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Showing papers on "Network management published in 2021"


Journal ArticleDOI
TL;DR: This article investigates the multicast communication of a satellite and aerial-integrated network with rate-splitting multiple access with RSMA, where both satellite and unmanned aerial vehicle (UAV) components are controlled by network management center and operate in the same frequency band.
Abstract: To satisfy the explosive access demands of Internet-of-Things (IoT) devices, various kinds of multiple access techniques have received much attention. In this article, we investigate the multicast communication of a satellite and aerial-integrated network (SAIN) with rate-splitting multiple access (RSMA), where both satellite and unmanned aerial vehicle (UAV) components are controlled by network management center and operate in the same frequency band. Considering a content delivery scenario, the UAV subnetwork adopts the RSMA to support massive access of IoT devices (IoTDs) and achieve desired performances of interference suppression, spectral efficiency, and hardware complexity. We first formulate an optimization problem to maximize the sum rate of the considered system subject to the signal-interference-plus-noise-ratio requirements of IoTDs and per-antenna power constraints at the UAV and satellite. To solve this nonconvex optimization problem, we exploit the sequential convex approximation and the first-order Taylor expansion to convert the original optimization problem into a solvable one with the rank-one constraint, and then propose an iterative penalty function-based algorithm to solve it. Finally, simulation results verify that the proposed method can effectively suppress the mutual interference and improve the system sum rate compared to the benchmark schemes.

218 citations


Journal ArticleDOI
TL;DR: In this paper, a comprehensive review on applications of deep learning in network traffic monitoring and analysis (NTMA) applications is provided, where the authors discuss key challenges, open issues, and future research directions for using deep learning for NTMA applications.

96 citations


Journal ArticleDOI
TL;DR: The 6G enabled network in box (NIB) architecture is presented as a powerful integrated solution that can support comprehensive network management and operations of next-generation mobile networks by dynamically reconfiguring the deployment of network functions.
Abstract: The ongoing deployment of 5G network involves the Internet of Things (IoT) as a new technology for the development of mobile communication, where the Internet of Everything (IoE) as the expansion of IoT has catalyzed the explosion of data and can trigger new eras. However, the fundamental and key component of the IoE depends on the computational intelligence (CI), which may be utilized in the sixth generation mobile communication system (6G). The motivation of this article presents the 6G enabled network in box (NIB) architecture as a powerful integrated solution that can support comprehensive network management and operations. The 6G enabled NIB can be used as an alternative method to meet the needs of next-generation mobile networks by dynamically reconfiguring the deployment of network functions, providing a high degree of flexibility for connection services in various situations. Especially the CI technology such as evolutionary computing, neural computing and fuzzy systems utilized as a part of NIB have inherent capabilities to handle various uncertainties, which have unique advantages in processing the variability and diversity of large amounts of data. Finally, CI technology for NIB, which is widely used is also introduced such as distributed computing, fog computing, and mobile edge computing in order to achieve different levels of sustainable computing infrastructure. This article discusses the key technologies, advantages, industrial scenario applications of CI technology as NIB, typical use cases and development trends based on IoE, which provides directional guidance for the development of CI technology as NIB for 6G.

87 citations


Journal ArticleDOI
TL;DR: A network traffic prediction method based on SA (Simulated Annealing) optimized ARIMA, non-linear model BPNN and optimization algorithm SA can fully realize the potential of mining linear and non- linear laws of historical network traffic data, hence improving the prediction accuracy.

71 citations


Journal ArticleDOI
TL;DR: Simulation results examine the efficiency of the proposed D2D-assisted fog computing framework, and demonstrate the superiority of the suggested resource allocation algorithm over the counterparts.
Abstract: In this paper, joint resource management for device-to-device (D2D) communication assisted multi-tier fog computing is studied. In the considered system model, each subscribed mobile end user can choose to offload its computation task to either an edge server deployed at the base station via the cellular connection or one nearby third-party fog node via the direct D2D connection. After receiving offloading requests from all end users, the network operator determines the optimal management of the fog computing system, including both computation and communication resource allocations, according to its service agreements with end users, energy cost of edge-server processing and total expense in renting third-party fog nodes. With the objective of maximizing the network management profit, a joint multi-dimensional resource optimization problem, integrating link scheduling, channel assignment and power control, is formulated. An optimal solution algorithm is proposed based on the idea of branch-and-price for addressing this complicated mixed integer nonlinear programming problem. To facilitate the practical implementation in large-scale systems, a suboptimal greedy algorithm with significantly reduced computational complexity is also developed. Simulation results examine the efficiency of the proposed D2D-assisted fog computing framework, and demonstrate the superiority of the proposed resource allocation algorithm over the counterparts.

