基于自发数据传输的高效双LAN太赫兹无线局域网MAC协议

针对现有的双局域网(LAN)太赫兹无线局域网(Dual-LAN THz WLAN)相关介质访问控制(MAC)协议中存在的某些节点会在多个超帧内重复发送相同的信道时隙请求帧以申请时隙资源以及网络运行的一些时段存在空闲时隙等问题,提出一种基于自发数据传输的高效MAC协议——SDTE-MAC(high-Efficiency MAC protocol based on Spontaneous Data Transmission)。SDTE-MAC通过让各节点都维护一张或多张时间单元链表,使各节点与其余节点在网络运行时间上达到同步,从而获悉各节点应该在信道空闲时隙的什么位置开始发送数据帧,优化了传统的信道时隙分配和信道剩余时隙再分配的流程,提高了网络吞吐量和信道时隙利用率,降低了数据时延,能够进一步提升双LAN太赫兹无线局域网的性能。仿真结果表明,网络饱和时,相较于AHT-MAC(Adaptive High Throughout multi-pan MAC protocol)中的N-CTAP(Normal Channel Time Allocation Period)时段时隙资源分配新机制以及自适应缩短超帧时段机制,SDTE-MAC的MAC层吞吐量提升了9.2%,信道时隙利用率提升了10.9%,数据时延降低了22.2%。

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate users.Moreover,the security design of the wireless structure is vulnerable to versatile attacks.For example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false messages.Althoughmany security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world applications.This paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks effectively.The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack.The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in theWLAN.The proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related work.The experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

基于IRF-PF算法的WLAN室内定位方法

针对室内复杂环境下无线局域网接收信号强度的时变波动性,提出一种基于改进随机森林算法并融合粒子滤波的室内定位方法。首先,通过自适应滑动窗口选取接收信号强度较为平滑的信号接入点作为定位指纹特征建立决策树,从而减少特征空间的噪声;然后,利用bagging抽样后的袋外数据赋予决策树不同的投票权重等级,增大决策树类别间判别差异性,优化随机森林算法在目标位置与接收信号强度间弱映射关联性场景下定位精度低的问题,依据改进的随机森林算法建立位置坐标与接收信号强度间的映射模型,计算定位点的位置;最后,为了提高定位准确性与稳定性,将依据改进随机森林算法计算出的位置作为粒子滤波的观测量,利用粒子滤波对目标位置进行估计和预测。在Zenodo数据集上的实验结果表明:提出的算法在定位误差2 m范围内精度达到75.8%,在4 m范围内精度已经高达94.8%;相比于传统随机森林算法,能够将2 m内的定位精度提高11.5%;与K近邻、岭回归和贝叶斯等室内定位算法相比,定位精度和算法实时性也得到明显提升。

Localized Coverage Connectivity Based on Shape and Area Using Mobile Sensor Robots in Wireless Sensor Networks

A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.

基于VLAN的多信道卫星通信地面系统设计

随着卫星通信技术的日益发展,系统内部多信道并存的现象越来越普遍,信道间传输的业务容易引起相互干扰,单纯运用传统路由器很难完成系统中不同信道间的业务完整隔离;同时,多信道卫星通信系统网络拓扑结构复杂,易引起控制混乱等问题。针对这些问题,提出一种基于虚拟局域网(Virtual Local Area Network,VLAN)的多信道卫星通信地面系统的设计方法。该方法充分利用网络划分VLAN技术的隔离性和安全性特征,结合多信道通信技术使用多个信道同时传输业务的特征,提升特征维度,实现高效、可靠的多信道卫星通信,具有易于硬件实现的特点,提高网络通信的吞吐量。

Application of Wireless Local Area Network Technology in Mobile Robot for Finned Tube Inspection

This paper introduces a novel robot for outer surface inspection of boiler tubes. The paper describes the hardware system, wireless communication strategy, communication procedure and system software of the robot. The WLAN technology is used in the robot. It solves the problem of shielding generated by iron boiler and 11Mbps bandwidth made it possible for video and control stream real-time transmit within the same channel. Though TCP/IP protocol is robust, serial server is a transparent channel but cannot detect error and retransmit the data. In order to improve the reliability of serial communication, a new communication protocol is proposed. Key words boiler tubes - mobile robotics - wireless local area network Project Supported by the National High-Tech Program (Grant No. 2002AA420080)

