Guest Editorial: Special issue on network/traffic optimisation towards 6G network

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
IET Networks Pub Date : 2024-02-28 DOI:10.1049/ntw2.12118
Michael Logothetis, João Paulo Barraca, Shigeo Shioda, Khaled Rabie
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Therefore, 6G brings even greater challenges to network/traffic engineering and optimisation.</p><p>This virtual collection on Network/Traffic Optimisation towards 6G Network brings together the best six research papers submitted from academia, and reflects some of the latest and original achievements, concentrating on the performance of a mobile hotspot in vehicular communication, on the mobility modelling and ad hoc routing in Flying Ad-hoc NETworks (FANETs), on the performance of a joint antenna and relay selection Multiple-Input Multiple-Output (MIMO) system for cooperative Non-Orthogonal Multiple Access (NOMA) networks, on optimal resource optimisation based on multi-layer monitoring and Machine Learning (ML), on Voice over Wi-Fi Security Threats—Address Resolution Protocol (ARP) attacks and countermeasures—and on the management of 5G and Beyond networks through cloud-native deployments and end-to-end monitoring.</p><p>Although the rapid and substantial changes in networking technologies towards the 6G Network over the recent years could readily justify this virtual issue, our real motivation was the 13th event of the International Symposium of Communications Systems, Networks and Digital Signal Processing, held in Porto, Portugal (20–22 July 2022), and the IET's open call.</p><p>We begin with the first paper where Marinos Vlasakis et al theoretically analyse the performance of a mobile hotspot with limited bandwidth capacity and a Connection Admission Control functionality which provides Quality of Service (QoS) support for handover voice calls by serving them in priority over new voice calls. An interesting application example of vehicular communication is presented by considering a vehicle (say a bus), which alternates between stop and moving phases. In the stop phase, the vehicle can service both new and handover calls, while in the moving phase, only new calls (originating from the vehicle) are supported. Obviously, when passengers enter the vehicle while talking on their mobile phone, a handover should occur, that is, the Access Point must support handover connections in priority over new call connections. To this end, the capacity of the mobile hotspot is probabilistically reserved during the stop phase to benefit handover calls. In this case, new calls are accepted with a probability. This is called probabilistic bandwidth reservation policy. The system is modelled based on three-dimensional Markov chains. Moreover, the traffic is assumed quasi-random (originating from a finite traffic source population). This consideration is the first for loss/queueing models applied in a mobile hotspot and is proven to be very essential.</p><p>In the second paper by G. Amponis et al, a novel approach is presented to model the movement of aerial nodes in ad hoc networks in general, but in particular it is presented in FANETs. Considering application-aware and mobility-aware routing for the representation of three-dimensional, anchored, and self-similar swarm mobility (drones) modelling, the so-called Anchored Self-Similar 3D Gauss-Markov Mobility Model (ASSGM-3D) is proposed to accurately capture the complex dynamics of aerial nodes (such as wind, turbulence, and changes in altitude) that significantly affect the communication performance in FANETs. The proposed model incorporates a set of spatio-temporal statistical metrics taking into account previously known metrics. Moreover, ASSGM-3D is designed using experimental data from experiments conducted with a set of routing protocols, namely Optimised Link State Routing and Ad-hoc On-demand Distance Vector Routing, as well as traditional mobility models, including the Gauss–Markov model and the Random Walk model. The proposed mobility model achieves an improved ad hoc routing performance in emergency communication scenarios for 6G applications, where a fast and reliable communication is crucial.</p><p>In the third paper, V. Balyan observes that (1) proper selection of relays can substantially improve both the QoS offered to users and the network coverage, especially when multiple antennas are used, as in the case of MIMO relay 5G-and-Beyond network, (2) the distinction of network users in two types, good channel quality, and poor channel quality, usually in the centre and edge of a cell respectively, fits well with the concept of Cooperative NOMA (a technology available in the 5G-and-Beyond network), whereby both the QoS offered to users and the network coverage are also substantially improved. In NOMA, multiple users can transmit in different power levels at the same time, code, and frequency. In Cooperative NOMA, users of good-channel quality decode the messages destined to poor-channel quality users, and therefore, the good-channel quality users are used as relays to improve the QoS support of the poor-channel quality users (a short-range communication system is needed in order for the messages to be sent from the good-channel quality users to poor-channel quality users). For this modern networking environment, the author proposes an Antenna Selection scheme, aiming at maximising the instantaneous rate of poor-channel condition users while providing better QoS. Then, a Relay Selection scheme follows that selects the least loaded relay. Thus, by this combined scheme, named ASRS, the best antenna of Base Station (BS), relay node, and antenna at relay are selected. The outage probability of the proposed scheme is simulatively evaluated with respect to the Signal-to-Noise Ratio, the number of antennas in the BS, the number of relays etc. Other performance metrics are also presented. The scheme is compared with other schemes found in the literature to show its superiority.