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19th IEEE Annual Consumer communications and Networking Conference, CCNC 2022

作者： Jalodia, Nikita Taneja, Mohit Davy, Alan Dezfouli, Behnam Waterford Institute of Technology Walton Institute for Information and Communication Systems Science Department of Computing and Mathematics Waterford Ireland Sfi Connect Research Centre for Future Networks and Communications Ireland Santa Clara University Department of Computer Science and Engineering Santa ClaraCA United States

ISBN: (纸本)9781665431620

We are witnessing an emergence of a new era of applications delivered via a paradigm of flexible and softwarized communication networks. This has opened the market to a wider movement towards virtualized applications and services in key verticals such as automated vehicles, smart grid, virtual reality (VR), Internet of Things (IoT), industry 4.0, telecommunications, etc. With an increasing emergence of verticals driven by the vision of low latency and high reliability, there is a wide gap to efficiently bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient in the long run. In this work, we present a Residual Long Short-Term Memory (LSTM) based multi-label classification framework for proactive SLA management in a latency-critical Network Function Virtualization (NFV) application use case. We compose a multivariate time-series forecasting model with multiple time-step predictions in a multi-output scenario, and associate a multi-label classifier for a granular prediction of individual Service Level Objective (SLO) violations for each step in the forecast horizon. The Residual LSTM approach achieves an improvement of 31.1% over the baseline on the forecast classification accuracy, and a 2.65% improvement on the interpolated average precision over the standard LSTM methodology. © 2022 IEEE.

关键词： Long short-term memory

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A smart resource-efficient surveillance system 20

A smart resource-efficient surveillance system

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6th International Conference on Engineering and MIS, ICEMIS 2020

作者： Kechroud, Amine A. Alseah, Salem A. Musbah, Mohamed S. Computer Networks and Data Communications Department University of Benghazi Benghazi Libya

ISBN: (纸本)9781450377362

The excessively increasing demand on bandwidth resources entails many challenges in terms of acquiring data while saving its acquisition resources. Surveillance systems are of those vital applications that consume data bandwidth. In this paper, a smart prototype surveillance system is proposed using machine learning techniques as a mean of saving bandwidth by transmitting only the significant information through the system's resources. The proposed system operates in two modes;motion detection and face recognition. Python environment and some of relevant libraries were used to establish the prototype. Promising and interesting results were obtained in terms of bandwidth and storage saving. © 2020 ACM.

关键词： Bandwidth

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A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things

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Computational Intelligence and Neuroscience 2022年 2022卷 5112375页

作者： Khan, Zard Ali Naz, Sheneela Khan, Rahim Teo, Jason Ghani, Abdullah Almaiah, Mohammed Amin Department of Computer Science Comsats University Islamabad45550 Pakistan Faculty of Computing and Informatics University of Malaysia Sabah Malaysia Department of Computer Science Abdul Wali Khan University Mardan Pakistan Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al-Ahsa Saudi Arabia

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only;that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches. © 2022 Zard Ali Khan et al.

关键词： Energy efficiency

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Agnostic Prediction of Multiple Types of Time-Varying Traffic in Optical networks

Agnostic Prediction of Multiple Types of Time-Varying Traffi...

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IEEE Conference on Global communications (GLOBECOM)

作者： Aleksandra Knapińska Piotr Lechowicz Salvatore Spadaro Krzysztof Walkowiak Department of Systems and Computer Networks Wrocław University of Science and Technology Wrocław Poland Optical Communications Group Universitat Politècnica de Catalunya Barcelona Spain

Relentless competition among communications ser-vice providers, increasing expectations of users, and escalating variety of new applications and services trigger the need to develop advanced solutions that can support the optimization and management of communication networks. Prediction of network traffic is one of the possible solutions providing additional data analytics information. In this paper, we consider an application-aware optical network that transmits various types of time-varying traffic. We analyze traffic prediction under two scenarios. As a reference scenario, we assume that the system is aware of multiple traffic types, and various prediction models are developed and trained for each traffic type separately. The second scenario - agnostic prediction - assumes that a single prediction model agnostic of the traffic types is created and trained. We develop several models for each of the analyzed scenarios using various regression methods. Next, we run extensive numerical experiments on real and semi-synthetic datasets to verify the performance of the proposed regression methods and compare both analyzed scenarios. The obtained results demonstrate that the proposed prediction model agnostic to the forecasted type of traffic provides excellent results; in many cases, it outperforms the reference scenario with dedicated prediction models for each traffic type. Moreover, we evaluate the proposed model's adaptability to predict unseen-before traffic types, showing that the quality loss is negligible. Finally, we test the proposed framework in a multilayer network with time-varying traffic and show how using an aggregated model does not lead to bandwidth blocking increase compared to dedicated prediction models.

