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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 11473

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Special Issue Editor

Dr. Ramon Aparicio-Pardo

E-Mail Website
Guest Editor

CNRS I3S laboratory, Université Côte d'Azur, Nice, France
Interests: optimal network management; artificial intelligence; SDN and NFV; optical, multimedia and wireless networks

Special Issue Information

Dear Colleagues,

Next-generation optical transport networks will face more and more demanding requirements, not only from the point of view of higher traffic volumes but mainly from the perspective of the very nature of these traffic volumes. The evolution of internet usage (e.g., emergence of cloud services, growth of mobile communications, rise of HDTV over the internet) is yielding to a complexified traffic with an increasing access diversity and augmented traffic dynamicity.

As a consequence, higher levels of network automation will be necessary to cope with these challenges. The scope of this Special Issue covers any kind of work helping to promote this shift from reactive autonomous to actual adaptive networks which monitor networks, extract knowledge from the monitoring data, and change hardware infrastructure and software control in accordance with this knowledge.

Dr. Ramon Aparicio-Pardo
Guest Editor

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Keywords

Network automation
Autonomous networks
Network orchestration, management, and control
Network monitoring and telemetry
Artificial intelligence and machine learning applied to networks
Software-defined networking
Network function virtualization

Published Papers (5 papers)

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Research

12 pages, 2161 KiB

Open AccessArticle

Distributed Authentication Model for Secure Network Connectivity in Network Separation Technology

by Na-Eun Park, So-Hyun Park, Ye-Sol Oh, Jung-Hyun Moon and Il-Gu Lee

Sensors 2022, 22(2), 579; https://doi.org/10.3390/s22020579 - 12 Jan 2022

Cited by 1 | Viewed by 1402

Abstract

Considering the increasing scale and severity of damage from recent cybersecurity incidents, the need for fundamental solutions to external security threats has increased. Hence, network separation technology has been designed to stop the leakage of information by separating business computing networks from the Internet. However, security accidents have been continuously occurring, owing to the degradation of data transmission latency performance between the networks, decreasing the convenience and usability of the work environment. In a conventional centralized network connection concept, a problem occurs because if either usability or security is strengthened, the other is weakened. In this study, we proposed a distributed authentication mechanism for secure network connectivity (DAM4SNC) technology in a distributed network environment that requires security and latency performance simultaneously to overcome the trade-off limitations of existing technology. By communicating with separated networks based on the authentication between distributed nodes, the inefficiency of conventional centralized network connection solutions is overcome. Moreover, the security is enhanced through periodic authentication of the distributed nodes and differentiation of the certification levels. As a result of the experiment, the relative efficiency of the proposed scheme (REP) was about 420% or more in all cases. Full article

(This article belongs to the Special Issue Optical Network Automation)

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24 pages, 15651 KiB

Open AccessArticle

Packet Flow Capacity Autonomous Operation Based on Reinforcement Learning

by Sima Barzegar, Marc Ruiz and Luis Velasco

Sensors 2021, 21(24), 8306; https://doi.org/10.3390/s21248306 - 12 Dec 2021

Cited by 4 | Viewed by 1982

Abstract

As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators is the dynamic capacity management of large packet flows, especially in the context of packet over optical networks. Machine Learning, particularly Reinforcement Learning, seems to be an enabler for autonomicity as a result of its inherent capacity to learn from experience. However, precisely because of that, RL methods might not be able to provide the required performance (e.g., delay, packet loss, and capacity overprovisioning) when managing the capacity of packet flows, until they learn the optimal policy. In view of that, we propose a management lifecycle with three phases: (i) a self-tuned threshold-based approach operating just after the packet flow is set up and until enough data on the traffic characteristics are available; (ii) an RL operation based on models pre-trained with a generic traffic profile; and (iii) an RL operation with models trained for real traffic. Exhaustive simulation results confirm the poor performance of RL algorithms until the optimal policy is learnt and when traffic characteristics change over time, which prevents deploying such methods in operators’ networks. In contrast, the proposed lifecycle outperforms benchmarking approaches, achieving noticeable performance from the beginning of operation while showing robustness against traffic changes. Full article

(This article belongs to the Special Issue Optical Network Automation)

