无线传感器网络中无标度拓扑的动态容错性分析

增强网络的动态容错性对于抵御级联失效具有重要的现实意义.根据无线传感器网络无标度拓扑中节点可变负载和恒定容量的特点建立级联失效模型,在随机节点失效下研究负载参数和拓扑参数对其级联失效容错性的影响规律,解析推导出该网络大规模级联失效的承载极限,同时模拟结果发现该网络的级联失效容错性与其度分布系数和幂指数正相关.这为从参数优化角度抵御随机节点失效下无线传感器网络无标度拓扑级联失效危害提供了依据.     

一种无线传感器网络簇间拓扑演化模型及其免疫研究

基于复杂网络理论, 研究由于节点失效所导致的无线传感器网络性能下降的问题, 提出一种新的簇间拓扑演化模型, 在此基础上讨论病毒的免疫策略, 并给出一种新的免疫机理. 理论分析表明, 该模型演化生成的网络不仅具有较强的容错性, 而且还可以有效避免节点因能量很快耗尽而过早死亡. 研究还发现, 对于网络全局信息未知的情况, 与随机免疫和熟人免疫策略相比, 本文所提免疫策略能够获得较好的免疫效果. 通过数值仿真对理论分析进行验证.     

一种能耗均衡的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均衡问题,通过对节点生命期建模,得出节点生命期受节点剩余能量和通信距离的影响,进而将两端节点生命期作为构建拓扑时边权重的影响因子,通过边权重控制节点权重,最终得出了一种能耗均衡的无线传感器网络加权无标度拓扑模型,并理论证明了该模型的点权、边权和节点度均服从幂律分布.实验结果表明,该模型具有无标度拓扑的强容错性,并有效的均衡了网络中的节点能耗,延长了网络的生命期.     

基于综合故障的无线传感器网络无标度容错拓扑模型研究

针对无线传感器网络实际应用中遇到的环境损毁和能量耗尽的问题,本文首先对网络综合故障进行建模,获取满足综合故障容忍能力和网络生命期双重需求的网络节点度和节点度上限值的取值规律,并结合由无标度特征导出的两者关系,从而求得最优节点度上限值,最终引入关于节点度上限值的适应度函数,提出了容忍环境损毁和能量耗尽综合故障的无标度容错拓扑演化模型.仿真实验结果表明,该模型演化生成的无标度拓扑对环境损毁和能量耗尽具有较好的容错性,并能够有效地延长网络生命期.     

一种强容侵能力的无线传感器网络无标度拓扑模型研究

针对无线传感器网络无标度拓扑容侵能力差的问题,本文借助节点批量到达的Poisson网络模型,提出了一种具有容侵优化特性的无标度拓扑模型,并在构建拓扑时引入剩余能量调节因子和节点度调节因子,得到了一种幂率指数可以在(1,+∞)调节的无标度拓扑结构,并通过网络结构熵优化幂率指数,得出了具有强容侵特性的幂律指数值.实验结果表明:新的拓扑保持了无标度网络的强容错性,增强了无标度网络的容侵性,并具有较好的节能优势.     

复杂网络系统拓扑连接优化控制方法

为了增加实际网络系统连接增益、减少网络连接成本, 提出了一种基于网络效率和平均连接度的网络拓扑连接优化控制方法, 该方法利用网络效率来表征网络连接收益、用网络平均连接度来表征网络连接成本, 并提出了其计算优化算法, 该算法的时间复杂性为O(Mpn²). 实验分析表明, 可以采取一定的方式对实际复杂网络拓扑连接进行优化控制, 小世界和无标度网络均存在一个最佳的网络平均度值能够使网络连接增益达到最大. 关键词： 复杂网络 拓扑连接 优化控制 连接增益     

能量异构的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均和如何高效获得节点和边的负载问题,提出一种局域范围内能量异构的加权无标度拓扑演化模型.通过对节点能量与负载、能耗的关系建模,建立节点能量与点权和边权的联系,进而结合点权和加权模型给出网络的演化方式,推出点权、度和边权的幂率分布规律,最终根据网络获得的点权和边权来分析负载和能耗.仿真结果表明,提出的模型不仅能够准确计算点边的负载,而且缓解了无标度网络的节点能耗不均衡问题.     

