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%。

Local singularity and S–A methods for analyzing ore-producing anomalies in the Jianbiannongchang area of Heilongjiang,China

The Heilongjiang Jianbiannongchang area is located at the confluence of the Great and Lesser Xing’an Ranges.This area has a complex magmatic and tectonic evolutionary history that has resulted in a complex and diverse geological background for mineralization.In this study,isometric logarithmic ratio(ILR)transformations of Au,Cu,Pb,Zn,and Sb contents were performed in the1:50,000 soil geochemical data of the Jianbiannongchang area.Robust principal component analysis(RPCA)was conducted based on ILR transformation.The local singularity and spectrum-area(S-A)methods were used to extract information on mineralogic anomalies.The results showed that:(1)the transformed data eliminated the influence of the original data closure effect,and the PC1and PC2 information obtained by applying RPCA reflected ore-producing element anomalies dominated by Au and Cu.(2)The local singularity method can enhance the information of the local strong and weak slow anomalies.After performing local singularity analysis on PC1 and PC2,the obtained local anomalies reflected the local singularity spatial anomaly patterns related to Cu and Au mineralization in this area,which is an effective method for trapping ore-producing anomalies.(3)Furthermore,the composite anomaly decomposition of PC1 and PC2 was performed using the S-A method,and the screened anomalous and background fields reflect the ore-producing anomalies related to Cu and Au mineralization.This information is in agreement with known Cu and Au mineralization.(4)The geochemical anomalies with mineralization potential were obtained outside the known mineralization sites by integrating the information of oreproducing anomalies extracted by the local singularity and S-A methods,providing the theoretical basis and exploration direction for future exploration in the study area.

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.

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.

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)

Strategies for Localized Transformation of Landscape in Old Industrial Areas: A Case Study of Xiadian Industrial Area in Xuzhou City

Based on the theory of “localization”, the landscape status of Xiadian industrial area in Xuzhou City was investigated and analyzed. Localized transformation of landscape in the old industrial area can be conducted from the restoration of landscape ecological environment, protection of industrial landscape heritage, and sustainable utilization of industrial waste resources. It can achieve a better balance between urban renewal and the landscape transformation of the old industrial area and then realize the reshaping and regeneration of landscape and promote the development of local industries and the continuation of industrial culture to provide useful thinking for creating geographically representative urban landscape.

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。

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。

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OPNET的分布式WLAN网络性能仿真研究

介绍了无线局域网的基本概念以及它的一些优缺点,分析了无线局域网中IEEE 802.11系列标准。基于网络仿真平台OPNET,构建了分布式无线局域网网络模型、节点模型和进程模型。为了优化网络性能,通过设置不同的网络参数(包括速率、信息包分组大小),对IEEE 802.11系列标准下的无线局域网网络性能进行了分析研究,减小了网络时延和丢包率,提高了系统吞吐量。对今后无线局域网部署中IEEE802.11系列标准的选择以及参数的设置有着重要的参考价值。

一类基于LAN/Control Net/Fieldbus分层结构的网络控制系统设计

大型的生产线网络结构一般分为3层:生产现场层,过程监控制层和管理层,文章以某企业控制系统改造为背景,基于MelsecNet/10控制网络,对网络控制系统设计中的控制方案,控制网络与企业管理层局域网的连接及系统运行模式等主要技术问题作了讨论.

An Adaptive Local Grid Nesting-based Genetic Algorithm for Multi-earth Observation Satellites' Area Target Observation

The Scheduling of the Multi-EOSs Area Target Observation(SMEATO)is an EOS resource schedul-ing problem highly coupled with computational geometry.The advances in EOS technology and the ex-pansion of wide-area remote sensing applications have increased the practical significance of SMEATO.In this paper,an adaptive local grid nesting-based genetic algorithm(ALGN-GA)is proposed for developing SMEATO solutions.First,a local grid nesting(LGN)strategy is designed to discretize the target area into parts,so as to avoid the explosive growth of calculations.A genetic algorithm(GA)framework is then used to share reserve information for the population during iterative evolution,which can generate high-quality solutions with low computational costs.On this basis,an adaptive technique is introduced to determine whether a local region requires nesting and whether the grid scale is sufficient.The effectiveness of the proposed model is assessed experimentally with nine randomly generated tests at different scales.The results show that the ALGN-GA offers advantages over several conventional algorithms in 88.9%of instances,especially in large-scale instances.These fully demonstrate the high efficiency and stability of the ALGN-GA.

基于netfilter/iptables和TC的LAN防火墙设计及流量控制

分析了Linux内核netfilter/iptables模块实现防火墙以及流量控制(TC)模块实现流量控制的原理和基本语法规则,在此基础上,针对小型局域网(LAN)提出了一套网络管理系统。该系统利用iptables实现防火墙及网络IP地址转换功能,并利用基于分层令牌桶队列规定的TC实现流量控制功能。实验结果表明,结合iptables和TC的网络管理系统能够有效保护网络安全,合理限制网络设备带宽,进而保障局域网服务质量。

Re-delineating mountainous areas with three topographic parameters in Mainland Southeast Asia using ASTER global digital elevation model data

Tropical mountainous areas not only provide substantial carbon storage and play an important role in global biological diversity, but also provide basic livelihood for a large number of poor ethnic minorities. However, there is no unified and explicit definition for mountainous areas. The local elevation range(LER) is a crucial structural parameter for delineating mountainous areas. However, current LER products are limited by the subjective selection of an optimum statistical window or coarser spatial resolution of topographical data. In this study, we presented an approach using thresholds for three topographic parameters, elevation, slope, and LER, derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model(ASTER GDEM) to redelineate the vast mountainous areas of mainland Southeast Asia(MSEA). The mean change-point analysis method was applied to determine the optimum statistical window of the 1 arc second(approximately 30 m)-resolution GDEM LER. The results showed that: First, the optimum statistical window is 38 × 38 cell units(width × height) in a rectangular neighborhood, or an area of about 1.30 km^2 for calculating GDEM LER in MSEA. Second, the LER of more than 80% of the area ranges from 30 m to 499 m in MSEA. The LERs in the northern and northwestern MSEA are greater than their counterparts in the south and east. Third, the area of the re-delineated mountainous areas was 83.52 × 10~4 km^2, about 38.71% of the total area. Spatially, the mountainous areas are mainly distributed in the north and northeast of MSEA. The re-delineated 30-m resolution map of the mountainous areas will serve as a topographical dataset for monitoring mountainrelated land surface changes in MSEA. The parameter-modified mountain extraction procedure can be expanded to delineate global mountainous areas.

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.

Localization and mapping in urban area based on 3D point cloud of autonomous vehicles

In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping （SLAM） algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points （ICP） algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles＇ importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles＇ poses, which would decrease particle numbers, increase algorithm speed and restrain particles＇ impoverishment. The calculation of particles＇ importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches.