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Cooperative Jamming for Physical Layer Security Enhancement Using Deep Reinforcement Learning arXiv.cs.NI Pub Date : 2024-03-15 Sayed Amir Hoseini, Faycal Bouhafs, Neda Aboutorab, Parastoo Sadeghi, Frank den Hartog
Wireless data communications are always facing the risk of eavesdropping and interception. Conventional protection solutions which are based on encryption may not always be practical as is the case for wireless IoT networks or may soon become ineffective against quantum computers. In this regard, Physical Layer Security (PLS) presents a promising approach to secure wireless communications through the
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NetBench: A Large-Scale and Comprehensive Network Traffic Benchmark Dataset for Foundation Models arXiv.cs.NI Pub Date : 2024-03-15 Chen Qian, Xiaochang Li, Qineng Wang, Gang Zhou, Huajie Shao
In computer networking, network traffic refers to the amount of data transmitted in the form of packets between internetworked computers or systems. Monitoring and analyzing network traffic is crucial for ensuring the performance, security, and reliability of a network. However, a significant challenge in network traffic analysis is to process diverse data packets including both ciphertext and plaintext
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RACH-less Handover with Early Timing Advance Acquisition for Outage Reduction arXiv.cs.NI Pub Date : 2024-03-15 Subhyal Bin Iqbal, Umur Karabulut, Ahmad Awada, Philipp Schulz, Gerhard P. Fettweis
For fifth-generation (5G) and 5G-Advanced networks, outage reduction within the context of reliability is a key objective since outage denotes the time period when a user equipment (UE) cannot communicate with the network. Earlier studies have shown that in the experimental high mobility scenario considered, outage is dominated by the interruption time that stems from the random access channel (RACH)-based
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NNCTC: Physical Layer Cross-Technology Communication via Neural Networks arXiv.cs.NI Pub Date : 2024-03-15 Haoyu Wang, Jiazhao Wang, Demin Gao, Wenchao Jiang
Cross-technology communication(CTC) enables seamless interactions between diverse wireless technologies. Most existing work is based on reversing the transmission path to identify the appropriate payload to generate the waveform that the target devices can recognize. However, this method suffers from many limitations, including dependency on specific technologies and the necessity for intricate algorithms
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Joint Optimization of STAR-RIS Assisted SWIPT Communication Systems arXiv.cs.NI Pub Date : 2024-03-15 Junlong Yang
Simultaneous wireless information and power transfer (SWIPT) is an effective energy-saving technology, but its efficiency is hindered by environmental factors. The introduction of reconfigurable intelligent surfaces (RIS) has alleviated this issue, although it still faces significant constraints due to geographical limitations. This paper proposes a scheme that employs a simultaneously transmitting
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An Admission Control Algorithm for Isochronous and Asynchronous Traffic in IEEE 802.11ad MAC arXiv.cs.NI Pub Date : 2024-03-14 Anirudha Sahoo
Due to availability of large amount of bandwidth in the 60 GHz band and support of contention-free channel access called Service Period (SP), the IEEE 802.11ad/ay Wi-Fi standard is well suited for low latency and high data rate applications. IEEE 802.11ad supports two types of SP user traffic: isochronous and asynchronous. These user traffic need guaranteed SP duration before their respective deadlines
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Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness arXiv.cs.NI Pub Date : 2024-03-15 Mohamed elShehaby, Aditya Kotha, Ashraf Matrawy
Machine Learning (ML) is susceptible to adversarial attacks that aim to trick ML models, making them produce faulty predictions. Adversarial training was found to increase the robustness of ML models against these attacks. However, in network and cybersecurity, obtaining labeled training and adversarial training data is challenging and costly. Furthermore, concept drift deepens the challenge, particularly
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An Energy-Efficient Ensemble Approach for Mitigating Data Incompleteness in IoT Applications arXiv.cs.NI Pub Date : 2024-03-15 Yousef AlShehri, Lakshmish Ramaswamy