5G LAN应用及安全探讨

第三代合作伙伴计划(3rd Generation Partnership Project,3GPP)在R16中启动了5G局域网(Local Area Network,LAN)项目研究,该技术让5G网络具备了提供广域局域网的能力,为5G网络与工业网络的融合提供了助力。首先介绍了5G LAN的标准演进、实现原理、关键技术,重点说明了5G LAN在家庭/园区网、企业网络、工业网络等的典型应用;其次分析了5G LAN中基础平台、网络接入、信息隔离、信息传输等方面的主要安全问题,并分别从基础设备计算平台、网络和系统安全防护、系统隔离防护3个方面提出了解决思路和方法,提升5G LAN的整体安全防护能力。

VLAN技术在校园网中的实际应用

VLAN技术可跨越物理边界,不仅缩小了广播域,还提高了局域网的安全性,有助于工作站实现增加、删除及修改等操作。文章选用三层交换机实现了VLAN间的路由,简化了网络管理和维护,保障了校园网络的良好运行。

基于local-area的Internet路由级拓扑抽象算法

通过分析Internet的本地聚集特性，给出了local-area和connect—area的定义，并基于此，为提高并行网络模拟性能，提出一种新型拓扑抽象算法——基于local—area的拓扑抽象（TABLA）算法。TABLA算法在给定的聚合粒度下，迭代搜索网络内的local-area，对拓扑进行抽象。模拟结果表明在Internet路由级拓扑上采用该算法，拓扑规模大约压缩为原先的45％，初始化内存节省约60％，模拟运行时间约缩短80％，大幅度提高了并行网络模拟性能。

基于VxLAN协议的新型SDN架构及应用

针对指挥信息系统云数据中心的传统网络架构存在的网络资源利用率不高、网络策略与业务无法自适应以及虚拟机迁移困难等问题,设计了一种基于虚拟扩展本地局域网(VxLAN)协议的新型软件定义网络(SDN)架构。分析了该架构的优势及信息处理流程,并以指挥信息系统双活云数据中心为例给出了该架构的应用效果。应用分析可见,该架构既可满足网络集中控制和网络服务质量适应差异化业务的需求,又可提升网络资源利用率、虚拟机跨域迁移能力以及指挥信息系统运行稳定性与可靠性。

Radio map updated method based on subscriber locations in indoor WLAN localization

With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.

A Semi-Supervised WLAN Indoor Localization Method Based on l1-Graph Algorithm

For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.

基于神经网络和遗传算法的高密集WLAN公平性保障算法

为了满足用户在各类场景下对无线业务日益增长的要求,高密集部署的无线局域网(wireless local area network,WLAN)是未来发展的趋势。但由于频率资源有限,相同信道必然存在多个WLAN无线接入点(access point,AP),然而处于同一信道的AP会互相干扰,造成网络中小区间吞吐量的公平性下降,无法为用户提供良好的服务质量。为了提高网络公平性,改善用户体验,需要制定合理的网络参数调优方法,给出了一种基于神经网络和遗传算法对WLAN参数优化的方法。采用神经网络构建网络参数与网络吞吐量公平性之间的映射,将训练完成的模型作为遗传算法的适应度评估函数,通过遗传算法求解优化参数组合配置来改善WLAN吞吐量公平性问题。仿真结果表明所提出算法能够使得高密集WLAN吞吐量公平性得到提升。

一种宽带WLAN贴片天线

文中提出了一种单馈的宽带贴片天线应用于无线局域网络通信。该天线由方形贴片、电抗性阻抗表面以及电磁带隙结构组成。文中提出同时加载电抗性阻抗表面和电磁带隙结构能够实现小型化以及宽带化。电抗性阻抗表面作为接地平面能够降低天线的谐振频率。电磁带隙结构能够提高天线的阻抗带宽。测试结果表明所提出的WLAN贴片天线的相对带宽为22.3%(S11=-10 dB),覆盖4.77~5.97 GHz,可以获得6.3~7.2 dBi增益。与现有宽带小型化天线相比,该天线在保证宽带小型化的前提下仍具有较高增益,且辐射性能具有较高的一致性,十分有利于其应用。该天线能够覆盖IEEE 802.11a标准所规定的5 GHz频段,能够实现无线局域网之间的高速数传。

