Dynamically adaptive channel reservation scheme for cellular networks

In personal communications networks (PCN) supporting network-wide handoffs, new and handoff requests compete for connection resources in both mobile and backbone networks. Forced call terminations due to handoff call blocking are generally more objectionable than new call blocking. In general, most of the previously proposed schemes for radio channel allocation in cellular networks reduce handoff call blocking probability substantially at the expense of increasing the new call blocking probability by giving higher priority to handoff calls over new calls in admission control. This reduces the total admitted traffic and results in inefficient utilization of wireless channels. The tradeoff between the new and handoff calls blocking probabilities should be defined on importance basis. In this paper, we propose a performance metric equation that makes a trade off between the two probabilities depending on the network preferences. Using this equation, we study the performance of various proposed channel reservation schemes. Also in this paper, a new dynamically adaptive channel reservation scheme (DACRS) is developed and compared with other schemes proposed in the literature. The DACRS assigns handoff-reserved channels to new calls depending on the locality principle in which the base station with the help of location estimation algorithms in the mobile location center predicts the position of the mobile terminal. Eventually, the DACRS is designed to improve channel utilization while satisfying the QoS of the calls. As will be shown analytically and through simulation, the DACRS outperforms current reservation schemes and results in more statistical gain, and powerful channel utilization.     

基于固有时间尺度分解模型的通信辐射源特征提取算法

针对当前通信辐射源个体识别方法准确率不高,特征提取效率不高等问题,提出了基于时间尺度分解(ITD)模型的通信辐射源特征提取算法。该算法提取了原始信号特征、信号经ITD分解后得到的固有旋转分量特征以及瞬时幅频谱的特征组成特征向量,使用支持向量机(SVM)得到分类结果。通过6部实际电台的分类实验结果表明：该算法在不需要先验信息的前提下,可以得到较好的分类效果,并且相对与经验模态分解(EMD)的特征提取在分类效果及运算效率上都有一定程度的提升。     

An efficient approach for distributed dynamic channel allocation with queues for real-time and non-real-time traffic in cellular networks

We are witnessing these days a rapid growth of mobile users. Therefore, frequency spectrum must be efficiently utilized, as available frequency spectrum is limited. This paper proposes a channel allocation scheme with efficient bandwidth reservation, which initially reserves some channels for handoff calls, and later reserves the channels dynamically, based on the user mobility. The direction of user mobility may not be straight always, but the user may also go left, right or backwards. Thus, QoS can be improved, if the channel reservation is made based upon the user mobility and the location of the user. We devise here a new algorithm that deals with multiple traffic systems by modifying the existing DDCA algorithm [Krishna, P.V., Iyengar, N.Ch.S.N., 2008. Optimal channel allocation algorithm with efficient channel reservation for cellular networks. International Journal of Communication Networks and Distributed Systems 1 (1), 33-51]. This algorithm reserves more channels for hot cells, less number of channels for cold cells and an average number of channels for the medium cells. Furthermore, we maintain queues for all types of calls. We model the system by a three-dimensional Markov Chain and compute the QoS parameters in terms of the blocking probability of originating calls and the dropping probability of handoff calls. The results indicate that the proposed channel allocation scheme exhibits better performance by considering the above mentioned user mobility, type of cells, and maintaining of the queues for various traffic sources. In addition, it can be observed that our approach reduces the dropping probability by using reservation factor.     

基于贝叶斯的改进WSNs信任评估模型

基于贝叶斯和熵,提出一种改进的WSNs信任评估模型。考虑到非入侵因素带来的网络异常行为,引入异常衰减因子,利用修正后的贝叶斯方程估算直接信任,并利用滑窗和自适应遗忘因子进行更新。根据直接信任的置信水平确定其是否足够可信来作为综合信任,减少网络能耗,并降低恶意反馈的影响。如果直接信任不足够可信,计算间接信任来获得综合信任,利用熵来对不同的推荐赋予权重,克服主观分配权重带来的局限性,加强模型的适应性。仿真实验表明,该模型能够有效检测恶意节点,具有较高的检测率和较低的误检率,同时在很大程度上降低了网络的能量消耗。     

On the accuracy of two analytical models for evaluating the performance of Gigabit Ethernet hosts

In this paper we develop and assess the accuracy of two analytical models that capture the behavior of network hosts when subjected to heavy load such as that of Gigabit Ethernet. The first analytical model is based on Markov processes and queuing theory, and the second is a pure Markov process. In order to validate the models and assess their accuracy, two different numerical examples are presented. The two numerical examples use system parameters that are realistic and appropriate for modern hardware. Both analytical models give closed-form solutions that facilitate the study of a number of important system performance metrics. These metrics include throughput, latency, stability condition, CPU utilizations of interrupt handling and protocol processing, and CPU availability for user applications. The two models give mathematically equivalent closed-form solutions for all metrics except for latency. To address latency, we compare the results of both models with the results of a discrete-event simulation. The latency accuracy of the two models is assessed relative to simulation in terms of differences and percentage errors. The paper shows that the second model is more accurate.     

Utility max–min fair resource allocation for communication networks with multipath routing

This paper considers the flow control and resource allocation problem as applied to the generic multipath communication networks with heterogeneous applications. We propose a novel distributed algorithm, show and prove that among all the sources with positive increasing and bounded utilities (no need to be concave) in steady state, the utility max–min fairness is achieved, which is essential for balancing Quality of Service (QoS) for different applications. By combining the first order Lagrangian method and filtering mechanism, the adopted approach eliminates typical oscillation behavior in multipath networks and possesses a rapid convergence property. In addition, the algorithm is capable of deciding the optimal routing strategy and distributing the total traffic evenly out of the available paths. The performance of our utility max–min fair flow control algorithm is evaluated through simulations under two representative case studies, as well as the real implementation issues are addressed deliberately for the practical purpose.     

基于实测模型的通信铁塔抗震性能仿真分析

针对目前通信铁塔抗震性能仿真分析结果存在较大误差的问题,提出基于实测模型进行仿真分析的方法.通过现场测试得到铁塔的动态参数,依据测得的动态参数对建成的有限元模型进行修正,使其能更加准确地反映铁塔的动力特性.依据相关标准,选用人工合成地震波和2条真实地震波对铁塔模型进行抗震性能分析.结果表明铁塔的响应位移和应力均小于标准要求值,满足抗震要求.     

An analytical model for mixing based on time-interleaved sequential segmentation

Due to constrains caused by the laminar flow in microscale, effective and fast mixing is important for many microfluidic applications. From the scaling law, decreasing the mixing path can shorten the mixing time and enhance the mixing quality. One of the techniques for reducing mixing path is time-interleaved sequential segmentation. This technique divides solvent and solute into segments in axial direction. The mixing path can be controlled by the switching frequency and the mean velocity of the flow. In this brief communication, we present a simple time-dependent one-dimensional analytical model for time-interleaved sequential segmentation. The model considers an arbitrary mixing ratio between solute and solvent as well as the axial Taylor–Aris dispersion. The analytical solution indicates that the Peclet number is the key parameter for this mixing concept.     

基于信道丢包补偿技术的多源信息融合研究

在多传感器融合网络中,由于网络本身的内在特性以及受实际应用环境的影响,在数据传输过程中丢包现象经常发生,大量有用的信息丢失直接影响了融合算法的正常运行.现有的一些处理网络丢包现象的方法都是直接将丢包的传感器排除在融合操作之外,因此整个系统的融合精确度大大降低.针对上述问题,提出利用基于最小二乘的多项式拟合技术,对丢失的传感器信息进行近似补偿,从而提出一种基于信道丢包补偿技术的数据融合算法.另外,考虑到集中式融合存在的高计算复杂度的特性,基于信息更新的分布式融合算法被用来融合所有传感器的信息,性能分析和计算机仿真显示了新算法的有效性.     

ROBIN: A novel personal recommendation model based on information propagation

With the rapid development of the Internet technology, we have now entered the era of information overloading. Recommendation System technology can recommend web resources or information to people based on his/her personal preference, and has gotten a great deal of attention and development in recent years. In this paper, by combining collaborative filtering technology and information propagation principle, we proposed ROBIN, a novel recommendation model. The ROBIN model achieves a good recommendation effect by propagating the relationship information between users and resources. Based on the ROBIN model, we designed and implemented tag recommendation algorithm named ROBIN-T. For evaluating our proposed method, we have conducted tag recommendation experiments on three real datasets and the results show that the ROBIN-T algorithm achieves good performance when compared with classical approaches.     

基于超图理论的无线传感器网络可靠通信协议

利用超图理论进行建模,从传感器网络的传输层、网络层和MAC层分别进行分析,提出了一种无线传感器网络可靠通信协议RCBHGT,Sink节点发送泛洪,先建立超边路由,然后数据采集节点发送RREQ,建立超边内路由,利用超边内数据确认机制,保证了数据可靠稳定传输,仿真结果表明:在通信环境比较恶劣的情况下,RCBHGT能够使用较低的能耗,保证数据的可靠传输.     

Analytical model based on green criteria for optical backbone network interconnection

Key terms such as Global warming, Green House Gas emissions, or Energy efficiency are currently on the scope of scientific research. Regarding telecommunications networks, wireless applications, routing protocols, etc. are being designed following this new “Green” trend. This work contributes to the evaluation of the environmental impact of networks from physical interconnection point of view. Networks deployment, usage, and disposal are analyzed as contributing elements to ICT's (Information and Communications Technology) CO₂ emissions. This paper presents an analytical model for evaluating and quantifying the CO₂ emissions of optical backbone networks during their lifetime. The main goal of this work is to present the model and illustrate how to evaluate the physical interconnection of backbones from an environmental perspective. This model can be applied as a new type of decision support criteria for backbone's interconnection, since minimization of CO₂ emissions is becoming an important factor. In addition, two case studies are presented to illustrate the use and application of this model, and the need for de facto and international standards to reduce CO₂ emissions through good network planning.     