60 citations


Journal ArticleDOI
TL;DR: In this article, the authors provide a survey of the latest V2X use cases including requirements, and various 5G enabling technologies under consideration for vehicular communications, and provide an interesting mapping between the three 5G pillars and vehicle-to-everything use case groups.
Abstract: 5G technologies promise faster connections, lower latency, higher reliability, more capacity and wider coverage. We are looking to rely on these technologies to achieve Vehicle-to-Everything (V2X) communications, which increase the safety and autonomy of vehicles in addition to road safety, saving energy and costs. The integration of vehicular communication systems and 5G is the subject of many research. Nowadays, researchers address challenges such as automated and intelligent networks, cloud and edge data processing, network management, virtualization, security, privacy and finally interoperability. This paper provides a survey of the latest V2X use cases including requirements, and various 5G enabling technologies under consideration for vehicular communications. Subsequently, we first provide an interesting mapping between the three 5G pillars and V2X use case groups. Then, we present a summary of potential applications of enabling technologies for V2X use case groups. Finally, the open directions of research are discussed, and the challenges that await to be met are pointed out.

59 citations


Journal ArticleDOI
TL;DR: A new regularizer method, namely SD-Reg, which is based on the standard deviation of the weight matrix, has been used to address the problem of overfitting and to improve the capability of NIDSs in detection of unseen intrusion events.

59 citations


Journal ArticleDOI
TL;DR: This paper carefully reviews existing network traffic classification methods from a new and comprehensive perspective by classifying them into five categories based on representative classification features, i.e., statistics-based classification, correlation- based classification, behavior-based classified, payload-based classifier, and port-based Classification.

58 citations


Journal ArticleDOI
TL;DR: This paper reviews and systematizes the state-of-the-art solutions that address both DoS and DDoS attacks in SDNs through the lenses of intrinsic and extrinsic approaches, and surveys the different approaches and tools adopted to implement the revised solutions.

58 citations


Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a novel data dissemination scheme by exploring short-term traffic prediction for Industry 4.0 applications enabled in Internet of Vehicles, which leverages the ant colony optimization algorithm to find the optimal routing path with minimum delay.
Abstract: As a key use case of Industry 4.0 and the Smart City, the Internet of Vehicles (IoV) provides an efficient way for city managers to regulate the traffic flow, improve the commuting performance, reduce the transportation facility cost, alleviate the traffic jam, and so on. In fact, the significant development of Internet of Vehicles has boosted the emergence of a variety of Industry 4.0 applications, e.g., smart logistics, intelligent transforation, and autonomous driving. The prerequisite of deploying these applications is the design of efficient data dissemination schemes by which the interactive information could be effectively exchanged. However, in Internet of Vehicles, an efficient data scheme should adapt to the high node movement and frequent network changing. To achieve the objective, the ability to predict short-term traffic is crucial for making optimal policy in advance. In this article, we propose a novel data dissemination scheme by exploring short-term traffic prediction for Industry 4.0 applications enabled in Internet of Vehicles. First, we present a three-tier network architecture with the aim to simply network management and reduce communication overheads. To capture dynamic network changing, a deep learning network is employed by the controller in this architecture to predict short-term traffic with the availability of enormous traffic data. Based on the traffic prediction, each road segment can be assigned a weight through the built two-dimensional delay model, enabling the controller to make routing decisions in advance. With the global weight information, the controller leverages the ant colony optimization algorithm to find the optimal routing path with minimum delay. Extensive simulations are carried out to demonstrate the accuracy of the traffic prediction model and the superiority of the proposed data dissemination scheme for Industry 4.0 applications.

56 citations


Book
19 Aug 2021
TL;DR: The authors explain the problems with current mobile IP networks and the need for a new mobility-aware IP-based control architecture, before presenting the Ambient Networking concept itself and the business opportunities that it offers.
Abstract: Ambient Networks defines a new kind of network architecture, which embeds support for co operation and competition between diverse network types within a common control layer. This unified networking concept can adapt to the heterogeneous environments of different radio technologies and service and network environments. Special focus is placed on facilitating both competition and co-operation of various market players, by defining interfaces which allow the instant negotiation of cooperation agreements. The Ambient Networking concept has been developed in the framework of the Ambient Networks project, which is co-sponsored by the European Union under the Information Society Technology (IST) priority of the 6th Framework Programme. The Ambient Networks project mobilised the work of researchers from over forty different organisations, both major industrial corporations and leading academic institutions, from Europe and worldwide. This book offers a complete and detailed overview of the Ambient Networking concept and its core technologies. The authors explain the problems with current mobile IP networks and the need for a new mobility-aware IP-based control architecture, before presenting the Ambient Networking concept itself and the business opportunities that it offers. The architecture, components, features and challenges of Ambient Networking are covered in depth, with comprehensive discussions of multi-radio access, generic Ambient Network signalling, mobility support, context and network management and built-in media delivery overlay control. Ambient Networks: Co-operative Mobile Networking for the Wireless World Explains the need for Ambient Networking, discussing the limitations of todays proposed architectures, and explaining the business potential of edge networks and network co-operation. Describes Ambient Networking technology in detail, and addresses the technical challenges for implementation. Includes practical user scenarios which are fully analysed and assessed through simulation studies. Including a complete examination of the research and technologies arising from the Ambient Networks concept, Ambient Networks will be invaluable for research and development teams in networking and communications technology, as well as advanced students in electrical engineering and computer science faculties. Standardisation specialists, research departments, and telecommunications analysts will also find this a helpful resource.

Journal ArticleDOI
TL;DR: Feature Selection (FS) as discussed by the authors is a crucial pre-processing step in network management and specifically for the purposes of network intrusion detection, where trade-offs between performance and resource consumption are crucial.

Journal ArticleDOI
TL;DR: The proposed SDVN provides lightweight end-to-end security using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks.
Abstract: One of the most promising application areas of the industrial Internet of Things (IIoT) is vehicular ad hoc networks (VANETs). VANETs are largely used by intelligent transportation systems to provide smart and safe road transport. To reduce the network burden, software-defined networks (SDNs) act as a remote controller. Motivated by the need for greener IIoT solutions, this article proposes an energy-efficient end-to-end security solution for software-defined vehicular networks (SDVNs). Besides, SDN's flexible network management, network performance, and energy-efficient end-to-end security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: 1) in roadside unit (RSU)-based group authentication scheme, each vehicle in the RSU range receives a group ID–key pair for secure communication; and 2) in private collaborative intrusion detection system (p-CIDS), the SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated in NS2 and MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDVN.

Journal ArticleDOI
TL;DR: A tree structural recurrent neural network (Tree-RNN), which divides a large classification into small classifications by using the tree structure, and can achieve higher performance in less training time and average accuracy is higher than other state-of-the-art methods.
Abstract: Network traffic classification plays an important role in network monitoring and network management. With the continuous development of network technology, traditional methods of traffic classification have more limitations in accuracy to deal with encrypted traffic. Fortunately, deep neural network (DNN) is an effective method for handling traffic classification due to its ability to learn inherent data features. However, this method generally classifies network traffic with only the single classifier, which makes it relatively less effective in some classes for the problem of large classification. In this paper, we propose a tree structural recurrent neural network (Tree-RNN), which divides a large classification into small classifications by using the tree structure. A specific classifier is set for each small classification after division. With multiple classifiers employed, Tree-RNN can complement each other in classification performance, and the problem of the single classifier is solved. Since multiple classifiers are all end-to-end frameworks, Tree-RNN can automatically learn the nonlinear relationship between input data and output data without feature extraction. To verify the validity of our model, we compare Tree-RNN with state-of-the-art methods using the ISCX public traffic dataset. Experimental results show that Tree-RNN can achieve higher performance in less training time. The average accuracy of Tree-RNN is 4.88% higher than other state-of-the-art methods, and it has higher average precision and average recall.