</p><p>In the fourth paper, D. Uzunidis, P. Karkazis, and H. Leligou shed fresh light on optimal network resource optimisation by leveraging ML. First, in practice, it is much easier to minimise the distance between the overallocation of network resources and the optimal allocation, called the “critical point” (where the allocated resources ensure the SLA with zero underutilisation). Second, decisions on resource allocation per service become more complex than ever because fast decisions are required at a finer level while knowing the profile of each service is necessary and a difficult task, since new types of services emerge every day. Third, an unavoidable consideration is the critical issue of high degree of heterogeneity in a modern networking environment and the virtualisation technologies that are used; to cope with them, monitoring and managing the allocated resources are mandatory not only at the application layer but at all layers. Taking all three points into account, the authors propose a novel architecture/mechanism to minimise the allocated resources per service while ensuring QoS. Data are monitored and collected from heterogeneous resources and used to train ML models, while being tailored to each service in real time. A holistic per-service resource optimisation is performed through ML, emphasising that the data that feed the ML models are collected from all layers. For validation and evaluation of the proposed mechanism, it is applied to real-life services, namely Hadoop (handling Big Data) and a Backend service. Service profiling and performance predictions are performed by collecting and analysing a list of monitoring data coming from the physical layer, CPU, memory usage, network throughput etc., as well as other performance metrics from the running services. The results show very good accuracy in predicting the required resources for many operational configurations.</p><p>In the fifth paper, Lu Kuan-Chu et al propose a method to protect the Voice over Wi-Fi (VoWi-Fi) service from cyber-attacks in Beyond 5G or 6G Network. The motivation was the fact that Taiwan's major telecom operators have introduced VoWi-Fi calling services, which provide cellular calls and text messages to mobile users through home/public Wi-Fi networks based on 3GPP IP Multimedia Subsystem technology, instead of cellular base stations. These services are potential threats if they pass through untrusted Wi-Fi networks. To defend against possible attacks, an attack defence algorithm is proposed for future app developers or device manufacturers that can detect whether the user's calling environment is safe or not. In addition, referring to 3GPP standards, the authors recommend that telecom companies boost observation mechanisms to detect abnormalities and provide new design knowledge towards the development of the network to the 6G network. Moreover, to examine the VoWi-Fi attacks, specifically ARP attacks, the authors deployed real-world experiments to confirm their feasibility, assess their potential damage, and evaluate the proposed anti-attack algorithm.</p><p>In the latest paper, S. Barrachina-Munoz et al examine three critical aspects of 5G-and-Beyond network management: cloud-native deployments, end-to-end monitoring, and network intelligence. After a thorough review of the current literature, the authors present how the proposed fully functional experimental framework (testbed) is constructed and complements existing research. The proposed framework uses containerised network operations on a Kubernetes cluster in a multi-domain network spanning clouds and hosts, as well as containerised end-to-end monitoring. For the latter, both infrastructure resources and radio metrics are presented using two scenarios, which involve User Plane Function reselection and user mobility; in a third scenario, it is shown how a decision engine interacts with the testbed to perform zero-touch containerised application relocation, highlighting the potential for enabling dynamic and intelligent management. In conclusion, the proposed testbed employs cutting-edge open-source networking technologies widely used in the industry, making it a highly suitable platform for realistic 5G-and-Beyond experiments. The presented use cases not only validate the capabilities of the testbed but also reflect real-life scenarios. However, in order to ensure a simple, safe, and controlled testing environment, traffic is originated from emulated User Equipments.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.12118","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Networks","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ntw2.12118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

An even faster and more heterogeneous communication infrastructure is planned for the 6G network, based on 5G in a way that leads us to much more deeply connected, programmable, intelligent, and sensing devices, with excellent network performance and coverage, and new dimensions of functionality. Therefore, 6G brings even greater challenges to network/traffic engineering and optimisation.

This virtual collection on Network/Traffic Optimisation towards 6G Network brings together the best six research papers submitted from academia, and reflects some of the latest and original achievements, concentrating on the performance of a mobile hotspot in vehicular communication, on the mobility modelling and ad hoc routing in Flying Ad-hoc NETworks (FANETs), on the performance of a joint antenna and relay selection Multiple-Input Multiple-Output (MIMO) system for cooperative Non-Orthogonal Multiple Access (NOMA) networks, on optimal resource optimisation based on multi-layer monitoring and Machine Learning (ML), on Voice over Wi-Fi Security Threats—Address Resolution Protocol (ARP) attacks and countermeasures—and on the management of 5G and Beyond networks through cloud-native deployments and end-to-end monitoring.

Although the rapid and substantial changes in networking technologies towards the 6G Network over the recent years could readily justify this virtual issue, our real motivation was the 13th event of the International Symposium of Communications Systems, Networks and Digital Signal Processing, held in Porto, Portugal (20–22 July 2022), and the IET's open call.

We begin with the first paper where Marinos Vlasakis et al theoretically analyse the performance of a mobile hotspot with limited bandwidth capacity and a Connection Admission Control functionality which provides Quality of Service (QoS) support for handover voice calls by serving them in priority over new voice calls. An interesting application example of vehicular communication is presented by considering a vehicle (say a bus), which alternates between stop and moving phases. In the stop phase, the vehicle can service both new and handover calls, while in the moving phase, only new calls (originating from the vehicle) are supported. Obviously, when passengers enter the vehicle while talking on their mobile phone, a handover should occur, that is, the Access Point must support handover connections in priority over new call connections. To this end, the capacity of the mobile hotspot is probabilistically reserved during the stop phase to benefit handover calls. In this case, new calls are accepted with a probability. This is called probabilistic bandwidth reservation policy. The system is modelled based on three-dimensional Markov chains. Moreover, the traffic is assumed quasi-random (originating from a finite traffic source population). This consideration is the first for loss/queueing models applied in a mobile hotspot and is proven to be very essential.

In the second paper by G. Amponis et al, a novel approach is presented to model the movement of aerial nodes in ad hoc networks in general, but in particular it is presented in FANETs. Considering application-aware and mobility-aware routing for the representation of three-dimensional, anchored, and self-similar swarm mobility (drones) modelling, the so-called Anchored Self-Similar 3D Gauss-Markov Mobility Model (ASSGM-3D) is proposed to accurately capture the complex dynamics of aerial nodes (such as wind, turbulence, and changes in altitude) that significantly affect the communication performance in FANETs. The proposed model incorporates a set of spatio-temporal statistical metrics taking into account previously known metrics. Moreover, ASSGM-3D is designed using experimental data from experiments conducted with a set of routing protocols, namely Optimised Link State Routing and Ad-hoc On-demand Distance Vector Routing, as well as traditional mobility models, including the Gauss–Markov model and the Random Walk model. The proposed mobility model achieves an improved ad hoc routing performance in emergency communication scenarios for 6G applications, where a fast and reliable communication is crucial.