基于博弈论的认知无线电网络跨层资源分配

针对多跳认知无线电网络的多层资源分配问题,提出了协作去耦合方法和跨层联合方法,协作去耦合方法首先单独完成路径选择任务,随后进行信道与功率的博弈分配;跨层联合方法则通过博弈直接对路径、信道、功率三层资源进行同时分配,两种方法都综合考虑网络层、介质访问控制层、物理层的启发原则,引入了节点被干扰度信息和节点主动干扰度信息来辅助路径选择,设计了基于功率允许宽度信息的Boltzmann探索来完成信道与功率选择,设计了长链路和瓶颈链路替换消除机制以进一步提高网络性能,从促进收敛角度,选择序贯博弈并设计了具体的博弈过程,此外还分析了博弈的纳什均衡,讨论了两种算法的复杂度,仿真结果表明,协作去耦合方法和跨层联合方法在成功流数量、流可达速率、发射功耗性能指标上均优于简单去耦合的链路博弈、流博弈方法。     

基于节点健康度的无线传感器网络冗余通路控制方法

广泛应用于各种物理参数测量领域的无线传感器网络,因其节点具有能量供应有限、硬件资源有限、数目众多、自组织和动态拓扑等特点,使得网络极易发生故障,从而高可靠、低故障是其运行的基本要求.本文针对多冗余通路设计的无线传感器网络故障预防方法存在工作状态冗余节点过多、能量大量浪费的问题,提出一种基于节点健康度的冗余通路控制方法.该方法利用汇聚节点收集网络内所有节点能量状态,计算节点健康度等相关参数,使用A-Star算法选择最优工作通路,控制其余冗余通路分批轮流休眠,从而达到减少和均衡网络工作过程能量消耗、预防某些节点能量提前耗尽导致网络能量故障发生的目的.仿真实验和实际节点实验的结果表明,在保证网络适当冗余通路的前提下,与其他相关方法比较,该方法可以显著均衡网络能量消耗,有效预防节点能量故障提前发生,明显延长网络寿命.     

一种优化无线传感器网络生命周期的容错拓扑研究

<正>由于无线传感器网络的节点能量受限,优化网络生命周期成为设计网络拓扑时首要考虑的问题.通过分析节点的剩余能量和负载量对节点生命周期的影响,提出了一种可延长无线传感器网络生命期的容错拓扑演化模型,并得出了在节点满足网络生存时间的条件下负载调节系数的取值范围.仿真实验结果表明,基于无标度网络的演化拓扑结构具有较好的容错性,并能够均衡网络节点能耗和延长网络生命周期.     

认知无线网络中基于Sigmoid函数的功率控制算法研究

为了实现认知无线网络中频谱分配公平性以及契合现代化绿色通信的需求,根据非合作博弈论和干扰温度,引入信道状态概念,设计出一种新型功率控制算法,分析了该算法的收敛性、纳什均衡解的存在性和唯一性。该算法不仅可以快速收敛,符合实时通信,而且分布式实施,简单实用。仿真结果表明,相比其他算法,该算法系统干扰小,能源消耗低,具有抗干扰性能,而且在日益多用户网络的情况下,具有低功率、低干扰,提高网络的整体效益,更加符合现代化的绿色通信的需求。     

基于混沌人工蜂群算法的无线传感器网络覆盖优化

针对无线传感器网络随机播撒的节点严重冗余并且导致网络寿命短、覆盖效率不高等缺陷,提出了一种混沌人工蜂群算法的无线传感器网络覆盖优化算法;将节点的利用率和覆盖率作为优化目标函数,建立与之对应的数学模型,之后用混沌人工蜂群算法改善人工蜂群算法陷入局部最优、收敛慢等问题,提高算法收敛速度和精度,对节点覆盖模型进行求解,得出网络最优覆盖方案;通过实验仿真,提出的算法提高了无线传感器网络的覆盖率,覆盖率可达93.48%以上,减少了网络节点冗余,提高了网络寿命,降低了网络成本。     