Journal ArticleDOI
TL;DR: A comprehensive study of representative works on IoT network management, analyzes existing solutions for IoT low power networks management and presents a taxonomy of those solutions and compares existing research proposals based on different requirements.

Journal ArticleDOI
TL;DR: In this paper, the authors survey the state-of-the-art research in utilizing machine learning techniques in improving the performance of wireless networks and identify challenges and open issues to provide a roadmap for the researchers.
Abstract: A plethora of demanding services and use cases mandate a revolutionary shift in the management of future wireless network resources. Indeed, when tight quality of service demands of applications are combined with increased complexity of the network, legacy network management routines will become unfeasible in 6G. Artificial Intelligence (AI) is emerging as a fundamental enabler to orchestrate the network resources from bottom to top. AI-enabled radio access and AI-enabled core will open up new opportunities for automated configuration of 6G. On the other hand, there are many challenges in AI-enabled networks that need to be addressed. Long convergence time, memory complexity, and complex behaviour of machine learning algorithms under uncertainty as well as highly dynamic channel, traffic and mobility conditions of the network contribute to the challenges. In this paper, we survey the state-of-art research in utilizing machine learning techniques in improving the performance of wireless networks. In addition, we identify challenges and open issues to provide a roadmap for the researchers.

Journal ArticleDOI
TL;DR: In this paper, an intelligent video surveillance-based vehicle tracking system is presented, which uses a combination of the neural network, image-based tracking, and You Only Look Once (YOLOv3) to track vehicles.
Abstract: The growing population in large cities is creating traffic management issues. The metropolis road network management also requires constant monitoring, timely expansion, and modernization. In order to handle road traffic issues, an intelligent traffic management solution is required. Intelligent monitoring of traffic involves the detection and tracking of vehicles on roads and highways. There are various sensors for collecting motion information, such as transport video detectors, microwave radars, infrared sensors, ultrasonic sensors, passive acoustic sensors, and others. In this paper, we present an intelligent video surveillance-based vehicle tracking system. The proposed system uses a combination of the neural network, image-based tracking, and You Only Look Once (YOLOv3) to track vehicles. We train the proposed system with different datasets. Moreover, we use real video sequences of road traffic to test the performance of the proposed system. The evaluation outcomes showed that the proposed system can detect, track, and count the vehicles with acceptable results in changing scenarios.

Journal ArticleDOI
TL;DR: In the paper, existing load modelling techniques and NILM methodologies are critically examined to inform and guide research activity, equipment development and regulator thinking, as well as network operators.

Journal ArticleDOI
TL;DR: SURFER is a routing protocol for the Internet of Vehicles (IoV) that makes use of a distributed SDN architecture and a Blockchain system within the RSU network in order to route packets securely and efficiently.
Abstract: Software-defined networking (SDN) is becoming the most dominant method for network management. By decoupling the control plane from the data plane, SDN provides a centralized view and more flexibility, scalability, and global knowledge of the network. In a previous paper, we presented ROAMER, a routing protocol that exploits roadside units (RSUs) in order to route messages within vehicular ad hoc networks (VANETs). ROAMER combines two famous routing methods that have been extensively used in VANETs, which are geographical and carry-and-forward. In this article, we upgrade ROAMER by shifting the routing operations into the SDN. Hence, we present SURFER, a routing protocol for the Internet of Vehicles (IoV) that makes use of a distributed SDN architecture and a Blockchain system within the RSU network in order to route packets securely and efficiently. We present two implementation methods for SURFER: the first uses SDN within the RSU network only, while the second method deploys SDN within the entire IoV. We implemented extensive simulations and compared SURFER to some very recent routing protocols for IoV. The simulation results illustrated the high efficiency of SURFER as compared to other IoV routing protocols in terms of latency, packet delivery, and network overhead.