In the third paper, V. Balyan observes that (1) proper selection of relays can substantially improve both the QoS offered to users and the network coverage, especially when multiple antennas are used, as in the case of MIMO relay 5G-and-Beyond network, (2) the distinction of network users in two types, good channel quality, and poor channel quality, usually in the centre and edge of a cell respectively, fits well with the concept of Cooperative NOMA (a technology available in the 5G-and-Beyond network), whereby both the QoS offered to users and the network coverage are also substantially improved. In NOMA, multiple users can transmit in different power levels at the same time, code, and frequency. In Cooperative NOMA, users of good-channel quality decode the messages destined to poor-channel quality users, and therefore, the good-channel quality users are used as relays to improve the QoS support of the poor-channel quality users (a short-range communication system is needed in order for the messages to be sent from the good-channel quality users to poor-channel quality users). For this modern networking environment, the author proposes an Antenna Selection scheme, aiming at maximising the instantaneous rate of poor-channel condition users while providing better QoS. Then, a Relay Selection scheme follows that selects the least loaded relay. Thus, by this combined scheme, named ASRS, the best antenna of Base Station (BS), relay node, and antenna at relay are selected. The outage probability of the proposed scheme is simulatively evaluated with respect to the Signal-to-Noise Ratio, the number of antennas in the BS, the number of relays etc. Other performance metrics are also presented. The scheme is compared with other schemes found in the literature to show its superiority.

In the fourth paper, D. Uzunidis, P. Karkazis, and H. Leligou shed fresh light on optimal network resource optimisation by leveraging ML. First, in practice, it is much easier to minimise the distance between the overallocation of network resources and the optimal allocation, called the “critical point” (where the allocated resources ensure the SLA with zero underutilisation). Second, decisions on resource allocation per service become more complex than ever because fast decisions are required at a finer level while knowing the profile of each service is necessary and a difficult task, since new types of services emerge every day. Third, an unavoidable consideration is the critical issue of high degree of heterogeneity in a modern networking environment and the virtualisation technologies that are used; to cope with them, monitoring and managing the allocated resources are mandatory not only at the application layer but at all layers. Taking all three points into account, the authors propose a novel architecture/mechanism to minimise the allocated resources per service while ensuring QoS. Data are monitored and collected from heterogeneous resources and used to train ML models, while being tailored to each service in real time. A holistic per-service resource optimisation is performed through ML, emphasising that the data that feed the ML models are collected from all layers. For validation and evaluation of the proposed mechanism, it is applied to real-life services, namely Hadoop (handling Big Data) and a Backend service. Service profiling and performance predictions are performed by collecting and analysing a list of monitoring data coming from the physical layer, CPU, memory usage, network throughput etc., as well as other performance metrics from the running services. The results show very good accuracy in predicting the required resources for many operational configurations.

In the fifth paper, Lu Kuan-Chu et al propose a method to protect the Voice over Wi-Fi (VoWi-Fi) service from cyber-attacks in Beyond 5G or 6G Network. The motivation was the fact that Taiwan's major telecom operators have introduced VoWi-Fi calling services, which provide cellular calls and text messages to mobile users through home/public Wi-Fi networks based on 3GPP IP Multimedia Subsystem technology, instead of cellular base stations. These services are potential threats if they pass through untrusted Wi-Fi networks. To defend against possible attacks, an attack defence algorithm is proposed for future app developers or device manufacturers that can detect whether the user's calling environment is safe or not. In addition, referring to 3GPP standards, the authors recommend that telecom companies boost observation mechanisms to detect abnormalities and provide new design knowledge towards the development of the network to the 6G network. Moreover, to examine the VoWi-Fi attacks, specifically ARP attacks, the authors deployed real-world experiments to confirm their feasibility, assess their potential damage, and evaluate the proposed anti-attack algorithm.

