认知无线网络中基于遗传算法的异构信道频谱感知策略

在具有异构信道的认知无线网络环境中,各异构信道具有不同带宽、不同感知时间、不同使用概率、不同稳定度。在给定的时间槽内,感知的信道数目不同,以及感知的信道不同,频谱感知获得的带宽也不同,并且频谱感知耗费的时间也不同,传输的时间也不同。因此,针对如何选择待感知的信道,提高次用户获得的带宽,以及用于传输的时间,提出基于遗传算法的异构信道频谱感知策略GAHS(Genetic Algorithm based on Heterogeneous channels spectrum Sensing strategy)。仿真结果表明GAHS能提高节点获得的带宽和用于传输的时间。     

移动网络中基于数据到达速率的数据传输能耗优化策略

数据传输能耗是移动网络能耗的重要部分,提高数据传输能耗效率是优化移动网络能耗的重要课题。考虑数据具有传输延时的要求,研究了基于数据到达速率的数据传输平均能耗最小化问题。利用无线信道质量随机变化的特征,构建基于数据到达速率的平均能耗最小化问题,然后将其转化为最优停止问题,证明最优停止规则存在。最后通过求解最优近视停止规则来获得各侦测时刻的最优传输速率阈值,实现基于数据到达速率的数据传输能耗优化策略。对提出的策略与其他策略就平均能耗、平均传递率和平均调度周期进行了仿真对比,结果表明提出的策略具有较小的平均能耗和较高的平均传递率,取得了较好的能耗优化效果。     

基于相似性度量的无线网络数据传输信道优化

在无线网络数据传输和通信过程中,由于网间路由的损伤和通信码元之间的码间干扰,导致数据传输信道失衡,需要对无线网络数据传输信道进行抗干扰滤波和均衡设计,实现无线网络数据的高保真性传输。传统方法采用复合链路相干均衡算法进行无线网络数据传输信道优化,随着通信信道载波数量的增长,导致信道均衡稳定性不好。提出一种基于数据通信码元相似性度量的无线网络数据传输信道优化算法,通过构建无线网络系统模型和信道模型,对无线网络的数据信息进行码元相似性度量和特征提取,以此为基础采用IIR滤波算法实现对信道干扰码元的滤波和抗干扰算法改进,实现对无线网络的数据传输信道优化和均衡设计,提高数据通信码的保真性传输能力。仿真结果表明,采用该算法能有效实现无线网络数据传输信道优化构建,信道的均衡性能和抗干扰性能较好,降低了数据传输的误比特率。     

基于感知时间和传输时间的认知无线网络单信道选择联合优化模型

为了确保认知无线网络中次用户对主用户的干扰低于预定值的同时,最大化次用户系统的吞吐量,减少次用户丢失频谱机会的次数,提出单信道模式下次用户感知时间和传输时间被联合优化的算法。在单信道模型中,次用户以混合接入策略与主用户共享一条信道,通过综合考虑对主用户的干扰和次用户频谱机会丢失两个因素,对传输时间和感知时间进行联合优化。经过与固定的感知时间和传输时间算法仿真分析对比,验证了算法的有效性。     

认知无线网络中多信道频谱感知周期优化算法

为了使认知无线电次用户发现并使用更多的空闲频谱资源,提出了一种多信道频谱感知周期优化算法。针对实际网络中各授权信道使用规律的不同,本文基于交替更新理论建立了多信道状态转移模型,将各信道感知周期的选取建模为一个带约束条件的多目标优化问题,并采用遗传算法对其求解,获得了相对较优的多信道感知周期向量。仿真结果表明,本文提出的目标函数能够有效衡量多信道空闲频谱资源检测率。当目标网络中包含8个授权信道时,所提算法可发现的空闲频谱资源占实际空闲频谱资源的68.23%,相对于以相同周期对各信道进行频谱感知的OFDM机制提高17.68%。     

认知无线网络信道接入协议

由于认知无线网络(cognitive radio network,简称CRN)固有\"二次利用\"的特性,使其日益得到重视.而作为CRN核心构成的MAC(medium access control)协议,业已成为当前各研究机构的一个热点.主要对频谱感测技术、信道接入技术等MAC层核心设计问题进行了探讨,并针对认知无线网络MAC的特性及需求进行了分析,然后对设计MAC频谱感知技术、信道接入技术、频谱共享技术等相关研究进展进行了归类总结.最后指出了当前面临的主要研究难点及挑战,并提出了一些方向性建议.     

