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检索条件"任意字段=Conference on Internet Performance and Control of Network Systems II"

共 4638 条记录，以下是141-150 订阅

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internet of Things (IoT) based Battery Maintenance System 7

Internet of Things (IoT) based Battery Maintenance System

引用

7th International conference on Intelligent Computing and control systems, ICICCS 2023

作者： Asvika, D. Deepika, M. Selvabarkavi, M. Shalini, V. Baby Information Technology Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9798350397253

Batteries, which are collections of electrochemical cells, produce electricity to run electrical equipment. Batteries continuously transform chemical energy into electrical energy, and for optimum performance, they need to be properly maintained using systems of management with particular monitoring capabilities. In order to avoid potential health, safety, and property risks associated with battery use, techniques including charge management systems and temperature regulation are implemented. These systems use merit-based metrics to control battery performance. In this work, neural network is used to keep track of the battery's health. The proposed system consists of a load cell, a temperature sensor, a voltage transformer, and a current transformer. It features automatic battery cleaning powered by servo motors. Also, alert for different battery fault conditions will be sent. © 2023 IEEE.

关键词： Electric loads

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Cross-Evaluation of Deep Learning-based network Intrusion Detection systems 10

Cross-Evaluation of Deep Learning-based Network Intrusion De...

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10th International conference on Future internet of Things and Cloud, FiCloud 2023

作者： Guida, Ciro Nascita, Alfredo Montieri, Antonio Pescape, Antonio University of Napoli 'Federico II' Italy University of Bergamo Italy

ISBN: (纸本)9798350316353

network Intrusion Detection systems are essential tools for protecting networks against attacks. Deep Learning approaches are increasingly employed in developing these systems due to their versatility and effectiveness. However, the common procedure for training and testing Deep Learning models typically leverages traffic data entirely collected from the operational network managed by a single organization, posing privacy and security concerns in sharing these data. As a result, the assessment of the performance of these models in real-world scenarios is significantly hindered. On the other hand, given the wide variety of existing attacks and the emergence of new attack types, it is crucial to evaluate the robustness of Intrusion Detection systems when the network context varies. Indeed, it is highly desirable that the effectiveness of trained Deep Learning models is not severely impacted when ported into other *** this aim, in this work, we exploit various single-modal and multimodal Deep Learning approaches and leverage a cross-evaluation procedure to assess their capability to distinguish malicious from benign traffic in different network contexts. Furthermore, we investigate the impact of various informative fields extracted from traffic on the generalization capability of models. Our cross-evaluation leverages three recent public-available network attack datasets related to diverse scenarios. The results obtained suggest that the availability at training time of traffic generated by attacks conducted in the operational network is crucial for designing a robust Intrusion Detection System that keeps working with minimal Fl-score degradation, when the network context changes. © 2023 IEEE.

关键词： network security

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Input-to-State Stable Coupled Oscillator networks for Closed-form Model-based control in Latent Space 38

Input-to-State Stable Coupled Oscillator Networks for Closed...

引用

38th conference on Neural Information Processing systems, NeurIPS 2024

作者： Stölzle, Maximilian Della Santina, Cosimo Department of Cognitive Robotics Delft University of Technology Netherlands

Even though a variety of methods have been proposed in the literature, efficient and effective latent-space control (i.e., control in a learned low-dimensional space) of physical systems remains an open challenge. We argue that a promising avenue is to leverage powerful and well-understood closed-form strategies from control theory literature in combination with learned dynamics, such as potential-energy shaping. We identify three fundamental shortcomings in existing latent-space models that have so far prevented this powerful combination: (i) they lack the mathematical structure of a physical system, (ii) they do not inherently conserve the stability properties of the real systems, (iii) these methods do not have an invertible mapping between input and latent-space forcing. This work proposes a novel Coupled Oscillator network (CON) model that simultaneously tackles all these issues. More specifically, (i) we show analytically that CON is a Lagrangian system - i.e., it possesses well-defined potential and kinetic energy terms. Then, (ii) we provide formal proof of global Input-to-State stability using Lyapunov arguments. Moving to the experimental side, we demonstrate that CON reaches SoA performance when learning complex nonlinear dynamics of mechanical systems directly from images. An additional methodological innovation contributing to achieving this third goal is an approximated closed-form solution for efficient integration of network dynamics, which eases efficient training. We tackle (iii) by approximating the forcing-to-input mapping with a decoder that is trained to reconstruct the input based on the encoded latent space force. Finally, we leverage these three properties and show that they enable latent-space control. We use an integral-saturated PID with potential force compensation and demonstrate high-quality performance on a soft robot using raw pixels as the only feedback information. © 2024 Neural information processing systems foundation. All rights

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Intelligent Transmission Scheduling for Edge Sensing in Industrial IoT systems

Intelligent Transmission Scheduling for Edge Sensing in Indu...

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

作者： Jin, Tiankai Ma, Yehan Ji, Zhiduo Chen, Cailian Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9798350310900

Edge sensing supported by wireless transmission is one of the core enabling technologies for flexibly implementing the Industrial internet of Things (iioT). Balancing network resource consumption and sensing accuracy under dynamic network conditions is a critical challenge. In this work, we bridge the gap between edge sensing performance and transmission design through observability analysis and learning-based methods. Particularly, utilizing observability probability as the key metric, we design the network resource reservation for specific sensing performance demands including stability based on our derived upper and lower probability bounds. Then, to further reduce the overall cost of edge sensing and transmission, an intelligent transmission scheduling method (ITSM) based on deep reinforcement learning is provided, which dynamically schedules the number of transmissions for each sensor. In ITSM, the action space is determined according to the amount of our reserved resources, and both the states of sensing error and fading channel are taken into account. Finally, the superiority of our proposed methods is fully demonstrated through numerical simulations in a typical iioT system of industrial hot rolling.

关键词： Industrial internet of Things (iioT) edge sensing observability transmission scheduling deep reinforcement learning

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Extending internet of Things systems with Fog and Edge Computing 30

Extending Internet of Things Systems with Fog and Edge Compu...

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30th IEEE International conference on Engineering, Technology, and Innovation

作者： Dumitru, Marian Cosmin Moisescu, Mihnea Alexandra Pietraru, Radu Nicolae Pop, Eugen Natl Univ Sci & Technol Fac Automat Control & Comp Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9798350362442;9798350362435

In recent years, whether we are talking about industrial implementations or whether we are talking about products offered to the masses, there is an accelerated trend of the appearance of IoT devices, devices that collect information and, depending on their specifics, can make certain decisions in the process. This increase automatically leads to the generation of more generated data that must be sent for processing, analyzed, stored, a certain decision being made based on the result of the analysis. This process can be long, the duration strictly determined by the volume of data and the performance of the Cloud infrastructure. Fog and Edge computing has come to our aid with an innovative solution, acting as an additional layer between IoT and Cloud devices. This layer aims to reduce the response time by analyzing the data at the edge of the network, in this way it is no longer necessary to send all the information directly to the Cloud. Whether we are talking about smart cities, the health field or the industrial fields, the presence of IoT devices shows the usefulness of the need to implement Fog and Edge systems. Starting from this growth in the IoT field, the authors wish through this article to analyze the existing implementations that use Fog and Edge computing, the existing architectural levels, the analysis of the areas that present vulnerabilities, as well as the possible improvements that can be added to make the processes more efficient.

关键词： Fog Computing Edge computing internet of Things (IoT) Cloud

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Queueing Theory Approach for an IoST-Based Space Debris Tracking network System and its performance Evaluation 5

Queueing Theory Approach for an IoST-Based Space Debris Trac...

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5th International conference on IoT Based control networks and Intelligent systems, ICICNIS 2024

作者： Christ, Richard J.T. Lavanya, G. College of Engineering and Technology Department of Mathematics SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9798331518097

As humanity evolves, space exploration is inevitable. There are several limitations for space exploration, notably space debris has also caused the growth of a new challenge for space exploration efforts. Functional satellites and space stations are endangered by collisions with space debris, it shortens their service life and is a threat to future missions. In this paper, a novel satellite network model is presented. The model works via a fleet of low Earth orbit femto-satellites that can detect space debris and they transmit that information back to a larger nanosatellite as the central unit for processing, in both time and data. The nanosatellite would then prepare and transmit the data to ground stations for evaluation and further action, or the nano satellite transmit the information to the nearest satellite, so that the satellite would transfer the data to the ground station forming an IoST (internet of Space Things). This network is constructed and evaluated using queueing principles presumably open Jackson queueing system, through which the network's response time, utilization of nodes etc. are obtained. This article shows how IoST -based satellite networks can help cope with the space debris problem. This capability can help space operators better track and monitor an increasingly crowded Earth's orbit debris, leading to sustained space exploration. © 2024 IEEE.

关键词： Space debris

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A Comparative Analysis of Deep Reinforcement Learning-based xApps in O-RAN

A Comparative Analysis of Deep Reinforcement Learning-based ...

引用

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

作者： Tsampazi, Maria D'Oro, Salvatore Polese, Michele Bonati, Leonardo Poitau, Gwenael Healy, Michael Melodia, Tommaso Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA Dell Technol P&O OCTO Adv Wireless Technol Round Rock TX USA

ISBN: (纸本)9798350310900

The highly heterogeneous ecosystem of Next Generation (NextG) wireless communication systems calls for novel networking paradigms where functionalities and operations can be dynamically and optimally reconfigured in real time to adapt to changing traffic conditions and satisfy stringent and diverse Quality of Service (QoS) demands. Open Radio Access network (RAN) technologies, and specifically those being standardized by the O-RAN Alliance, make it possible to integrate network intelligence into the once monolithic RAN via intelligent applications, namely, xApps and rApps. These applications enable flexible control of the network resources and functionalities, network management, and orchestration through data-driven control loops. Despite recent work demonstrating the effectiveness of Deep Reinforcement Learning (DRL) in controlling O-RAN systems, how to design these solutions in a way that does not create conflicts and unfair resource allocation policies is still an open challenge. In this paper, we perform a comparative analysis where we dissect the impact of different DRL-based xApp designs on network performance. Specifically, we benchmark 12 different xApps that embed DRL agents trained using different reward functions, with different action spaces and with the ability to hierarchically control different network parameters. We prototype and evaluate these xApps on Colosseum, the world's largest O-RAN-compliant wireless network emulator with hardware-in-the-loop. We share the lessons learned and discuss our experimental results, which demonstrate how certain design choices deliver the highest performance while others might result in a competitive behavior between different classes of traffic with similar objectives.

关键词： Open RAN O-RAN Resource Allocation network Intelligence Deep Reinforcement Learning

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Enhancing Intrusion Detection systems in IoT networks: A Hybrid Approach using CNN, ANN, LSTM, GRU for Improved Security 8

Enhancing Intrusion Detection Systems in IoT Networks: A Hyb...

引用

8th International conference on Inventive systems and control, ICISC 202

作者： Pradeep, M. Gopalakrishnan, S. Hindustan Institute of Technology and Science Department of Information Technology TamilNadu Kelambakkam603103 India

ISBN: (纸本)9798350386578

The automation of daily tasks via the internet of Things (IoT) has drastically changed people's lives in the last few years. By connecting different physical devices with various functionalities, this is accomplished. As a result, as IoT networks grow, so does the rate of cyber threats, risking the stability and integrity of data. Numerous intrusion detection systems (IDS s) are currently developed to identify harmful actions based on predefined attacks patterns, thereby safeguarding data against misuse and abnormal attempts. The current IDS have to be improved due to the sharp rise in these types of attacks. The key to enhancing intrusion detection systems is now machining learning. A proposed intrusion detection methodology in this study is hybrid approach. After evaluating the model's performance using a number of popular, advanced methods currently in use, the suggested model uses CNN, ANN, LSTM and GRU model. The model gives better trade -off compared to other approaches. © 2024 IEEE.

关键词： network intrusion

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An Innovative control Approach for Cyber-Physical Transportation systems: The Case of Monte-Carlo Workflow Computations 32

An Innovative Control Approach for Cyber-Physical Transporta...