Machine Learning (ML) is becoming increasingly important for IoT-based applications. However, the dynamic and ad-hoc nature of many IoT ecosystems poses unique challenges to the efficacy of ML algorithms. One such challenge is data incompleteness, which is manifested as missing sensor readings. Many factors, including sensor failures and/or network disruption, can cause data incompleteness. Furthermore
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Charting Censorship Resilience & Global Internet Reachability: A Quantitative Approach arXiv.cs.NI Pub Date : 2024-03-14 Marina Ivanović, François Wirz, Jordi Subirà Nieto, Adrian Perrig
Internet censorship and global Internet reachability are prevalent topics of today's Internet. Nonetheless, the impact of network topology and Internet architecture to these aspects of the Internet is under-explored. With the goal of informing policy discussions with an objective basis, we present an approach for evaluating both censorship resilience and global Internet reachability using quantitative
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PreConfig: A Pretrained Model for Automating Network Configuration arXiv.cs.NI Pub Date : 2024-03-14 Fuliang Li, Haozhi Lang, Jiajie Zhang, Jiaxing Shen, Xingwei Wang
Manual network configuration automation (NCA) tools face significant challenges in versatility and flexibility due to their reliance on extensive domain expertise and manual design, limiting their adaptability to diverse scenarios and complex application needs. This paper introduces PreConfig, an innovative NCA tool that leverages a pretrained language model for automating network configuration tasks
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Whittle Index Based User Association in Dense Millimeter Wave Networks arXiv.cs.NI Pub Date : 2024-03-14 Mandar R. Nalavade, Gaurav S. Kasbekar, Vivek S. Borkar
We address the problem of user association in a dense millimeter wave (mmWave) network, in which each arriving user brings a file containing a random number of packets and each time slot is divided into multiple mini-slots. This problem is an instance of the restless multi-armed bandit problem, and is provably hard to solve. Using a technique introduced by Whittle, we relax the hard per-stage constraint
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Evaluation of Control/User-Plane Denial-of-Service (DoS) Attack on O-RAN Fronthaul Interface arXiv.cs.NI Pub Date : 2024-03-13 Ferlinda Feliana, Ting-Wei Hung, Binbin Chen, Ray-Guang Cheng
The open fronthaul interface defined by O-RAN ALLIANCE aims to support the interoperability between multi-vendor open radio access network (O-RAN) radio units (O-RU) and O-RAN distributed units (O-DU). This paper introduces a new tool that could be used to evaluate Denial-of-Service (DoS) attacks against the open fronthaul interface. We launched an array of control/user planes (C/U-Planes) attacks
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An Extended View on Measuring Tor AS-level Adversaries arXiv.cs.NI Pub Date : 2024-03-13 Gabriel Karl Gegenhuber, Markus Maier, Florian Holzbauer, Wilfried Mayer, Georg Merzdovnik, Edgar Weippl, Johanna Ullrich
Tor provides anonymity to millions of users around the globe which has made it a valuable target for malicious actors. As a low-latency anonymity system, it is vulnerable to traffic correlation attacks from strong passive adversaries such as large autonomous systems (ASes). In preliminary work, we have developed a measurement approach utilizing the RIPE Atlas framework -- a network of more than 11
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Plotinus: A Satellite Internet Digital Twin System arXiv.cs.NI Pub Date : 2024-03-13 Yue Gao, Kun Qiu, Zhe Chen, Wenjun Zhu, Qi Zhang, Handong Luo, Quanwei Lin, Ziheng Yang, Wenhao Liu
The development of integrated space-air-ground network (SAGIN) requires sophisticated satellite Internet emulation tools that can handle complex, dynamic topologies and offer in-depth analysis. Existing emulation platforms struggle with challenges like the need for detailed implementation across all network layers, real-time response times, and the ability to scale. Plotinus, a new digital twin system
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MobileAtlas: Geographically Decoupled Measurements in Cellular Networks for Security and Privacy Research arXiv.cs.NI Pub Date : 2024-03-13 Gabriel Karl Gegenhuber, Wilfried Mayer, Edgar Weippl, Adrian Dabrowski
