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Dynamic TSN Reconfiguration for Time-Triggered Organic computing 16

Dynamic TSN Reconfiguration for Time-Triggered Organic Compu...

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Qosja, Mario Raj, Utkarsh Meckel, Simon Obermaisser, Roman University of Siegen Adolf-Reichwein-Straße 2a Siegen 57076 Germany

In response to the increasing complexity of distributed embedded systems, self-organizing systems have been developed and studied extensively. One such system, Organic computing (OC), draws inspiration from biological organisms to enhance the adaptability and robustness. However, this approach poses challenges related to determinism and dependability, as Organic computing approaches lack deterministic communication mechanisms, making them unsuitable for safety-critical applications. To address these limitations, we introduce Time-Triggered Organic computing (TTOC), integrating time-triggered communication concepts to ensure predictable task execution and message exchange. In this architecture, task scheduling and message communication follows a predefined table schedule generated by a list scheduler. A key step toward real-world implementation of TTOC is simulating message communication using a time-triggered protocol such as Time-Sensitive networking (TSN). To achieve this, we leverage the INET framework of OMNeT++, which provides a discrete-event network simulation. Our approach allows for dynamic reconfiguration of TSN based on the communication schedules generated by TTOC. We evaluate the proposed method through a co-simulation of TTOC and TSN in OMNeT++, demonstrating its effectiveness in dynamic reconfiguration. This work contributes toward integrating self-organizing principles with time-deterministic networking, bridging the gap between adaptability and reliability in embedded systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： Organic computing Time-Sensitive networking Time-Triggered TSN

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international conference on computing, networking and Communications (ICNC)

作者： Mohamad Khattar Awad Ghazal Alsholi Haniah Altabaa Dania Hani Abu Daqar Shahad Alshaher Hamed M.K. Alazemi Computer Engineering Department Kuwait University Alshadadiyah Kuwait

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

The emergence of software-defined networks (SDN) has made the network flexible, programmable, and responsive to change. However, the architectural characteristics of SDN make the network vulnerable to various security attacks. distributed denial of service (DDoS) is one such attack that aims to exhaust the network’s computational and bandwidth resources, blocking legitimate users’ access. This paper aims to study the effectiveness of using the self-similarity property, measured by the Hurst-exponent, in detecting DDoS attacks on the SDN controller. Simulation results have shown that normal and attack traffic flows captured at the controller have different degrees of self-similarity. Specifically, attack traffic flows show high levels of self-similarity, whereas normal traffic flows show lower degrees. Experiments demonstrated an average of 35% difference between the Hurst-exponent values of normal and attack traffic flows obtained, making it effective in classifying traffic flows and detecting attacks.

关键词： Simulation Prevention and mitigation Bandwidth Denial-of-service attack Security Computer crime Software defined networking

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A Review on Detection and Mitigation Strategies of DDoS Attack in SDN Environment using Machine Learning and Deep Learning

A Review on Detection and Mitigation Strategies of DDoS Atta...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Maram Smriti Boruah Satyajit Sarmah Department of Information Technology Gauhati University Assam India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Software-Defined networking (SDN) has a unique architecture, however it is highly susceptible to distributed Denial-Of-Service (DDoS) attacks targeting its control and data planes. This paper reviews the use of Machine Learning (ML) and Deep Learning (DL) techniques for the detection and mitigation of DDoS attacks in Software-Defined networking (SDN). The review examines research published between 2019 and 2024, highlighting various approaches, such as Kernel Principal Component Analysis (KPCA), Random Forest (RF), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), among others. These techniques are evaluated based on their effectiveness in detecting high-rate, low-rate, and zero-day attacks, with the focus on accuracy, false positive rates, and real-time adaptability. While several methods show promising results in experimental environments, challenges remain in terms of scalability, generalization, and dynamic adaptation to evolving attack patterns in large-scale deployments.

关键词： Deep learning Reviews Prevention and mitigation Scalability Denial-of-service attack Real-time systems Convolutional neural networks Software defined networking Long short term memory Random forests

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Ensemble Learning based WiFi Sensing using Spatially distributed TX-RX Links

Ensemble Learning based WiFi Sensing using Spatially Distrib...

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international conference on computing, networking and Communications (ICNC)

作者： Nafeez Fahad Md Touhiduzzaman Eyuphan Bulut Department of Computer Science Virginia Commonwealth University VA USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Utilizing fine grained analysis of wireless signals for human activity recognition has gained a lot of traction recently. The unique changes made on the ambient wireless signals by different activities made it possible to recognize these fingerprints through deep learning classification methods. However, most existing approaches fail to fully leverage the rich information available from multiple transmitter-receiver (TX-RX) pairs in a given environment. This study proposes an aggregated weighted ensemble learning method that benefits from spatially distributed TX-RX links to enhance WiFi sensing based activity recognition performance. Our approach utilizes Channel State Information (CSI) data collected from multiple angles and viewpoints, made possible by strategically placed TX-RX pairs, and integrates them through the proposed method. This allows our system to benefit from the complementary strengths of each TX-RX pair, capturing a wider range of signal propagation patterns and environmental factors. We provide an experimental evaluation using datasets of human limb activities collected from six different TX-RX pairs. The results show that the proposed model can achieve a much higher accuracy (i.e., 90%) than the base models and the model that is trained on the combined dataset thanks to the integrated weighted ensemble learning technique and the data from multiple TX-RX pairs.