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Highly integrated reconfigurable radiofrequency filter based on microcomb and a silicon photonic processor

Highly integrated reconfigurable radiofrequency filter based...

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CLEO: Science and Innovations, S and I 2022

作者： Tao, Yuansheng Chang, Lin Shen, Bitao Shu, Haowen Xie, Weiqiang Jin, Ming Tao, Zihan Bai, Bowen Yu, Shaohua Wang, Xingjun Bowers, John E. State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peng Cheng Laboratory Shenzhen518055 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China

We demonstrate a novel chip-scale reconfigurable radiofrequency filter by using AlGaAs-on-insulator microcomb and silicon photonics in combination, simultaneously bypassing the employment of bulky pulse shaper and lon... 详细信息

ISBN: (纸本)9781557528209

关键词： Silicon photonics

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An Adaptive Service Function Chains Mapping With Multi-Task Deep Reinforcement Learning

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IEEE Transactions on Network Science and Engineering 2025年

作者： Wei, Wenting Wang, Qingyi Gu, Huaxi Zheng, Danyang Zhang, Ning Wu, Celimuge Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Southwest Jiaotong University School of Computing and Artificial Intelligence China University of Windsor Department of Electrical and Computer Engineering Canada University of Electro-Communications Meta-Networking Research Center Japan

Network function virtualization (NFV) facilitates different virtual network functions (VNF) to be dynamically chained in sequence to offer new services in a flexible, scalable, and cost-effective manner. Recent years have witnessed the increasing diverse service demands from the ever-increasing new applications, which has posed significant challenges to the efficient and sequential execution of VNFs to achieve specific objectives, especially under conditions of shared resources. To address these challenges, substantial efforts have been dedicated to enhancing resource utilization and minimizing the costs associated with service function chains (SFCs), while maintaining high quality of service. However, an overemphasis on cost reduction can sometimes result in network congestion, which ultimately degrades both network performance and service quality. Given the time-varying and unpredictable characteristics of SFCs, it is essential to leverage their temporal features, along with those of network states, for adaptive SFC mapping. In this paper, we introduce an adaptive online SFC mapping algorithm to reduce operational costs and alleviate network congestion. This is achieved through the adaptive allocation of VNFs and the control of traffic routing between them. Our approach incorporates multi-task deep reinforcement learning to manage the coexistence of multiple SFC requests with varying resource requirements. Specifically, we integrate a long short-term memory (LSTM) layer into our model to capture the temporal dynamics of network states and resource demands, thereby enabling more effective long-term planning. To address the issue of reward sparsity, we implement a hierarchical reward mechanism and reward shaping techniques. Experimental results demonstrate that our algorithm achieves near-optimal performance in optimizing service delay, bandwidth consumption, and network congestion, while also ensuring a high acceptance rate for user requests. © 2013 IEEE.

关键词： Network function virtualization

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Testing and Exploiting Tools to Improve OWASP Top Ten Security Vulnerabilities Detection

Testing and Exploiting Tools to Improve OWASP Top Ten Securi...

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International Conference on Computational Intelligence and Communication networks (CICN)

作者： Malak Aljabri Maryam Aldossary Noor Al-Homeed Bushra Alhetelah Malek Althubiany Ohoud Alotaibi Sara Alsaqer Networks and Communications Department SAUDI ARAMCO Cybersecurity Chair College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Networks and Communications Department College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (纸本)9781665487726

In many different sorts of businesses, including public and private, government, critical infrastructures, etc., web apps have grown recently. Therefore, securing web applications is a major concern to protect businesses from loss and unauthorized access to sensitive information. Developers use vulnerable thirdparty modules or components or create programming security flaws themselves and occasionally work with tight budgets. These situations frequently cause people to overlook a crucial aspect of development life cycle security. This paper studies and tests the currently available web security and exploitation tools of OWASP's top ten security vulnerabilities. The main aim of this paper is to improve the detection of OWASP's top ten security vulnerabilities by proposing an exploitation and detection tool that combined features of the tools that has been tested in the paper.