Cellular networks are not merely data access networks to the Internet. Their distinct services and ability to form large complex compounds for roaming purposes make them an attractive research target in their own right. Their promise of providing a consistent service with comparable privacy and security across roaming partners falls apart at close inspection. Thus, there is a need for controlled testbeds
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Zero-Rating, One Big Mess: Analyzing Differential Pricing Practices of European MNOs arXiv.cs.NI Pub Date : 2024-03-12 Gabriel Karl Gegenhuber, Wilfried Mayer, Edgar Weippl
Zero-rating, the practice of not billing data traffic that belongs to certain applications, has become popular within the mobile ecosystem around the globe. There is an ongoing debate whether mobile operators should be allowed to differentiate traffic or whether net neutrality regulations should prevent this. Despite the importance of this issue, we know little about the technical aspects of zero-rating
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From Files to Streams: Revisiting Web History and Exploring Potentials for Future Prospects arXiv.cs.NI Pub Date : 2024-03-12 Lucas Vogel, Thomas Springer, Matthias Wählisch
Over the last 30 years, the World Wide Web has changed significantly. In this paper, we argue that common practices to prepare web pages for delivery conflict with many efforts to present content with minimal latency, one fundamental goal that pushed changes in the WWW. To bolster our arguments, we revisit reasons that led to changes of HTTP and compare them systematically with techniques to prepare
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Adapting LoRaWAN to the Open-RAN Architecture arXiv.cs.NI Pub Date : 2024-03-12 Sobhi Alfayoumi, Joan Melia-Segui, Xavier Vilajosana
This article proposes O-LoRaWAN, an adaptation of the LoRaWAN architecture into a modular network architecture based on the Open RAN (O-RAN) principles. In our vision, standardization of the network components and interfaces will enable the reuse of network functions, and thus, foster an accelerated tailoring of the network functions to the changing application demands. LoRaWAN shares similarities
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Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC) arXiv.cs.NI Pub Date : 2024-03-12 Masoud Shokrnezhad, Hao Yu, Tarik Taleb, Richard Li, Kyunghan Lee, Jaeseung Song, Cedric Westphal
In the context of advancing 6G, a substantial paradigm shift is anticipated, highlighting comprehensive everything-to-everything interactions characterized by numerous connections and stringent adherence to Quality of Service/Experience (QoS/E) prerequisites. The imminent challenge stems from resource scarcity, prompting a deliberate transition to Computing-Network Convergence (CNC) as an auspicious
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Dynamic Frequency Assignment for Mobile Users in Multibeam Satellite Constellations arXiv.cs.NI Pub Date : 2024-03-09 Guillem Casadesus-Vila, Juan Jose Garau-Luis, Nils Pachler, Edward Crawley, Bruce Cameron
Mobile users such as airplanes or ships will constitute an important segment of the future satellite communications market. Operators are now able to leverage digital payloads that allow flexible resource allocation policies that are robust against dynamic user bases. One of the key problems is managing the frequency spectrum efficiently, which has not been sufficiently explored for mobile users. To
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Monitoring the Venice Lagoon: an IoT Cloud-Based Sensor Nerwork Approach arXiv.cs.NI Pub Date : 2024-03-11 Filippo Campagnaro, Matin Ghalkhani, Riccardo Tumiati, Federico Marin, Matteo Del Grande, Alessandro Pozzebon, Davide De Battisti, Roberto Francescon, Michele Zorzi
Monitoring the coastal area of the Venice Lagoon is of significant importance. While the impact of global warming is felt worldwide, coastal and littoral regions bear the brunt more prominently. These areas not only face the threat of rising sea levels but also contend with the escalating occurrence of seaquakes and floods. Additionally, the intricate ecosystems of rivers, seas, and lakes undergo profound
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Joint Source-and-Channel Coding for Small Satellite Applications arXiv.cs.NI Pub Date : 2024-03-11 Olga Kondrateva, Stefan Dietzel, Björn Scheuermann
Small satellites are widely used today as cost effective means to perform Earth observation and other tasks that generate large amounts of high-dimensional data, such as multi-spectral imagery. These satellites typically operate in low earth orbit, which poses significant challenges for data transmission due to short contact times with ground stations, low bandwidth, and high packet loss probabilities