多射频无线网络中多信道分配方法的研究

针对多射频多信道无线网络信道分配中用户收益不均衡和网络资源利用率低的问题,给出了一种基于博弈论的信道分配策略,该策略在考虑信道分配有效性、公平性的同时,基于不完美信息博弈,给出了一种使网络负载更均衡的算法,并通过实验仿真验证了算法的鲁棒性和有效性。     

一种认知无线网络中公共控制信道的动态建立方法

针对目前对认知无线网络公共控制信道研究较少的现状,提出了一种在不增加额外成本、不采用其他通信方式且不影响授权用户使用的前提下,根据信道空闲程度优先序列动态建立认知无线网络公共控制信道的方法。用网络行为仿真软件OPNET建立基于该方法的完整的系统模型和仿真环境,根据仿真结果设计了基于"通信成功率"和"公共信道建立时间"加权的通信质量优化方案,提出了相应的归一化性能函数,并以此为基础确定了优化预设公共控制信道数目这一主要参数。     

基于压缩感知的信道估计技术

通信系统中的传统信道估计方法均基于多信道密集型假设,导致频谱利用率低下,压缩感知理论为解决这一问题提供了一种新的途径。本文介绍了压缩感知基本理论,探讨了压缩感知应用于信道估计的可行性,详细分析了压缩感知信道估计技术的MP算法、OMP算法、CoSaMP等几种重构算法。研究表明基于压缩感知理论的信道估计方法能利用较少的导频信号达到与传统方法相比拟的估计性能,从而提高频谱利用率。     

基于能耗量化传导的WSN路由探测算法

为了降低无线传感器网络(WSN)路由节点的能量损耗,提高网络的寿命周期,需要进行路由节点的优化分布设计。传统方法采用CSMA/CA有限竞争的信道分配模型进行WSN的路由探测算法设计,实现能量均衡,在节点规模较大和干扰较强时,节能的能耗开销较大。提出一种基于能耗量化传导的WSN路由探测算法,首先建立WSN的分簇能耗调度模型,以能量控制开销、丢包率、传输时延等为约束参量指标进行路由探测的控制目标函数的构建,然后采用路由冲突协调机制进行能耗量化分配,结合WSN传输信道的能量传导均衡模型实现WSN路由的优化探测和WSN节点的优化部署。仿真结果表明,采用该方法进行WSN路由探测设计时网络的能效较高,传输时延和误码率等参量指标的表现优于传统方法。     

移动多sink传感器网络数据发布协议的QoS保障研究

在移动多sink无线传感器网络应用中,传感器节点感知目标信息并暂存起来,sink通过向网络发出查询来收集感知信息,如何保障数据发布过程中的服务质量(QoS)是具有挑战性的工作。首先分析数据发布过程的特点,提出将能量消耗、查询成功率和服务时延作为数据发布协议服务质量的核心指标。然后讨论了数据发布协议相关技术与QoS核心指标间的关联关系,给出数据发布协议的QoS协商过程。最后分析了未来的研究趋势,为QoS感知的数据发布协议设计提供参考。     

Optimal,quality-aware scheduling of data consumption in mobile ad hoc networks

In this paper we study the delivery of quality contextual information in mobile ad-hoc networks. We consider that information has a certain quality level that fades over time. Mobile context-aware applications receive and process disseminated information given that the corresponding quality is above the lowest level. The necessity for optimally scheduling information delivery arises from the dynamic nature of the network, e.g., probabilistic spreading, caching, deferred delivery, and mobility of nodes. We propose two policies for optimal scheduling information delivery consumption based on the Optimal Stopping Theory. The mobile nodes delay the reporting of information to mobile context-aware applications in search for better quality. The proposed policies efficiently deal with the delivery of quality information in mobile ad-hoc networks.     