一种改进的DV-HOP算法在无线传感网中的定位研究

WSN中的信息传递主要通过传感器来进行传递信号,针对无线传感中DV-Hop算法在节点定位上存在精度低的问题,本文首先提出建立双曲线二维模型用来确定锚节点与未知节点的距离关系,其次设定误差系数使得传感器节点之间的误差降低,最后采用斯蒂芬森迭代法(Steffensen)定位方法对传感器节点进一步进行定位修正。仿真实验表明本文算法的在远程控制的中定位精度提高,传感器之间能量消耗降低,具有一定的推广价值。     

基于状态反馈控制的无线网络拥塞控制流体流模型的Hopf分岔

针对控制无线网络拥塞控制系统中流体流模型的Hopf分岔的问题,提出一种状态反馈控制器.通过选择通信时延作为分岔参数,验证模型在加入状态反馈控制器后,①增加了分岔参数的临界值,扩大了稳定性区域,使系统的Hopf分岔延迟;②通过选择合适的参数,可以容易地改变分岔周期解的稳定性及其分岔方向.理论分析和数据仿真验证了该方法能够有效地控制系统的Hopf分岔.     

Outage Performance Analysis of NOMA in Wireless Powered Cognitive Radio Networks with AF and DF Relaying Techniques

Improving spectral efficiency under a certain energy limitation is an important design metric for future wireless communications as a response to the growing transmission demand of wireless devices. In order to improve spectral efficiency for communication systems without increasing energy consumption, this paper considers a non-orthogonal multiple access (NOMA)–based cognitive radio network, with the assistance of a wireless-powered relay station (RS), and then analyzes the system outage performance under amplified-and-forward (AF) and decoded-and-forward (DF) cooperative transmission modes. Specifically, the base station (BS) has the opportunity to cooperate by transmitting information through the RS, depending on whether the RS can harvest sufficient RF energy for cooperative transmission. That is to say, when the energy stored by the RS is sufficient for cooperative transmission, the RS will assist the BS to forward information; otherwise, the BS will send information through direct links, while the RS converts the radio frequency (RF) signals sent by the BS into energy for future transmission. Moreover, the transmission power required by the RS for cooperative transmission is usually relatively large, while the amount of harvested energy by the RS in a transmission slot is usually low, so it takes several consecutive time slots to accumulate enough transmission energy. To this end, we utilize a discrete-time Markov chain to describe the processes of charging and discharging of the RS. Subsequently, we derive the closed-form outage probabilities of both the primary and secondary systems for the considered system in AF and DF modes through mathematical analysis, and verify the accuracy of the analyses through Monte Carlo simulation. The simulation results show that the two proposed cooperative transmission schemes with AF and DF relaying techniques outperform both direct transmission and other similar schemes in both the primary and secondary system, while the DF scheme can provide better performance than the AF scheme within the range of setting values.     

基于无线传感网的温室花卉自适应调控系统

针对传统温室控制系统大多只是简单地采集环境参数如空气温湿度,并且缺乏长期评估和有效反馈机制的问题,文章提出了一种基于无线传感网的温室花卉自适应调控方法,并实现了一个综合传感、决策、执行反馈的软硬件系统。为此,系统中设计了基于Zigbee的传感器节点模块、传输网关和控制指令集。传感器节点模块可以灵活地布置并实时可靠的传输数据。中间件系统则通过控制指令集接收传感数据和发送反馈控制指令。上层应用管理软件可以实时查看花卉、温室和设备的实时状态以及各种参数。运行结果表明,本系统可以实时采集温室温湿度,并结合专家规则通过中间件系统对花卉生长环境进行有效的评估和精准的反馈调控,达到了在降低温室花卉培育成本的同时,也提高了高端花卉培育成功率的目的。     

A Novel Epidemic Model Base on Pulse Charging in Wireless Rechargeable Sensor Networks