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ACM MMSys 2024 Bandwidth Estimation in Real Time Communications Challenge arXiv.cs.NI Pub Date : 2024-03-10 Sami Khairy, Gabriel Mittag, Scott Inglis, Vishak Gopal, Mehrsa Golestaneh, Ross Cutler, Francis Yan, Zhixiong Niu
The quality of experience (QoE) delivered by video conferencing systems to end users depends in part on correctly estimating the capacity of the bottleneck link between the sender and the receiver over time. Bandwidth estimation for real-time communications (RTC) remains a significant challenge, primarily due to the continuously evolving heterogeneous network architectures and technologies. From the
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Evaluation and improvement of ETSI ITS Contention-Based Forwarding (CBF) of warning messages in highway scenarios arXiv.cs.NI Pub Date : 2024-03-09 Oscar Amador, Manuel Urueña, Maria Calderon, Ignacio Soto
This paper evaluates the performance of the ETSI Contention-Based Forwarding (CBF) GeoNetworking protocol for distributing warning messages in highway scenarios, including its interaction with the Decentralized Congestion Control (DCC) mechanism. Several shortcomings of the standard ETSI CBF algorithm are identified, and we propose different solutions to these problems, which are able to reduce the
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Enabling 5G QoS configuration capabilities for IoT applications on container orchestration platform arXiv.cs.NI Pub Date : 2024-03-08 Yu Liu, Aitor Hernandez Herranz
Container orchestration platform is the foundation of modern cloud infrastructure. In recent years, container orchestration platform has been evolving to cross the boundary of device, edge, and cloud. More and more IoT applications such as robotics and XR have been deployed across the device-cloud continuum through the container orchestration platform, e.g., the Kubernetes (K8s) framework. Meanwhile
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Wykorzystanie Rekonfigurowalnych Iinteligentnych Matryc Antenowych w Łączu Dosyłowym Sieci 5G/6G Wykorzystującej Bezzałogowe Statki Powietrzne arXiv.cs.NI Pub Date : 2024-03-08 Salim Janji, Paweł Sroka, Adrian Kliks
Drony, dzi\k{e}ki mo\.zliwo\'sci ich szybkiego rozmieszczenia w trudnym terenie, uwa\.zane s\k{a} za jeden z kluczowych element\'ow system\'ow bezprzewodowych 6G. Jednak w celu wykorzystania ich jako punkty dost\k{e}powe sieci konieczne jest zapewnienie {\l}\k{a}cza dosy{\l}owego o odpowiedniej przepustowo\'sci. Dlatego w niniejszym artykule rozwa\.zane jest zwi\k{e}kszenie zasi\k{e}gu sieci bezprzewodowej
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RIS-aided multi-hop backhauling for 5G/6G UAV-assisted access points arXiv.cs.NI Pub Date : 2024-03-08 Salim Janji, Paweł Sroka
Drones are envisaged as an important part of the future 6G systems. With the possibility of their fast deployment they provide additional connectivity options in form of a hotspot. However, typically in such a use case they require provisioning of a wireless backhaul link to facilitate their proper operation, which might be a challenging task in urban environment. One of the possible ways to connect
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ADROIT6G DAI-driven Open and Programmable Architecture for 6G Networks arXiv.cs.NI Pub Date : 2024-03-08 Christophoros Christophorou, Iacovos Ioannou, Vasos Vassiliou, Loizos Christofi, John S Vardakas, Erin E Seder, Carla Fabiana Chiasserini, Marius Iordache, Chaouki Ben Issaid, Ioannis Markopoulos, Giulio Franzese, Tanel Järvet, Christos Verikoukis
In the upcoming 6G era, mobile networks must deal with more challenging applications (e.g., holographic telepresence and immersive communication) and meet far more stringent application requirements stemming along the edge-cloud continuum. These new applications will create an elevated level of expectations on performance, reliability, ubiquity, trustworthiness, security, openness, and sustainability
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Evaluation of Road User Radio-Frequency Exposure Levels in an Urban Environment from Vehicular Antennas and the Infrastructure in ITS-G5 5.9 GHz Communication arXiv.cs.NI Pub Date : 2024-03-08 Martina Benini, Silvia Gallucci, Marta Bonato, Marta Parazzini, Gabriella Tognola