Time-optimized contextual information forwarding in mobile sensor networks

We study on the forwarding of quality contextual information in mobile sensor networks (MSNs). Mobile nodes form ad-hoc distributed processing networks that produce accessible and quality-stamped information about the surrounding environment. Due to the dynamic network topology of such networks the context quality indicators seen by the nodes vary over time. A node delays the context forwarding decision until context of better quality is attained. Moreover, nodes have limited resources, thus, they have to balance between energy conservation and quality of context. We propose a time-optimized, distributed decision making model for forwarding context in a MSN based on the theory of optimal stopping. We compare our findings with certain context forwarding schemes found in the literature and pinpoint the advantages of the proposed model.     

Optimal energy allocation in heterogeneous wireless sensor networks for lifetime maximization

We consider the problem of optimal energy allocation and lifetime maximization in heterogeneous wireless sensor networks. We construct a probabilistic model for heterogeneous wireless sensor networks where sensors can have different sensing range, different transmission range, different energy consumption for data sensing, and different energy consumption for data transmission, and the stream of data sensed and transmitted from a sensor and the stream of data relayed by a sensor to a base station are all treated as Poisson streams. We derive the probability distribution and the expectation of the number of data transmissions during the lifetime of each sensor and the probability distribution and the expectation of the lifetime of each sensor. In all these analysis, energy consumption of data sensing and data transmission and data relay are all taken into consideration. We develop an algorithm to find an optimal initial energy allocation to the sensors such that the network lifetime in the sense of the identical expected sensor lifetime is maximized. We show how to deal with a large amount of energy budget that may cause excessive computational time by developing accurate closed form approximate expressions of sensor lifetime and network lifetime and optimal initial energy allocation. We derive the expected number of working sensors at any time. Based on such results, we can find the latest time such that the expected number of sensors that are still functioning up to that time is above certain threshold.     

无线传感器网络在环境监测系统中的应用

介绍了在环境监测系统中应用无线传感器网络技术的几点优势,分析了基于无线传感器网络技术的环境监测系统的体系结构,给出了三个典型应用领域中该系统的创新性构建方案,并对该类系统中的几种关键技术进行了研究,最后对无线传感器网络技术的应用前景进行了展望。     

能量高效的无线传感器网络路由协议

针对目前无线传感器网络分簇路由协议存在的节点能耗不均衡的问题,提出一种基于分簇思想的能量高效的多跳路由协议(EEMR)。该协议首先基于节点临近度将网络划分成簇,采用簇首自适应轮转模式优化簇内节点通信的能量消耗,以高剩余能量短路径向心角的适应度路由算法均衡簇间通信负载和能量消耗,有效避免多跳路由中出现的能量消耗不均衡问题。仿真结果表明,EEMR协议能有效均衡网络内节点的能量消耗,显著延长无线传感器网络的生命期并提高网络能量利用率。     

Practical data compression in wireless sensor networks: A survey

Power consumption is a critical problem affecting the lifetime of wireless sensor networks. A number of techniques have been proposed to solve this issue, such as energy-efficient medium access control or routing protocols. Among those proposed techniques, the data compression scheme is one that can be used to reduce transmitted data over wireless channels. This technique leads to a reduction in the required inter-node communication, which is the main power consumer in wireless sensor networks. In this article, a comprehensive review of existing data compression approaches in wireless sensor networks is provided. First, suitable sets of criteria are defined to classify existing techniques as well as to determine what practical data compression in wireless sensor networks should be. Next, the details of each classified compression category are described. Finally, their performance, open issues, limitations and suitable applications are analyzed and compared based on the criteria of practical data compression in wireless sensor networks.