基于协同分集的无线传感网络路由与信道分析

协同分集是一种新近提出的空域分集技术,它使得终端之间可以共享彼此的天线,为实现多输入多输出(MIMO)提供了有效的方式,适合用于无线Ad hoc网络和无线传感网络中,成为研究热点.在分析协同分集技术的基础上,针对无线传感网络的特点,分析了传感网络中多跳路由算法与协同分集路由算法,给出网络的等效信道模型,并由此讨论使用协同分集对网络能源消耗的影响,讨论了基于协同分集的无线传感网络的实现.分析结果表明,协同分集在无线传感网络中具有较好的应用价值.     

基于小波分析的网络通信大数据谱聚类算法研究

传统谱聚类算法存在聚类效果差的缺陷,为此提出基于小波分析的网络通信大数据谱聚类算法研究。采用小波分析方法对采集到的电网通信网络大数据的相异性进行度量,将得到的相异性度量结果转换为数据之间的相似性,并对网络通信大数据相似性关系进行构建,得到网络通信大数据的相似度矩阵,以上述得到的网络通信大数据相似度矩阵为基础,采用聚类算法对数据进行聚类,实现了网络通信大数据的谱聚类。通过实验可得,提出的谱聚类算法的准确率与纯度分别高出传统算法34%与21.2%,说明提出的基于小波分析的谱聚类算法具备极好的聚类效果。     

基于车辆健康状态评估的自适应分簇算法

车间（V2V）安全预警信息（SWM）交换是重要的道路安全改进技术。SWM的高概率生成很容易导致局部信息风暴,严重影响SWM传输性能。为了降低局部信息负载,定义通信需求的概念,并且根据通信需求关系,提出面向SWM的分簇算法,实现道路车辆的自适应分簇。分簇算法中基于车辆健康状态评估的簇头选择方法能有效加强簇的稳定性和降低网络开销。为了进一步降低局部信息风暴和提升SWM传输性能,提出一种基于簇内通信需求数量,自适应调整簇半径的方法。采用Anylogic仿真平台验证算法有效性,仿真结果显示,该分簇算法具有较高的稳定性并且能有效提高SWM传输性能。     

基于大数据的电力通信网络风险辨识与评估方法研究

为了高效、准确地识别网络庞大、结构复杂电力通信网中所潜在的风险点,从网络结构、承载业务等方面分析其对电力通信的影响,并结合电力事故事件调查规程的评定标准,客观且科学地评估其对电力通信引起的电力安全事件,为电力通信风险管控提供决策支撑。本文结合电力通信风险管控具体内容及特点,分析电力通信网事故发生原因,推理事故演变过程,提出解决思路和方案,并通过实际测试案例,验证电力事故评估模型具有较高的计算效率和准确性,为准确定位事故发生原因和评判风险等级提供科学依据。该模型结合复杂网络理论可靠性因素的抗毁性、生存性、有效性等特征,计算其相关特性的风险因子值;利用通径系数分析技术,验证风险因子的完备性;根据已标注的风险等级样例数据,构建深层卷积神经网络CNN模型,实时评测事故的风险等级。     

基于故障传播模型与监督学习的电力通信网络故障定位

针对电力通信网络中的故障定位问题,分析了一种网络设备或链路故障引发的大范围连通片故障告警情形,提出一种基于故障传播模型和监督分类学习方法的故障定位算法。首先使用改进的故障传播模型求得初步定位结果,用最少的故障数目解释当前告警;然后通过故障源-故障告警向量分解将故障定位问题转化为监督分类问题,定位告警区域内部故障;最后加入猜测的故障设备和故障链路完善定位结果以提高定位准确率。模拟结果表明提出的故障定位算法的故障检测率达到84%~95%,具有较高的故障定位可靠性。     

无线传感器网络跳数和通信距离自适应差错控制策略

无线传感器网络(WSNS)的动态信道特性、网络带宽和节点能量受限等特点,使得在无线环境下为数据传输提供可靠性保证面临更大的挑战.本文提出一种适用于WSNS的自适应链路层差错控制策略(AEC-Hops/RSSI),以便改善WSNS的通信性能.首先建立数学模型,分析数据帧被转发所经历的跳数与能效的变化规律,然后分析前向纠错...     

基于模糊分析的信息系统风险灰色评估模型

为了准确地评价信息系统风险,提出了一种融合模糊层次分析法和灰色评估方法的新模型.从“灰度”概念出发,建立信息系统风险灰色评估模型,消减评价者的主观性.在此基础上引入模糊层次分析法,该方法通过引入模糊数学的有关概念对权重的确定方法进行改进,确保了权重选择的客观性.对信息系统风险的实证进行了研究,实验结果表明了该方法的有效性,为进一步提高信息系统风险评估的准确性提供了可参考的模型和方法支持.