This study aims to investigate the variability of exposure levels among road users generated in a realistic urban scenario by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies operating at 5.9 GHz. The exposure levels were evaluated in terms of whole-body Specific Absorption Rate (wbSAR) [W/kg] in three different human models, ranging from children to adults. We
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An End-to-End Pipeline Perspective on Video Streaming in Best-Effort Networks: A Survey and Tutorial arXiv.cs.NI Pub Date : 2024-03-08 Leonardo Peroni, Sergey Gorinsky
Video streaming continues to captivate attention of users and service providers, dominate in Internet traffic, and form a vibrant research field. Taking a pragmatic approach to reviewing recent research in the field, this paper considers the most dominant streaming paradigm, the main aspects of which include transmission of two-dimensional videos over the best-effort Internet, support from content
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Adaptive Split Learning over Energy-Constrained Wireless Edge Networks arXiv.cs.NI Pub Date : 2024-03-08 Zuguang Li, Wen Wu, Shaohua Wu, Wei Wang
Split learning (SL) is a promising approach for training artificial intelligence (AI) models, in which devices collaborate with a server to train an AI model in a distributed manner, based on a same fixed split point. However, due to the device heterogeneity and variation of channel conditions, this way is not optimal in training delay and energy consumption. In this paper, we design an adaptive split
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iTRPL: An Intelligent and Trusted RPL Protocol based on Multi-Agent Reinforcement Learning arXiv.cs.NI Pub Date : 2024-03-07 Debasmita Dey, Nirnay Ghosh
Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that
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Performance evaluation of conditional handover in 5G systems under fading scenario arXiv.cs.NI Pub Date : 2024-03-07 Souvik Deb, Megh Rathod, Rishi Balamurugan, Shankar K. Ghosh, Rajeev K. Singh, Samriddha Sanyal
To enhance the handover performance in fifth generation (5G) cellular systems, conditional handover (CHO) has been evolved as a promising solution. Unlike A3 based handover where handover execution is certain after receiving handover command from the serving access network, in CHO, handover execution is conditional on the RSRP measurements from both current and target access networks, as well as on
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Super-resolution on network telemetry time series arXiv.cs.NI Pub Date : 2024-03-07 Fengchen Gong, Divya Raghunathan, Aarti Gupta, Maria Apostolaki
Fine-grained monitoring is crucial for multiple data-driven tasks such as debugging, provisioning, and securing networks. Yet, practical constraints in collecting, extracting, and storing data often force operators to use coarse-grained sampled monitoring, degrading the performance of the various tasks. In this work, we explore the feasibility of leveraging the correlations among coarse-grained time
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Improving HTTP/3 Quality of Experience with EPS arXiv.cs.NI Pub Date : 2024-03-06 Abhinav Gupta, Radim Bartos
With the introduction of QUIC, a modern transport-layer network protocol, HTTP/3 leverages its benefits to enhance web content delivery. This paper proposes a mechanism based on the recently standardized Extensible Prioritization Scheme (EPS) for weighted incremental web content delivery. The mechanism augments the sequential scheduling to provide incremental and weighted incremental resource delivery
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GreenBytes: Intelligent Energy Estimation for Edge-Cloud arXiv.cs.NI Pub Date : 2024-03-07 Kasra Kassai, Tasos Dagiuklas, Satwat Bashir, Muddesar Iqbal
This study investigates the application of advanced machine learning models, specifically Long Short-Term Memory (LSTM) networks and Gradient Booster models, for accurate energy consumption estimation within a Kubernetes cluster environment. It aims to enhance sustainable computing practices by providing precise predictions of energy usage across various computing nodes. Through meticulous analysis
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Architectural Blueprint For Heterogeneity-Resilient Federated Learning arXiv.cs.NI Pub Date : 2024-03-07 Satwat Bashir, Tasos Dagiuklas, Kasra Kassai, Muddesar Iqbal
This paper proposes a novel three tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates
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On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks arXiv.cs.NI Pub Date : 2024-03-07 Bingkun Lai, Jiayi He, Jiawen Kang, Gaolei Li, Minrui Xu, Tao zhang, Shengli Xie