Journal ArticleDOI
12 May 2021-Symmetry
TL;DR: In this article, the authors present a survey of botnet detection techniques by using systematic reviews and meta-analyses (PRISMA) guidelines and propose directions for future research.
Abstract: In recent decades, the internet has grown and changed the world tremendously, and this, in turn, has brought about many cyberattacks. Cybersecurity represents one of the most serious threats to society, and it costs millions of dollars each year. The most significant question remains: Where do these attacks come from? The answer is that botnets provide platforms for cyberattacks. For many organizations, a botnet-assisted attack is a terrifying threat that can cause financial losses and leave global victims in its wake. It is therefore imperative to defend organizations against botnet-assisted attacks. Software defined networking (SDN) has emerged as one of the most promising paradigms for this because it allows exponential increases in the complexity of network management and configuration. SDN has a substantial advantage over traditional approaches with regard to network management because it separates the control plane from network equipment. However, security challenges continue to arise, which raises the need for different types of implementation strategies to spread attack vectors, despite the significant benefits. The main objective of this survey is to assess botnet detection techniques by using systematic reviews and meta-analyses (PRISMA) guidelines. We evaluated various articles published since 2006 in the field of botnet detection, based on machine learning, and from 2015 in the field of SDN. Specifically, we used top-rated journals that featured the highest impact factors. In this paper, we aim to elaborate on several research areas regarding botnet attacks, detection techniques, machine learning, and SDN. We also address current research challenges and propose directions for future research.

Journal ArticleDOI
Youhwan Seol1, Doyeon Hyeon1, Junhong Min1, Moonbeom Kim1, Jeongyeup Paek1 
TL;DR: In this paper, the authors present an up-to-date survey of the research efforts in TSN over the past few years, including the latest efforts in standardization by the IEEE.
Abstract: Time-sensitive networking (TSN) is a next generation local area network technology for the coexistence of information and operation technology, targeted to industrial automation, in-vehicle networks, and avionic networks in the industrial Internet of Things. This paper presents an up-to-date survey of the research efforts in TSN over the past few years, including the latest efforts in standardization by the IEEE. We review more than 170 TSN-related academic research papers published by major academic publishers and categorize these studies according to the topic, purpose, and methodology, analyzing research trends in TSN. The TSN core function deals with the functional and internal perspective to achieve ultra-low latency/jitter and ultra-reliability. Network management includes work with macroscopic and external views, such as configuration, management, and maintenance of TSN-enabled networks. In particular, we analyze the characteristics of existing studies that have evaluated the TSN network and propose future directions to advance the limits of TSN for real-world deployment. We aim this study to be a cornerstone for fellow and new researchers.

Journal ArticleDOI
TL;DR: A virtual network recognition and optimization method to improve quality-of-service (QoS) of cloud services and a scheduling strategy by combining SDN-based network management and instance placement to improve the service-level agreements (SLA) fulfillment.
Abstract: Cloud computing is a scalable and efficient technology for providing different services For better reconfigurability and other purposes, users build virtual networks in cloud environments Since some applications bring heavy pressure to cloud datacenter networks, it is necessary to recognize and optimize virtual networks with different applications In some cloud environments, cloud providers are not allowed to monitor user private information in cloud instances Therefore, in this paper, we present a virtual network recognition and optimization method to improve quality-of-service (QoS) of cloud services We first introduce a community detection method to recognize virtual networks from the cloud datacenter network Then, we design a scheduling strategy by combining SDN-based network management and instance placement to improve the service-level agreements (SLA) fulfillment Our experimental result shows that we can achieve a recognition accuracy as high as 80 percent to find out the virtual networks, and the scheduling strategy increases the number of SLA fulfilled virtual networks

Journal ArticleDOI
TL;DR: A novel Software Defined Networking (SDN)-controlled and Cognitive Radio (CR)-enabled V2X routing approach to achieve ultra-high data rate, by using predictive V2 X routing that supports the intelligent switching between two 5G technologies: millimeter-wave (mmWave) and terahertz (THz).