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21 pages, 1954 KiB

Open AccessArticle

Cognitive Control-Loop for Elastic Optical Networks with Space-Division Multiplexing

by Silvana Trindade, Ricardo da S. Torres, Zuqing Zhu and Nelson L. S. da Fonseca

Sensors 2021, 21(23), 7821; https://doi.org/10.3390/s21237821 - 24 Nov 2021

Viewed by 1700

Abstract

This paper introduces a new solution to improve network performance by decreasing spectrum fragmentation, crosstalk interference, blocking of virtual networks, cost, and link load imbalance. These problems degrade the performance of Elastic Optical Networks with Space-Division Multiplexing. The proposed solution, called Cognitive control loop (CO-OP), is capable of identifying a set of problems and creating plans to mitigate these problems. The CO-OP comprises four functions that employ learning algorithms to identify problems and plan a series of actions to reduce or eliminate them. The results show that the CO-OP can effectively decrease up to 30% the blocking of requests and up to 50% the crosstalk occurrence compared to existing algorithms. Full article

(This article belongs to the Special Issue Optical Network Automation)

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17 pages, 441 KiB

Open AccessArticle

MIND: A Multi-Source Data Fusion Scheme for Intrusion Detection in Networks

by Naveed Anjum, Zohaib Latif, Choonhwa Lee, Ijaz Ali Shoukat and Umer Iqbal

Sensors 2021, 21(14), 4941; https://doi.org/10.3390/s21144941 - 20 Jul 2021

Cited by 10 | Viewed by 2865

Abstract

In recent years, there is an exponential explosion of data generation, collection, and processing in computer networks. With this expansion of data, network attacks have also become a congenital problem in complex networks. The resource utilization, complexity, and false alarm rates are major challenges in current Network Intrusion Detection Systems (NIDS). The data fusion technique is an emerging technology that merges data from multiple sources to form more certain, precise, informative, and accurate data. Moreover, most of the earlier intrusion detection models suffer from overfitting problems and lack optimal detection of intrusions. In this paper, we propose a multi-source data fusion scheme for intrusion detection in networks (MIND) , where data fusion is performed by the horizontal emergence of two datasets. For this purpose, the Hadoop MapReduce tool such as, Hive is used. In addition, a machine learning ensemble classifier is used for the fused dataset with fewer parameters. Finally, the proposed model is evaluated with a 10-fold-cross validation technique. The experiments show that the average accuracy, detection rate, false positive rate, true positive rate, and F-measure are 99.80%, 99.80%, 0.29%, 99.85%, and 99.82% respectively. Moreover, the results indicate that the proposed model is significantly effective in intrusion detection compared to other state-of-the-art methods. Full article

(This article belongs to the Special Issue Optical Network Automation)

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12 pages, 3789 KiB

Open AccessArticle

Joint Fiber Nonlinear Noise Estimation, OSNR Estimation and Modulation Format Identification Based on Asynchronous Complex Histograms and Deep Learning for Digital Coherent Receivers

by Shuailong Yang, Liu Yang, Fengguang Luo, Bin Li, Xiaobo Wang, Yuting Du and Deming Liu

Sensors 2021, 21(2), 380; https://doi.org/10.3390/s21020380 - 07 Jan 2021

Cited by 6 | Viewed by 2185

Abstract

In this paper, asynchronous complex histogram (ACH)-based multi-task artificial neural networks (MT-ANNs), are proposed to realize modulation format identification (MFI), optical signal-to-noise ratio (OSNR) estimation and fiber nonlinear (NL) noise power estimation simultaneously for coherent optical communication. Optical performance monitoring (OPM) is demonstrated with polarization mode multiplexing (PDM), 16 quadrature amplitude modulation (QAM), PDM-32QAM, as well as PDM-star 16QAM (S-16QAM) for the first time. The range of launched power is −3 to −2 dBm with a fiber link of 160–1600 km. Then, the accuracy of MFI reaches 100%. The average root mean square error (RMSE) of OSNR estimation can reach 0.37 dB. The average RMSE of NL noise power estimation can reach 0.25 dB. The results show that the monitoring scheme is robust to the increase of fiber length, and the solution can monitor more optical network parameters with better performance and fewer training data, simultaneously. The proposed ACH MT-ANN has certain reference significance for the future long-haul coherent OPM system. Full article

(This article belongs to the Special Issue Optical Network Automation)

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