Generative Artificial Intelligence (GAI) shows remarkable productivity and creativity in Mobile Edge Networks, such as the metaverse and the Industrial Internet of Things. Federated learning is a promising technique for effectively training GAI models in mobile edge networks due to its data distribution. However, there is a notable issue with communication consumption when training large GAI models
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In the Search of Optimal Tree Networks: Hardness and Heuristics arXiv.cs.NI Pub Date : 2024-03-06 Maxim Buzdalov, Pavel Martynov, Sergey Pankratov, Vitaly Aksenov, Stefan Schmid
Demand-aware communication networks are networks whose topology is optimized toward the traffic they need to serve. These networks have recently been enabled by novel optical communication technologies and are investigated intensively in the context of datacenters. In this work, we consider networks with one of the most common topologies~ -- a binary tree. We show that finding an optimal demand-aware
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Joint User Association and Resource Allocation for Tailored QoS Provisioning in 6G HetNets arXiv.cs.NI Pub Date : 2024-03-06 Yongqin Fu, Xianbin Wang
The proliferation of wireless-enabled applications with divergent quality of service (QoS) requirements necessitates tailored QoS provisioning. With the growing complexity of wireless infrastructures, application-specific QoS perceived by a user equipment (UE) is jointly determined by its association with the supporting base station in heterogeneous networks (HetNets) and the amount of resource allocated
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Scalable Network Tomography for Dynamic Spectrum Access arXiv.cs.NI Pub Date : 2024-03-06 Aadesh Madnaik, N. Cameron Matson, Karthikeyan Sundaresan
Mobile networks have increased spectral efficiency through advanced multiplexing strategies that are coordinated by base stations (BS) in licensed spectrum. However, external interference on clients leads to significant performance degradation during dynamic (unlicensed) spectrum access (DSA). We introduce the notion of network tomography for DSA, whereby clients are transformed into spectrum sensors
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A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction arXiv.cs.NI Pub Date : 2024-03-05 Haofan Lu, Christopher Vattheuer, Baharan Mirzasoleiman, Omid Abari
We present NeWRF, a deep learning framework for predicting wireless channels. Wireless channel prediction is a long-standing problem in the wireless community and is a key technology for improving the coverage of wireless network deployments. Today, a wireless deployment is evaluated by a site survey which is a cumbersome process requiring an experienced engineer to perform extensive channel measurements
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A Connector for Integrating NGSI-LD Data into Open Data Portals arXiv.cs.NI Pub Date : 2024-03-06 Laura Martín, Jorge Lanza, Víctor González, Juan Ramón Santana, Pablo Sotres, Luis Sánchez
Nowadays, there are plenty of data sources generating massive amounts of information that, combined with novel data analytics frameworks, are meant to support optimisation in many application domains. Nonetheless, there are still shortcomings in terms of data discoverability, accessibility and interoperability. Open Data portals have emerged as a shift towards openness and discoverability. However
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Evacuation Management Framework towards Smart City-wide Intelligent Emergency Interactive Response System arXiv.cs.NI Pub Date : 2024-03-07 Anuj Abraham, Yi Zhang, Shitala Prasad
A smart city solution toward future 6G network deployment allows small and medium sized enterprises (SMEs), industry, and government entities to connect with the infrastructures and play a crucial role in enhancing emergency preparedness with advanced sensors. The objective of this work is to propose a set of coordinated technological solutions to transform an existing emergency response system into
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Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees arXiv.cs.NI Pub Date : 2024-03-05 Ehsan Nowroozi, Nada Jadalla, Samaneh Ghelichkhani, Alireza Jolfaei