As wireless rechargeable sensor networks (WRSNs) are gradually being widely accepted and recognized, the security issues of WRSNs have also become the focus of research discussion. In the existing WRSNs research, few people introduced the idea of pulse charging. Taking into account the utilization rate of nodes’ energy, this paper proposes a novel pulse infectious disease model (SIALS-P), which is composed of susceptible, infected, anti-malware and low-energy susceptible states under pulse charging, to deal with the security issues of WRSNs. In each periodic pulse point, some parts of low energy states (LS nodes, LI nodes) will be converted into the normal energy states (S nodes, I nodes) to control the number of susceptible nodes and infected nodes. This paper first analyzes the local stability of the SIALS-P model by Floquet theory. Then, a suitable comparison system is given by comparing theorem to analyze the stability of malware-free T-period solution and the persistence of malware transmission. Additionally, the optimal control of the proposed model is analyzed. Finally, the comparative simulation analysis regarding the proposed model, the non-charging model and the continuous charging model is given, and the effects of parameters on the basic reproduction number of the three models are shown. Meanwhile, the sensitivity of each parameter and the optimal control theory is further verified.     

A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks

Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Mahalanobis sampling surrogate model assisting Ant Lion optimization algorithm to address this problem. For expensive calculation problems, the optimization effect goes even further by using MSAALO. This model includes three surrogate models: the global model, Mahalanobis sampling surrogate model, and local surrogate model. Mahalanobis distance can also exclude the interference correlations of variables. In the Mahalanobis distance sampling model, the distance between each ant and the others could be calculated. Additionally, the algorithm sorts the average length of all ants. Then, the algorithm selects some samples to train the model from these Mahalanobis distance samples. Seven benchmark functions with various characteristics are chosen to testify to the effectiveness of this algorithm. The validation results of seven benchmark functions demonstrate that the algorithm is more competitive than other algorithms. The simulation results based on different radii and nodes show that MSAALO improves the average coverage by 2.122% and 1.718%, respectively.     

基于改进蛙跳算法的无线传感器网络覆盖优化

针对传统算法在解决无线传感器网络覆盖优化上存在的覆盖率较低和节点分布不够均匀的问题,提出了一种改进的蛙跳算法;为了同时达到增加算法的种群多样性和加快算法收敛速度的目的,改进蛙跳算法分别增加了个体高斯学习机制和根据粒子群思想改进的更新策略,让族内最差个体在自身附近进行局部搜索,若无效,则使族内最差个体同时向族内最优个体和全局最优个体学习;在性能评估实验中,对改进的蛙跳算法分别进行了标准函数测试和无线传感器网络覆盖优化测试;测试结果表明,在6个标准测试函数中,改进的蛙跳算法与其他算法相比在4个测试函数上的收敛精度有了明显提高;在无线传感器网络覆盖优化中,改进的蛙跳算法也能够使节点分布更加均匀,使网络覆盖率达到了85.6%。      

Attention-Shared Multi-Agent Actor–Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks

The breakthrough of wireless energy transmission (WET) technology has greatly promoted the wireless rechargeable sensor networks (WRSNs). A promising method to overcome the energy constraint problem in WRSNs is mobile charging by employing a mobile charger to charge sensors via WET. Recently, more and more studies have been conducted for mobile charging scheduling under dynamic charging environments, ignoring the consideration of the joint charging sequence scheduling and charging ratio control (JSSRC) optimal design. This paper will propose a novel attention-shared multi-agent actor–critic-based deep reinforcement learning approach for JSSRC (AMADRL-JSSRC). In AMADRL-JSSRC, we employ two heterogeneous agents named charging sequence scheduler and charging ratio controller with an independent actor network and critic network. Meanwhile, we design the reward function for them, respectively, by considering the tour length and the number of dead sensors. The AMADRL-JSSRC trains decentralized policies in multi-agent environments, using a centralized computing critic network to share an attention mechanism, and it selects relevant policy information for each agent at every charging decision. Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms.