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Exploring LoRaWAN Class B and Class C Devices: Performance Analysis and Parameter Optimization Strategies

Exploring LoRaWAN Class B and Class C Devices: Performance A...

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ieee International conference on Electro Information Technology (EIT)

作者： Jagatha, Aditya Rimal, Bhaskar P. Vaidyan, Varghese Wang, Yong Dakota State Univ Madison SD 57042 USA Univ Idaho Dept Comp Sci Moscow ID USA

ISBN: (纸本)9798350330656;9798350330649

LoRaWAN is an open networking protocol used to create long-range communication for low-powered devices. LoRaWAN allows the configuration of several parameters that affect the network performance. LoRaWAN end devices utilize this protocol for communication in the Internet Of Things (IoT). These devices transmit and receive data through radio gateways using LoRa RF Modulation. LoRaWAN end devices include Class A, B, and C operational categories that vary based on their functionality in communication with the network. Research has been undertaken on Class A end devices, but a notable gap exists in studies dedicated to Class B and Class C devices. Understanding the communication behaviors of these devices is important for finely tuned and efficient implementations. This study aims to bridge this gap by performing a simulationbased performance analysis in ns-3 for these device classes. Additionally, we demonstrate the potential for significant performance improvements through the careful adjustments of system parameters.

关键词： C (programming language)

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Improving the Performance of the Ryu SDN Controller Through Multi-Threading

Improving the Performance of the Ryu SDN Controller Through ...

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2024 International conference on Intelligent Communication, Sensing and Electromagnetics, ICSE 2024

作者： Mamadou, B.A. Kondengar, Thierry Ndiaye, Alie Ngom, Ibrahima Doumbia, Oumane Ouya, Samuel Higher Polytechnic School LITA University Cheikh Anta Diop of Dakar Dakar Senegal Central School for Open Source Software Dakar Senegal

ISBN: (纸本)9798350354935

Software-Defined networks (SDN) fundamentally transform traditional network architecture. However, the performance of SDN controllers, particularly in their single-Threaded implementations like Ryu, poses a major challenge for large-scale environments. This paper proposes an innovative multi-Threaded architecture to enhance the performance of the Ryu controller. Our approach enables efficient parallel management of network events without requiring major changes to the source code. Experimental tests conducted with the Cbench tool demonstrate significant performance improvements, with a 174% increase in throughput (from 2112.30 to 5791.66 flows per second) and a notable reduction in latency. This solution addresses the growing demands of modern network infrastructures while maintaining compatibility with existing applications. © 2024 ieee.

关键词： Parallel architectures

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Roracle: Enabling Lookahead Routing for Scalable Traffic Engineering with Supervised Learning 31

Roracle: Enabling Lookahead Routing for Scalable Traffic Eng...

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31st ieee International conference on network Protocols (ICNP)

作者： Ye, Minghao Zhang, Junjie Guo, Zehua Chao, H. Jonathan New York Univ New York NY 10012 USA Fortinet Inc Sunnyvale CA USA Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9798350303223

Traditional Traffic Engineering (TE) usually balances the load on network links by formulating and solving a routing optimization problem based on measured Traffic Matrices (TMs). Given that traffic demands could change unexpectedly and significantly in realistic scenarios, routing strategies optimized based on currently measured TMs might not work well in future traffic scenarios. To compensate for the mismatch between stale routing decisions and future TMs, network operators may perform routing updates more frequently, which could introduce significant network disturbance and service disruption. Moreover, given the high routing computation overhead of TE optimization in today's large-scale networks, routing updates could experience severe delay and thus cannot accommodate future traffic changes in time. To address these challenges, we propose Roracle, a scalable learning-based TE that quickly predicts a good routing strategy for a long sequence of future TMs, while the learning process is guided by the optimal solutions of Linear programming (LP) problems using Supervised Learning (SL). We design a scalable Graph Neural network (GNN) architecture that greatly facilitates training and inference processes to accelerate TE in large networks. Extensive simulation results on real world network topologies and traffic traces show that Roracle outperforms existing TE solutions by up to 36% in terms of worst -case performance under future unknown traffic scenarios. Additionally, Roracle achieves good scalability by providing at least 71 x speedup over the most efficient baseline method in large-scale networks.

关键词： Linear programming

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An Analysis of Edge Communication architectures for Ultra-Dense Fog networks 2

An Analysis of Edge Communication Architectures for Ultra-De...

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2nd International conference on Disruptive Technologies, ICDT 2024

作者： Singh, Raghvendra Pratap Mishra, Ghanshyam Singh, Mahesh Kumar Jain, Sachin Ajay Kumar Garg Engineering College Department of Ece UP Ghaziabad India Buddha Institute of Technology Gida UP Gorakhpur India Ajay Kumar Garg Engineering College Department of Cse UP Ghaziabad India

ISBN: (纸本)9798350371055

In ultra-dense fog networks, faceted conversation architectures are crucial because they provide low latency, high capacity, and electricity-green speech communication. Several component verbal exchange topologies, such as distributed antennas, mesh networks, and multi-hop ad hoc networks, have been the focus of recent research. Along with open mobile coverage, lower latency, and more potential, these also bring blessings. An overview of the benefits and drawbacks of the current nation of part verbal exchange architectures is provided in this study. We examine the three most well-known side communication designs and talk about how well they work overall in extraordinarily thick fog networks. We analyze the architectures with respect to strength performance, capability, latency, and coverage. While multi-hop advert hoc networks are better suited for excessive potential and energy efficiency, mesh networks provide a great way to distribute antennas for low latency and coverage. In general, it is important to understand the advantages and disadvantages of each structure in order to choose the best structure for an exceptionally thick fog network. We present a thorough examination of the various edge conversation architectures and suggest directions for further research. © 2024 ieee.

关键词： Antennas Communication Guidelines network Structure

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Scheduling Model for Congestion-free Virtualized network Update

Scheduling Model for Congestion-free Virtualized Network Upd...

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ieee conference on Global Communications (ieee GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Sato, Takehiro Kurimoto, Takashi Urushidani, Shigeo Oki, Eiji Kyoto Univ Kyoto Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798350310900

This paper proposes a scheduling model for updating resource allocations for virtualized networks (VNs) without congestion. The proposed model determines the schedule of migrating traffic flows on VNs from old routes to new routes. The model aims to minimize the number of rounds required to complete the update of all existing VNs. We evaluate the performance of model in terms of the percentage of trials where feasible update scheduling exists and the number of rounds required to complete the update. Numerical results show that more rounds are required to achieve congestion-free update when the traffic demand or the number of VNs increases. The number of required rounds tends to remain the same when the maximum number of rounds exceeds a certain value;this observation helps network operators estimate the time required for VN update.

关键词： network virtualization optimization model integer linear programming network update

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A Local Optima network Analysis of the Feedforward Neural Architecture Space

A Local Optima Network Analysis of the Feedforward Neural Ar...

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ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / International Joint conference on Neural networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Potgieter, Isak Cleghorn, Christopher W. Bosman, Anna S. Univ Pretoria Dept Comp Sci Pretoria South Africa Univ Witwatersrand Sch Comp Sci & Appl Math Johannesburg South Africa

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

This study investigates the use of local optima network (LON) analysis, a derivative of the fitness landscape of candidate solutions, to characterise and visualise the neural architecture space. The search space of feedforward neural network architectures with up to three layers, each with up to 10 neurons, is fully enumerated by evaluating trained model performance on a selection of data sets. Extracted LONs, while heterogeneous across data sets, all exhibit simple global structures, with single global funnels in all cases but one. These results yield early indication that LONs may provide a viable paradigm by which to analyse and optimise neural architectures.

关键词： neural architectures fitness landscapes local optima networks

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PR-ESP: An open-Source Platform for Design and programming of Partially Reconfigurable SoCs

PR-ESP: An Open-Source Platform for Design and Programming o...

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Seyoum, Biruk Giri, Davide Chiu, Kuan-Lin Natter, Bryce Carloni, Luca Columbia Univ Dept Comp Sci New York NY 10027 USA

ISBN: (纸本)9798350396249

Despite its presence for more than two decades and its proven benefits in expanding the space of system design, dynamic partial reconfiguration (DPR) is rarely integrated into frameworks and platforms that are used to design complex reconfigurable system-on-chip (SoC) architectures. This is due to the complexity of the DPR FPGA flow as well as the lack of architectural and software runtime support to enable and fully harness DPR. Moreover, as DPR designs involve additional design steps and constraints, they often have a higher FPGA compilation (RTL-to-bitstream) runtime compared to equivalent monolithic designs. In this work, we present PR-ESP, an open-source platform for a system-level design flow of partially reconfigurable FPGA-based SoC architectures targeting embedded applications that are deployed on resource-constrained FPGAs. Our approach is realized by combining SoC design methodologies and tools from the open-source ESP platform with a fully-automated DPR flow that features a novel size-driven technique for parallel FPGA compilation. We also developed a software runtime reconfiguration manager on top of Linux. Finally, we evaluated our proposed platform using the WAMI-App benchmark application on Xilinx VC707.

关键词： Field programmable gate arrays (FPGA)

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Active Batch Sampling for Multi-label Classification with Binary User Feedback

Active Batch Sampling for Multi-label Classification with Bi...

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ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Goswami, Debanjan Chakraborty, Shayok Florida State Univ Dept Comp Sci Tallahassee FL 32306 USA

ISBN: (纸本)9798350318920;9798350318937

Multi-label classification is a generalization of multi-class classification, where a single data sample can have multiple labels. While deep neural networks have depicted commendable performance for multi-label learning, they require a large amount of manually annotated training data to attain good generalization capability. However, annotating a multi-label data sample requires a human oracle to consider the presence/absence of every single class individually, which is extremely laborious. Active learning algorithms automatically identify the salient and exemplar instances from large amounts of unlabeled data and are effective in reducing human annotation effort in inducing a machine learning model. In this paper, we propose a novel active learning framework for multi-label learning, which queries a batch of (image-label) pairs and for each pair, poses the question whether the queried label is present in the corresponding image;the human annotators merely need to provide a binary feedback ("yes/no") in response to each query, which involves much less manual work. We pose the image and label selection as a constrained optimization problem and derive a linear programming relaxation to select a batch of (image-label) pairs, which are maximally informative to the underlying deep neural network. Our extensive empirical studies on three challenging datasets corroborate the potential of our method for real-world multi-label classification applications.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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network Attack Detection in the PfSense Firewall via Deep Learning

Network Attack Detection in the PfSense Firewall via Deep Le...

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2024 ieee International Multi-conference on Smart Systems and Green Process, IMC-SSGP 2024

作者： Soltane, Merzoug Gasmi, Aimen Tebessa Algeria Echahid Cheikh Larbi Tebessi University Department of Mathematics and Computer Science Tebessa Algeria

ISBN: (纸本)9798350378382

The detection of network attacks is a crucial aspect in ensuring the sustainability and proper functioning of information systems. Complex threat patterns and malicious actors possess the ability to inflict significant damage to cyber systems. In this study, we propose novel approaches utilizing deep learning techniques to identify and respond to threats and alerts found within network logs acquired through pfSense, an open-source firewall software. pfSense incorporates various robust security services including firewall capabilities, URL filtering, and virtual private networking (VPN), among others. The primary objective of this research is to analyze the acquired logs from a local installation of pfSense software, in order to develop a potent and efficient solution that can effectively manage traffic flow by automatically recognizing patterns through the proposed deep learning architectures, which present a significant challenge. To accomplish this, we have devised an attack detection system based on the CICDDOS2019 dataset, leveraging a deep neural network (DNN) model that has been seamlessly integrated into pfSense, enabling automatic identification and prevention of attacks. © 2024 ieee.

关键词： Computer system firewalls

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Self-Tuning network Control architectures 61

Self-Tuning Network Control Architectures

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ieee 61st conference on Decision and Control (CDC)

作者： Summers, Tyler Ganapathy, Karthik Shames, Iman Hudoba de Badyn, Mathias Univ Texas Dallas Control Optimizat & Networks Lab Richardson TX 75083 USA Australian Natl Univ Canberra ACT Australia Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland

ISBN: (纸本)9781665467612

We formulate a general mathematical framework for self-tuning network control architecture design. This problem involves jointly adapting the locations of active sensors and actuators in the network and the feedback control policy to all available information about the time-varying network state and dynamics to optimize a performance criterion. We propose a general solution structure analogous to the classical self-tuning regulator from adaptive control. We show that a special case with full-state feedback can be solved in principle with dynamic programming, and in the linear quadratic setting the optimal cost functions and policies are piecewise quadratic and piecewise linear, respectively. For large networks where exhaustive architecture search is prohibitive, we describe a greedy heuristic for joint architecture-policy design. We demonstrate in numerical experiments that self-tuning architectures can provide dramatically improved performance over fixed architectures. Our general formulation provides an extremely rich and challenging problem space with opportunities to apply a wide variety of approximation methods from stochastic control, system identification, reinforcement learning, and static architecture design.

关键词： Dynamic programming

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