Malicious URLs provide adversarial opportunities across various industries, including transportation, healthcare, energy, and banking which could be detrimental to business operations. Consequently, the detection of these URLs is of crucial importance; however, current Machine Learning (ML) models are susceptible to backdoor attacks. These attacks involve manipulating a small percentage of training
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Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks arXiv.cs.NI Pub Date : 2024-03-05 Ehsan Nowroozi, Imran Haider, Rahim Taheri, Mauro Conti
Federated Learning (FL) is a machine learning (ML) approach that enables multiple decentralized devices or edge servers to collaboratively train a shared model without exchanging raw data. During the training and sharing of model updates between clients and servers, data and models are susceptible to different data-poisoning attacks. In this study, our motivation is to explore the severity of data
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Quantum Data Management: From Theory to Opportunities arXiv.cs.NI Pub Date : 2024-03-05 Rihan Hai, Shih-Han Hung, Sebastian Feld
Quantum computing has emerged as a transformative tool for future data management. Classical problems in database domains, including query optimization, data integration, and transaction management, have recently been addressed using quantum computing techniques. This tutorial aims to establish the theoretical foundation essential for enhancing methodologies and practical implementations in this line
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Magnetic Localization for In-body Nano-communication Medical Systems arXiv.cs.NI Pub Date : 2024-03-04 Krzysztof Skos, Josep Miquel Jornet, Pawel Kulakowski
Nano-machines circulating inside the human body, collecting data on tissue conditions, represent a vital part of next-generation medical diagnostic systems. However, for these devices to operate effectively, they need to relay not only their medical measurements but also their positions. This paper introduces a novel localization method for in-body nano-machines based on the magnetic field, leveraging
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Probabilistic Fault-Tolerant Robust Traffic Grooming in OTN-over-DWDM Networks arXiv.cs.NI Pub Date : 2024-03-04 Dimitrios Michael Manias, Joe Naoum-Sawaya, Abbas Javadtalab, Abdallah Shami
The development of next-generation networks is revolutionizing network operators' management and orchestration practices worldwide. The critical services supported by these networks require increasingly stringent performance requirements, especially when considering the aspect of network reliability. This increase in reliability, coupled with the mass generation and consumption of information stemming
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Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks arXiv.cs.NI Pub Date : 2024-03-04 Dimitrios Michael Manias, Ali Chouman, Abdallah Shami
The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices. This transition to ubiquitous intelligence demands high connectivity, synchronicity, and end-to-end communication between users and network operators
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Towards Fair and Efficient Learning-based Congestion Control arXiv.cs.NI Pub Date : 2024-03-04 Xudong Liao, Han Tian, Chaoliang Zeng, Xinchen Wan, Kai Chen
Recent years have witnessed a plethora of learning-based solutions for congestion control (CC) that demonstrate better performance over traditional TCP schemes. However, they fail to provide consistently good convergence properties, including {\em fairness}, {\em fast convergence} and {\em stability}, due to the mismatch between their objective functions and these properties. Despite being intuitive
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Graph neural network for in-network placement of real-time metaverse tasks in next-generation network arXiv.cs.NI Pub Date : 2024-03-04 Sulaiman Muhammad Rashid, Ibrahim Aliyu, Il-Kwon Jeong, Tai-Won Um, Jinsul Kim
This study addresses the challenge of real-time metaverse applications by proposing an in-network placement and task-offloading solution for delay-constrained computing tasks in next-generation networks. The metaverse, envisioned as a parallel virtual world, requires seamless real-time experiences across diverse applications. The study introduces a software-defined networking (SDN)-based architecture
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Towards Memory-Efficient Traffic Policing in Time-Sensitive Networking arXiv.cs.NI Pub Date : 2024-03-04 Xuyan Jiang, Xiangrui Yang, Tongqing Zhou, Wenwen Fu, Wei Quan, Yihao Jiao, Yinhan Sun, Zhigang Sun