Journal ArticleDOI
08 Jan 2021
TL;DR: In this paper, a software-defined dynamic mmWave V2X network is proposed to support cooperative perception for safe automated driving, and necessary hardware and software platforms are introduced to compose prototype systems, which demonstrate the network functions, the prototype scalability and most importantly, the significant enhancement to the vehicle perception.
Abstract: The technical exploration of safe automated driving has evolved from single vehicle intelligence to vehicular networking. Therefore, to a certain extent, the safety requirements have been translated into the requirements for network performance. Although Vehicle-to-Everything (V2X) communication technologies have developed decades of years, from the Dedicated Short-Range Communication (DSRC) to the Long Term Evolution V2X (LTE-V2X), the introduction of millimeter wave (mmWave) in the 5G era will achieve an unprecedented leap in V2X performance by empowering multi-gigabit data transmission with ultra-low latency. Meanwhile, the emergence of Software Defined Networking (SDN) enables centralized network management and allows flexible scheduling of communication resources to match V2X capacities. Their benefits for vehicular networks have been revealed by plenty of studies respectively. Nevertheless, no researchers investigate their combination to improve the safety of automated driving, especially lack of scalable prototype systems for proof-of-concepts (PoCs), not to mention the field test with real traffic. In this paper, we firstly propose a novel architecture, i.e. software-defined dynamic mmWave V2X network, to support cooperative perception. And then, necessary hardware and software platforms are introduced to compose prototype systems. Step by step, we conduct PoCs from the indoors to the outdoors, which demonstrate the network functions, the prototype scalability and most importantly, the significant enhancement to the vehicle perception for safe automated driving.

Journal ArticleDOI
TL;DR: In this paper, the current status of 5G nonterrestrial network (NTN) standardization and state-of-the-art satellite networks, such as high-throughput satellites (HTS), LEO networks, and satellite-terrestrial heterogeneous networks, are discussed.
Abstract: As the 3rd Generation Partnership Project (3GPP) is discussing the integration of satellite networks into 5G New Radio (NR) and the mega-constellation low-Earth orbit (LEO) satellites are being deployed, communication satellites are gaining momentum for technical and economic success. Ultimate integration of satellites will be able to provide immense payoff for 5G networks, including global coverage extension and 3-D mobility enhancement. This article begins with an overview of the current status of 5G nonterrestrial network (NTN) standardization and state-of-the-art satellite networks, such as high-throughput satellites (HTS), LEO networks, and satellite-terrestrial heterogeneous networks. Technical impediments for realizing orthogonal frequency-division multiplexing (OFDM)-based satellite communications are presented, including channel nonlinearity, long propagation delay, and satellite mobility. We then suggest future engineering directions for network architecture, management/operation, and user terminal design. Options of network architectures are explained in terms of relay nodes between satellites and user terminals, satellite altitudes, and on-board processing. Hand-over, cell pattern generation, and resource allocation are discussed to improve network management and operation. Primary physical layer issues for user equipment terminal design, including antennas, channel models, timing advance, frequency offset, timing relationships, and hybrid automatic repeat requests (HARQs), are discussed.

ReportDOI
01 Jan 2021
TL;DR: This document provides a framework that describes and discusses an architecture for service and network management automation that takes advantage of YANG modeling technologies and aims to exemplify an approach that specifies the journey from technology-agnostic services to technology-specific actions.
Abstract: Data models for service and network management provides a programmatic approach for representing (virtual) services or networks and deriving (1) configuration information that will be communicated to network and service components that are used to build and deliver the service and (2) state information that will be monitored and tracked. Indeed, data models can be used during various phases of the service and network management life cycle, such as service instantiation, service provisioning, optimization, monitoring, and diagnostic. Also, data models are instrumental in the automation of network management. They also provide closed-loop control for the sake of adaptive and deterministic service creation, delivery, and maintenance. This document provides a framework that describes and discusses an architecture for service and network management automation that takes advantage of YANG modeling technologies. This framework is drawn from a network provider perspective irrespective of the origin of a data module; it can accommodate even modules that are developed outside the IETF. The document aims to exemplify an approach that specifies the journey from technology-agnostic services to technology-specific actions.

Proceedings ArticleDOI
25 Oct 2021
TL;DR: In this article, the authors discuss the challenges and provide results for a candidate NI-driven functionality that is properly integrated into the proposed architecture: network capacity forecasting, and propose an end-to-end NI-native architecture for 6G.
Abstract: The success of the upcoming 6G systems will largely depend on the quality of the Network Intelligence (NI) that will fully automate network management. Artificial Intelligence (AI) models are commonly regarded as the cornerstone for NI design, as they have proven extremely successful at solving hard problems that require inferring complex relationships from entangled, massive (network traffic) data. However, the common approach of plugging ‘vanilla’ AI models into controllers and orchestrators does not fulfil the potential of the technology. Instead, AI models should be tailored to the specific network level and respond to the specific needs of network functions, eventually coordinated by an end-to-end NI-native architecture for 6G. In this paper, we discuss these challenges and provide results for a candidate NI-driven functionality that is properly integrated into the proposed architecture: network capacity forecasting.