关键词： Government Programming Feature extraction Critical infrastructure Security Communication networks Computational intelligence

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Secure Wireless-Powered zeRIS communications

arXiv

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arXiv 2025年

作者： Chen, Jingyu Cao, Kunrui Diamantoulakis, Panagiotis D. Lv, Lu Yang, Liang Chi, Haolian Ding, Haiyang School of Information and Communications National University of Defense Technology Wuhan430035 China Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

This paper introduces the concept of wireless-powered zero-energy reconfigurable intelligent surface (zeRIS), and investigates a wireless-powered zeRIS aided communication system in terms of security, reliability and energy efficiency. In particular, we propose three new wireless-powered zeRIS modes: 1) in mode-I, N reconfigurable reflecting elements are adjusted to the optimal phase shift design of information user to maximize the reliability of the system;2) in mode-II, N reconfigurable reflecting elements are adjusted to the optimal phase shift design of cooperative jamming user to maximize the security of the system;3) in mode-III, N1 and N2 (N1+N2 = N) reconfigurable reflecting elements are respectively adjusted to the optimal phase shift designs of information user and cooperative jamming user to balance the reliability and security of the system. Then, we propose three new metrics, i.e., joint outage probability (JOP), joint intercept probability (JIP), and secrecy energy efficiency (SEE), and analyze their closed-form expressions in three modes, respectively. The results show that under high transmission power, all the diversity gains of three modes are 1, and the JOPs of mode-I, mode-II and mode-III are improved by increasing the number of zeRIS elements, which are related to N2, N, and N12, respectively. In addition, mode-I achieves the best JOP, while mode-II achieves the best JIP among three modes. We exploit two security-reliability trade-off (SRT) metrics, i.e., JOP versus JIP, and normalized joint intercept and outage probability (JIOP), to reveal the SRT performance of the proposed three modes. It is obtained that mode-II outperforms the other two modes in the JOP versus JIP, while mode-III and mode-II achieve the best performance of normalized JIOP at low and high transmission power, respectively. © 2025, CC BY.

关键词： Outages

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Quantum noise of dark pulse microcombs

Quantum noise of dark pulse microcombs

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CLEO: QELS_Fundamental Science, QELS 2022

作者： Lao, Chenghao Jin, Xing Chang, Lin Xie, Weiqiang Shu, Haowen Wang, Xingjun Bowers, John E. Yang, Qi-Fan State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Frontiers Science Center for Nano-Optoelectronics School of Physics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China

We investigate the fundamental timing jitter of dark pulse microcombs generated in an AlGaAs microresonator, where the quantum-limited frequency noise of the 91-GHz repetition rate is measured to be 0.5 Hz2/Hz. ©... 详细信息

ISBN: (纸本)9781557528209

关键词： Aluminum gallium arsenide

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On Advantages of Traffic Prediction and Grooming for Provisioning of Time-Varying Traffic in Multilayer networks

On Advantages of Traffic Prediction and Grooming for Provisi...

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International Conference on Optical Network Design and Modeling, ONDM

作者： Aleksandra Knapińska Piotr Lechowicz Salvatore Spadaro Krzysztof Walkowiak Department of Systems and Computer Networks Wrocław University of Science and Technology Wrocław Poland Optical Communications Group (GCO) Universitat Politècnica de Catalunya (UPC) Barcelona Spain

The constantly increasing traffic volume triggers the fast development of new optimization methods for backbone optical networks. Operators seek new ways to improve several network performance metrics, including network stability. In this paper, we propose a multilayer packet-over-optical approach combining grooming and traffic prediction. We demonstrate the advantages of the proposed algorithm for provisioning daily time-varying traffic of various services through multiple metrics. We show how the proposed approach, enabled by the cross-layer information exchange, can achieve a served traffic increase of about 26% in operational scenarios.

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