Time-Sensitive Networking (TSN) is an emerging real-time Ethernet technology that provides deterministic communication for time-critical traffic. At its core, TSN relies on Time-Aware Shaper (TAS) for pre-allocating frames in specific time intervals and Per-Stream Filtering and Policing (PSFP) for mitigating the fatal disturbance of unavoidable frame drift. However, as first identified in this work
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Superflows: A New Tool for Forensic Network Flow Analysis arXiv.cs.NI Pub Date : 2024-03-02 Michael Collins, Jyotirmoy V. Deshmukh, Dristi Dinesh, Mukund Raghothaman, Srivatsan Ravi, Yuan Xia
Network security analysts gather data from diverse sources, from high-level summaries of network flow and traffic volumes to low-level details such as service logs from servers and the contents of individual packets. They validate and check this data against traffic patterns and historical indicators of compromise. Based on the results of this analysis, a decision is made to either automatically manage
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Experimental Evaluation of the ETSI DCC Adaptive Approach and Related Algorithms arXiv.cs.NI Pub Date : 2024-03-02 Oscar Amador, Ignacio Soto, Maria Calderon, Manuel Urueña
Decentralized Congestion Control (DCC) mechanisms have been a core part of protocol stacks for vehicular networks since their inception and standardization. The ETSI ITS-G5 protocol stack for vehicular communications considers the usage of DCC not only in the network or access layers, but also as a part of the cross-layer architecture that influences how often messages are generated and transmitted
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Misconfiguration in O-RAN: Analysis of the impact of AI/ML arXiv.cs.NI Pub Date : 2024-03-02 Noe Yungaicela-Naula, Vishal Sharma, Sandra Scott-Hayward
User demand on network communication infrastructure has never been greater with applications such as extended reality, holographic telepresence, and wireless brain-computer interfaces challenging current networking capabilities. Open RAN (O-RAN) is critical to supporting new and anticipated uses of 6G and beyond. It promotes openness and standardisation, increased flexibility through the disaggregation
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I DPID It My Way! A Covert Timing Channel in Software-Defined Networks arXiv.cs.NI Pub Date : 2024-03-04 Robert Krösche, Kashyap Thimmaraju, Liron Schiff, Stefan Schmid
Software-defined networking is considered a promising new paradigm, enabling more reliable and formally verifiable communication networks. However, this paper shows that the separation of the control plane from the data plane, which lies at the heart of Software-Defined Networks (SDNs), can be exploited for covert channels based on SDN Teleportation, even when the data planes are physically disconnected
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MTS: Bringing Multi-Tenancy to Virtual Networking arXiv.cs.NI Pub Date : 2024-03-04 Kashyap Thimmaraju, Saad Hermak, Gábor Rétvári, Stefan Schmid
Multi-tenant cloud computing provides great benefits in terms of resource sharing, elastic pricing, and scalability, however, it also changes the security landscape and intro- duces the need for strong isolation between the tenants, also inside the network. This paper is motivated by the observation that while multi-tenancy is widely used in cloud computing, the virtual switch designs currently used
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IoT Device Labeling Using Large Language Models arXiv.cs.NI Pub Date : 2024-03-03 Bar Meyuhas, Anat Bremler-Barr, Tal Shapira
The IoT market is diverse and characterized by a multitude of vendors that support different device functions (e.g., speaker, camera, vacuum cleaner, etc.). Within this market, IoT security and observability systems use real-time identification techniques to manage these devices effectively. Most existing IoT identification solutions employ machine learning techniques that assume the IoT device, labeled
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FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission arXiv.cs.NI Pub Date : 2024-03-01 Zeling Zhang, Dongqi Cai, Yiran Zhang, Mengwei Xu, Shangguang Wang, Ao Zhou
Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models. In this paper, we propose FedRDMA, a communication-efficient cross-silo FL system that integrates RDMA into the FL communication protocol. To overcome the limitations of RDMA in wide-area networks (WANs), FedRDMA divides the updated model into chunks and designs