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32nd Euromicro International conference on Parallel, Distributed and network-Based Processing (PDP)

作者： Kashansky, Vladislav Kashani, Sara Agha Hossein Garcia-Blas, Javier Marozzo, Fabrizio Hai Zhuge Sun, Xiaoping Eteronix GmbH Res Lab Vienna Austria Johannes Kepler Univ Linz Inst Prod & Logist Management Sch Business Linz Austria Univ Carlos III Madrid ARCOS Grp Leganes Spain Univ Calabria DIMES Dept Arcavacata Di Rende Italy Inst Comp Technol ICTCAS Key Lab Intelligent Informat Proc Beijing Peoples R China

ISBN: (纸本)9798350363074;9798350363081

Contemporarily, in light of the intelligent transportation systems (ITS) sector, the tendency can be observed that the solution of the multi-objective cyber-physical optimization problems with imperfect information takes an increasingly weighted role. In the present scientific work, the authors want to take these developments into account by introducing an innovative cyber-physical architectural design and corresponding the two-stage heuristic computing approach. It is utilized in synergy with the MCSA1 and DCEx architectural principles for the workflow scheduling of Monte-Carlo simulation, which is based on the intelligent and sustainable route-order dispatching process model. Factors such as emissions, transport costs, risks, and the individual weighting of orders are reflected in the model. In particular, the authors define a stochastic ILP-based2 monte-carlo workflow model. They further propose two-stage scheduling heuristic with d-HEFT DAG relaxation as first stage and apply state-of-the-art techniques as a part of SCIP framework to solve 2nd 1-0 ILP-based stage;evaluate the performance of the scheduling approach. The authors obtain preliminary results of the second stage behavior using a realistic heterogeneous computing scenario and corresponding constraint structures within MACS simulator engine3. The results from the experiments illustrate moderate complexity of the approach. Scalability of the model looks promising for the applicability in various industry-related scenarios and corresponding computing environments.

关键词： Cyber-Physical Transportation systems Monte-Carlo Workflows Scheduling Discrete Optimization Sustainable Transportation Dispatching systems

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A Novel Elm Optimization and Its Application in IoT Intrusion Detection 5th

A Novel Elm Optimization and Its Application in IoT Intrusio...

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5th International conference on Neural Computing for Advanced Applications (NCAA)

作者： Maseno, Elijah M. Wang, Zenghui Univ South Africa Dept Comp Sci Florida South Africa Univ South Africa Dept Elect & Smart Syst Engn ZA-1709 Florida South Africa

ISBN: (纸本)9789819770038;9789819770045

Globally, the internet of Things (IoT) has grown exponentially in the last few years. The integration of numerous IoT devices enhances connectivity but also exposes them to various cyber-attacks. With the increase of complex and intelligent cyber-attacks, the traditional methods of cyber-attack detection, such as anomaly and misuse intrusion detection systems (IDS), have proven ineffective in combating emerging cyber-attacks. Scholars have suggested that the existing IDS's performance can be enhanced by integrating machine learning methods. This study explored the integration of ELM (Extreme Learning Machine) with NSGA-ii (Non-Dominated Sorting Genetic Algorithm ii). Our empirical analysis reveals that ELM outperforms other algorithms regarding learning rate, usability, and effectiveness of generalization. NSGA-ii provides an equilibrium between exploration and exploitation, successfully managing the trade-offs present in multi-objective optimization problems to identify a range of excellent solutions along the Pareto front. Most of the existing works on ELM optimization focuses on improving one input parameter. This study explored the integration of ELM with NSGA-ii for both input weights and hidden neurons. The model was trained and tested using two datasets namely IoT_ToN network and UNSWNB15 datasets. This research used TON_IoT Windows dataset to further test the capacity of the model to identify novel attacks. Using the UNSWNB15 and IoT_ToN network datasets, respectively, the model's accuracy was 0.68 and 0.65. In addition, the model recorded an accuracy 0.768 using TON_IoTWindows dataset, demonstrating the potential of the model in detection of new forms of attacks. This study acknowledges the ongoing evolution of challenges in IoT security and the need for continuous innovation to stay ahead of emerging threats in the future. The contributions of this study can be explored further to develop reliable intrusion detection solutions for the dynamic IoT landscape

关键词： Artificial Intelligence (AI) internet of Things (IoT) Cyber-Attack Intrusion Detection System (IDS) Evolutionary Algorithm Optimization Machine Learning

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