结果表明,所提出的频谱分配方法能够较快找到全局最优解,并且在不同网络效益函数下均优于已有的经典频谱分配算法。     

基于移动边缘计算的NOMA异构网络资源分配

以最大化缓存收益为目标,针对部署缓存的NOMA异构网络下的基站用户匹配及功率分配问题,结合消息传递及DC规划提出了NOMA联合优化算法。首先将约束条件合并到目标函数中,通过计算新的优化问题中函数节点与变量节点间消息传递的边缘得到用户协同结果;然后将原优化问题变形为2个凸函数差的形式,通过DC规划对功率资源进行分配;最后迭代计算得到最终的用户协同及功率分配结果。仿真结果证明所提算法有效地提升了网络性能。     

基于多门限预留机制的自适应带宽分配算法

针对异构无线网络融合环境提出了一种基于多门限预留机制的自适应带宽分配算法,从而为多业务提供QoS保证。该算法采用多宿主传输机制,通过预设各个网络中不同业务的带宽分配门限,并基于各个网络中不同业务和用户的带宽分配矩阵,根据业务k支持的传输速率等级需求和网络状态的变化,将自适应带宽分配问题转化为一个动态优化问题并采用迭代方法来求解,在得到各个网络中不同业务和用户优化的带宽分配矩阵的同时,在带宽预留门限和网络容量的约束条件下实现网络实时吞吐量的最大化,以提高整个异构网络带宽的利用效率。数值仿真结果显示,所提算法能够支持满足QoS需求的传输速率等级,减小了新用户接入异构网络的阻塞概率,提高了平均用户接入率并将网络吞吐量最大提高40%。     

联合速率控制与功率分配的多信道无线网络跨层优化

本文基于凸优化方法,以提升网络效用与降低网络总功耗为目标,针对多无线多信道(Multi-radio Multi-channel)的多跳无线网络提出了一种联合速率控制与功率分配的跨层优化模型,并利用对偶分解方法设计了优化模型对应的分布式算法,证明了该分布式算法收敛性.该算法通过改变本征权的取值能够在网络效用与网络功耗之间取得折衷,并能根据速率要求动态调整各条链路的注入速率与发射功率使得网络达到效用与功耗的联合最优.通过仿真实验验证了该分布式算法可有效的调节网络效用与总功耗之间的平衡.