In the latest paper, S. Barrachina-Munoz et al examine three critical aspects of 5G-and-Beyond network management: cloud-native deployments, end-to-end monitoring, and network intelligence. After a thorough review of the current literature, the authors present how the proposed fully functional experimental framework (testbed) is constructed and complements existing research. The proposed framework uses containerised network operations on a Kubernetes cluster in a multi-domain network spanning clouds and hosts, as well as containerised end-to-end monitoring. For the latter, both infrastructure resources and radio metrics are presented using two scenarios, which involve User Plane Function reselection and user mobility; in a third scenario, it is shown how a decision engine interacts with the testbed to perform zero-touch containerised application relocation, highlighting the potential for enabling dynamic and intelligent management. In conclusion, the proposed testbed employs cutting-edge open-source networking technologies widely used in the industry, making it a highly suitable platform for realistic 5G-and-Beyond experiments. The presented use cases not only validate the capabilities of the testbed but also reflect real-life scenarios. However, in order to ensure a simple, safe, and controlled testing environment, traffic is originated from emulated User Equipments.

特邀编辑:面向 6G 网络的网络/流量优化特刊
在 5G 的基础上,我们计划为 6G 网络提供更快、更异构的通信基础设施,从而实现更深入的互联、可编程、智能和传感设备,并提供卓越的网络性能和覆盖范围以及新的功能维度。因此,6G 给网络/流量工程和优化带来了更大的挑战。这本关于面向 6G 网络的网络/流量优化的虚拟文集汇集了学术界提交的六篇最佳研究论文,反映了一些最新的原创性成果,主要集中在车载通信中移动热点的性能、移动性建模和飞行 ad-hoc 网络(FANETs)中的 ad hoc 路由、合作非正交多址(NOMA)网络的联合天线和中继选择多输入多输出(MIMO)系统的性能,基于多层监控和机器学习(ML)的最优资源优化,Wi-Fi语音安全威胁--地址解析协议(ARP)攻击和应对措施,以及通过云原生部署和端到端监控管理5G及其他网络。尽管近年来网络技术在向 6G 网络发展的过程中发生了快速而巨大的变化,这很容易为这一虚拟议题提供依据,但我们真正的动机是在葡萄牙波尔图举行的第 13 届通信系统、网络和数字信号处理国际研讨会(2022 年 7 月 20-22 日),以及 IET 的公开征集。我们从第一篇论文开始,Marinos Vlasakis 等人从理论上分析了带宽容量有限的移动热点的性能,以及连接接入控制(Connection Admission Control)功能,该功能通过优先于新语音呼叫为切换语音呼叫提供服务质量(QoS)支持。考虑到车辆(如公共汽车)在停止和行驶阶段之间交替,介绍了一个有趣的车载通信应用实例。在停止阶段,车辆可以为新呼叫和移交呼叫提供服务,而在移动阶段,只支持新呼叫(来自车辆)。显然,当乘客在使用手机通话时进入车内,就应该进行切换,也就是说,接入点必须优先支持切换连接,而不是新呼叫连接。为此,在停车阶段,移动热点的容量有可能被预留,以利于切换呼叫。在这种情况下,新呼叫会以一定概率被接受。这就是所谓的概率带宽预留策略。该系统基于三维马尔可夫链建模。此外,流量被假定为准随机的(源自有限的流量源群体)。