802.11ax WLAN与LTE系统共存环境中的用户分配方案

随着使用智能设备移动用户的爆炸式增长,无线局域网逐渐呈密集部署态势。2021年发布的IEEE802.11ax标准与持续演进的系统长期共存。针对共存环境下用户如何连接网络实现用户吞吐量最大化的问题,提出了吞吐量感知的用户分配方案。该方案将两种网络的性能与用户需求相匹配,并基于实际距离分配用户连接哪个网络。数值分析表明,所提出的方案在吞吐量和能效方面优于已有方案。

多媒体中WLAN技术的跨层设计分析

随着移动通信技术的快速发展,无线局域网(Wireless Local Area Network,WLAN)技术成为人们日常生活中的重要组成部分。然而由于无线信道的不稳定性和带宽资源的有限性,如何提高多媒体传输的效率和质量成为一个重要的研究课题。跨层设计作为一种有效的解决方案,可以在不同的网络层之间进行信息交互,从而提升整个系统的性能。基于此,文章主要分析跨层设计在WLAN无线网络通信技术中的应用意义和策略,并展望未来的发展方向。

基于WLAN分析的网络安全技术

由于无线网络主要在开放的空间环境内展开数据信息传输,而不是固定在物理线缆上,存在一定的安全隐患。因此,必须要优化落实对无线网络的安全防护。文章强调了无线局域网(Wireless Local Area Network,WLAN)安全防护的必要性,并对恶意攻击、非法用户入侵、重传攻击等影响计算机无线网络安全的主要因素进行了分析。在此基础上,从无线与有线一体化关联分析技术、无线网络安全保障机制、WLAN安全技术这几方面入手,着重阐述了基于WLAN的网络安全防护技术的具体应用,以营造更为安全的WLAN无线网络运行环境。

农机具田间试验动态遥测系统设计

针对当前农机具田间试验参数检测技术薄弱、无法全面综合评估农机关键部件作业性能的情况,设计了农机具田间试验的遥测系统。在当前农机具三点悬挂连接方式利用上拉杆拉压力传感器、下悬挂销剪切力传感器、角传感器、动态扭矩传感器等实现机具作业过程中的多分量力有效检测。系统检测终端采用STM32F429单片机为主核心芯片,设计了多通道传感器数据融合同步采集处理电路、以太网处理电路及外部输出电路等,可实时对前端传感器数据进行采集、处理及存储,并采用RTK差分定位系统准确检测机组的作业速度及位置,通过大功率WiFi模块经局域网络将作业参数实时传输给田间远程监测平台。用户可通过上位机软件实现对作业参数进行计算存储、曲线绘制、时域分析、统计设置等。测试结果表明:系统结构简单,检测精度高,能够实现机具作业同步受力分析及技术参数测量,可作为田间试验的有效检测手段。

模态分解下的无线局域网室内全覆盖通信方法

室内无线局域网的覆盖率是影响用户网络通信效率的关键因素。若室内面积越大以及墙体过厚,且终端使用环境密封会导致通过折射等途径传输通信信号非常微弱,严重影响无线局域网的通信覆盖范围。为此,提出模态分解下的无线局域网室内全覆盖通信方法。通过划分局域网模态单元和确定室内信号振幅阈值,构建模态振幅阈值模型,消除无线局域网室内信号滤波噪声。从终端无线访问节点(AccessPoint,AP)的信号容量和天线传播损耗两方面分析影响无线局域网通信覆盖范围的原因。基于此,利用非朗伯发射器扩充无线局域网终端AP的信号容量,实现无线局域网的室内全覆盖通信。实验结果表明,研究方法应用下无线局域网的室内通信的盲点面积小,覆盖效率更高,实验场景室内通信信息传输率约为6.8Mbit·s^(-1),说明上述方法的可应用性更强。