Journal ArticleDOI
TL;DR: This article argues that IoT network control should be jointly coordinated by the same SDN instance that also manages the wired segments and presents a new fully programmable solution that shifts the Whisper scope from the edge to the core, deploying and testing such architecture in real-world large-scale testbeds.
Abstract: Current industrial Internet of Things (IoT) demands are calling for more flexible and programmable networks that ensure high reliability in dynamic mission-critical scenarios. Centralized software-defined networking (SDN) offers high levels of flexibility and programmability that traditional distributed IoT protocols cannot offer. However, the use of SDN in IoT is currently not really lifting off due to wireless links unreliability, excessive control overhead, and devices’ limited resources. In order to reduce the impact of these issues, Whisper enables SDN-like capabilities in IoT by centrally controlling the distributed routing and scheduling planes in the IoT network (things overlay). To do so, the Whisper controller carefully sends computed messages compatible with the standardized distributed protocols already running in the network that change the default protocols’ behavior. However, as many other SDN-on-IoT approaches, Whisper is currently limited to the IoT network scope and remains as yet another independent network management silo. In this article, we argue that IoT network control should be jointly coordinated by the same SDN instance that also manages the wired segments. In order to do so, we present a new fully programmable solution that shifts the Whisper scope from the edge to the core, deploying and testing such architecture in real-world large-scale testbeds. We use 6TiSCH as industrial IoT enabler and the open network operating system platform to orchestrate all network segments. Finally, we report the technical challenges, discussing the lessons learned, and demonstrating the feasibility and suitability of this Whisper-based solution to provide an efficient and programmable end-to-end control over a heterogeneous network domain.

Journal ArticleDOI
TL;DR: A new software-defined networking-based IoV heterogeneous networking measurement framework is proposed to build software- defined networking of IoV and a performance measurement and analysis method to measure and characterize its performance is proposed.
Abstract: Internet of Vehicles (IoV), which plays a significantly important role in smart future cities, has become current hot research topics. However, the high heterogeneous nature of IoV has brought many new challenges such as low network performance and difficult network management for IoV. Software-defined networking enables the efficient solution of these problem. This article studies the measurement and analysis technology for software-defined networking of IoV. A new software-defined networking-based IoV heterogeneous networking measurement framework is proposed to build software-defined networking of IoV. We propose a performance measurement and analysis method to measure and characterize its performance. The performance indexes and measure methods about the delay, loss, throughput, delay jitter is in detail derived. The switch selection mechanism is proposed to establish optimal measurement points of advantage. The packet sampling process is presented to quickly obtain the needed measurement information from massive traffic flows. To validate our measurement method and fairly characterize its measurement performance for different controllers, we conduct massive simulation experiments to systematically analyze and compare current famous controllers. In such a case, we provide more comprehensive, systematic measurement analysis for application in software-defined networking of IoV. Experiments results show that our measurement approach is feasible and effective.

Journal ArticleDOI
TL;DR: In this article, the applicability of an active form of ML, called Active Learning (AL), which reduces the need for a high number of labeled examples by actively choosing the instances that should be labeled, is investigated.
Abstract: Network Traffic Classification (NTC) has become an important component in a wide variety of network management operations, e.g., Quality of Service (QoS) provisioning and security purposes. Machine Learning (ML) algorithms as a common approach for NTC methods can achieve reasonable accuracy and handle encrypted traffic. However, ML-based NTC techniques suffer from the shortage of labeled traffic data which is the case in many real-world applications. This study investigates the applicability of an active form of ML, called Active Learning (AL), which reduces the need for a high number of labeled examples by actively choosing the instances that should be labeled. The study first provides an overview of NTC and its fundamental challenges along with surveying the literature in the field of using ML techniques in NTC. Then, it introduces the concepts of AL, discusses it in the context of NTC, and review the literature in this field. Further, challenges and open issues in the use of AL for NTC are discussed. Additionally, as a technical survey, some experiments are conducted to show the broad applicability of AL in NTC. The simulation results show that AL can achieve high accuracy with a small amount of data.