在 G. Amponis 等人撰写的第二篇论文中,提出了一种新颖的方法来模拟特设网络中的空中节点移动,特别是在 FANET 中。考虑到表示三维、锚定和自相似蜂群移动(无人机)建模的应用感知和移动感知路由,提出了所谓的锚定自相似三维高斯-马尔科夫移动模型(ASSGM-3D),以准确捕捉严重影响 FANET 通信性能的空中节点的复杂动态(如风、湍流和高度变化)。所提出的模型结合了一组时空统计指标,并考虑了以前已知的指标。此外,ASSGM-3D 的设计还使用了一组路由协议的实验数据,即优化链路状态路由(Optimised Link State Routing)和按需分布矢量路由(Ad-hoc On-demand Distance Vector Routing),以及传统的移动模型,包括高斯-马尔科夫模型(Gauss-Markov Model)和随机漫步模型(Random Walk Model)。在第三篇论文中,V. Balyan 观察到:(1) 在 6G 应用的紧急通信场景中,快速可靠的通信是至关重要的。Balyan 在第三篇论文中指出:(1) 合理选择中继站可大幅提高为用户提供的服务质量和网络覆盖范围,尤其是在使用多天线的情况下,如在 MIMO 中继 5G 及以上网络中;(2) 网络用户分为两类,即信道质量好和信道质量差,通常分别位于小区的中心和边缘,这与合作 NOMA(5G 及以上网络中的一种技术)的概念非常吻合,从而大幅提高了为用户提供的服务质量和网络覆盖范围。在 NOMA 技术中,多个用户可以在同一时间、同一编码和同一频率以不同的功率水平进行传输。 在合作 NOMA 中,信道质量好的用户会解码发送给信道质量差的用户的信息,因此,信道质量好的用户被用作中继站,以提高信道质量差的用户的 QoS 支持(需要短程通信系统才能将信息从信道质量好的用户发送给信道质量差的用户)。针对这种现代网络环境,作者提出了一种天线选择方案,旨在最大限度地提高信道质量差的用户的瞬时速率,同时提供更好的服务质量。然后,中继选择方案选择负载最小的中继。因此,通过这个名为 ASRS 的组合方案,基站 (BS)、中继节点和中继天线的最佳天线都被选中。模拟评估了所提方案的中断概率与信噪比、基站天线数量、中继站数量等的关系。此外还介绍了其他性能指标。在第四篇论文中,D. Uzunidis、P. Karkazis 和 H. Leligou 对利用 ML 优化网络资源进行了新的阐述。首先,在实践中,最小化网络资源总体分配与最优分配之间的距离(称为 "临界点",在临界点上,分配的资源能确保 SLA,且利用率为零)要容易得多。其次,每项服务的资源分配决策变得比以往任何时候都更加复杂,因为需要在更细的层面上做出快速决策,而了解每项服务的概况是必要的,也是一项艰巨的任务,因为每天都会出现新的服务类型。第三,一个不可避免的考虑因素是现代网络环境和所使用的虚拟化技术中的高度异构性这一关键问题;为了应对这些问题,不仅在应用层,而且在所有层上都必须对分配的资源进行监控和管理。考虑到以上三点,作者提出了一种新颖的架构/机制,在确保服务质量的同时,最大限度地减少每项服务所分配的资源。从异构资源中监控和收集数据,用于训练 ML 模型,同时实时为每项服务量身定制。通过 ML 对每项服务的资源进行整体优化,强调从各层收集数据,为 ML 模型提供养分。为了验证和评估所提出的机制,我们将其应用于现实生活中的服务,即 Hadoop(处理大数据)和后端服务。通过收集和分析来自物理层、CPU、内存使用率、网络吞吐量等的监控数据列表,以及来自运行服务的其他性能指标,进行服务剖析和性能预测。在第五篇论文中,Lu Kuan-Chu 等人提出了一种在超越 5G 或 6G 网络中保护 Wi-Fi 语音(VoWi-Fi)服务免受网络攻击的方法。其动机是台湾的主要电信运营商已经推出了 VoWi-Fi 通话服务,通过基于 3GPP IP 多媒体子系统技术的家庭/公共 Wi-Fi 网络,而不是蜂窝基站,向移动用户提供蜂窝电话和文本信息。如果这些服务通过不受信任的 Wi-Fi 网络,就会构成潜在威胁。为了抵御可能的攻击,我们为未来的应用程序开发商或设备制造商提出了一种攻击防御算法,可以检测用户的通话环境是否安全。此外,作者还参考了 3GPP 标准,建议电信公司加强观测机制,以检测异常情况,并为网络向 6G 网络的发展提供新的设计知识。在最新的论文中,S. Barrachina-Munoz 等人研究了 5G 及以后网络管理的三个关键方面:云原生部署、端到端监控和网络智能。在对当前文献进行全面回顾后,作者介绍了如何构建拟议的全功能实验框架(测试平台)并对现有研究进行补充。建议的框架在跨越云和主机的多域网络中的 Kubernetes 集群上使用容器化网络操作,以及容器化端到端监控。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IET Networks
IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
5.00
自引率
0.00%
发文量
41
审稿时长
33 weeks
期刊介绍: IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.
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