关键词： Wireless communication Wireless sensor networks Limbs Accuracy Fingerprint recognition Data models Sensors Ensemble learning Human activity recognition Wireless fidelity

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Edge Based Smart Energy Management System Using Object Detection

Edge Based Smart Energy Management System Using Object Detec...

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international conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Bennish, M. Saravanan, M. Department of Networking and Communications SRM Institute of Science and Technology Chennai Kattankulathur India

ISBN: (纸本)9789819795062

With rising demand for energy efficiency and sustainability, advanced technologies to optimize energy consumption in various domains are in high demand. This paper proposes an Edge-based Smart Energy Management System that intelligently monitors and controls energy usage in real-time using object detection techniques. This system employs a distributed architecture to efficiently manage energy consumption by combining the power of edge computing and computer vision. Smart computer vision devices are deployed at the edge to collect real-time data from the environment. These devices use YOLOv8 object detection algorithms to identify and track energy-intensive objects such as appliances like lights, and fans. The detected objects information is then processed and analyzed at the edge devices, which generate the corresponding threshold value for managing the electrical appliances. These processed data will be sent to the cloud infrastructure for further analysis and decision-making. Through the integration of object detection and YOLOv8 machine learning techniques, this system offers several key benefits. Firstly, it enables real-time monitoring of energy consumption at a granular level, allowing users to identify the number of persons and take appropriate actions to reduce electricity consumption. Secondly, the system provides accurate energy forecasting, enabling proactive load management and efficient allocation of energy resources. The proposed system will implement the computer vision in real-time monitoring and evaluation to assess its performance, scalability, and energy-saving potential. The evaluation results will demonstrate the proposed system’s effectiveness in optimizing energy usage, reducing costs, and contributing to a more sustainable environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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Customizable DDoS Attack Data Generation in SDN Environments for Enhanced Machine Learning Detection Models

Customizable DDoS Attack Data Generation in SDN Environments...

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Advanced Research in computing (ICARC), international conference on

作者： Nadeera Gayantha Chathura Rajapakse Janaka Senanayake Dept. of Industrial Management University of Kelaniya Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen United Kingdom

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

distributed Denial of Service (DDoS) attacks are a critical threat to the security and reliability of Software-Defined networking (SDN) environments. Existing datasets for training machine learning (ML) models, such as KDDCup ’99 and CICIDS 2017, are either outdated or fail to capture SDN-specific characteristics, limiting their effectiveness in detecting modern DDoS attacks. This paper proposes a framework for generating a comprehensive, SDN-specific dataset using a virtual environment that integrates Mininet, the Ryu controller, and Python-based automation. The dataset incorporates advanced flow-level metrics, including SYN counts, queue lengths, and real-time traffic dynamics, reflecting contemporary attack scenarios such as ICMP floods, TCP SYN floods, and UDP floods. By addressing the limitations of traditional datasets, this custom dataset enhances ML model training for DDoS detection in SDN environments, providing improved accuracy and adaptability. Contributions include a scalable SDN-based dataset generation framework, enriched feature sets for ML training, and a comprehensive approach to capturing both legitimate and malicious traffic dynamics. This study highlights the potential of SDN programmability in advancing security research and offers a robust tool for the development of reliable DDoS detection mechanisms.

关键词： Training Adaptation models Accuracy Virtual environments Denial-of-service attack Vectors Robustness Real-time systems Computer crime Software defined networking

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On Developing Smart IoT-Based distributed System for Remote Detection of Slope Movements

On Developing Smart IoT-Based Distributed System for Remote ...

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international conference on computing, networking and Communications (ICNC)

作者： Rahul Debnath Ahmed Abdelmoamen Ahmed Md Jobair Bin Alam Department of Computer Science Prairie View A&M University Prairie View TX USA Department of Civil Engineering Prairie View A&M University Prairie View TX USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Landslides or slope failures cause significant damage to the transportation infrastructure worldwide, resulting in billions of dollars of property damage. The initial slope movement of highway slopes, caused by environmental factors and man-made activities, must be detected early to prevent significant losses in infrastructure damages and human lives. However, traditional monitoring approaches for slope movement, such as periodic human inspection and surveying, are labor-intensive and time-consuming. With the advent of the Internet of Things (IoT) and high-speed internet, the potential to use such technologies for monitoring remote earth movement of highway embankments is becoming increasingly important. This paper presents an IoT-based distributed system for the early detection of slope movements to assist transportation authorities in immediately repairing the slope before a major failure occurs. We built a laboratory prototype embankment, equipped with various IoT sensors (e.g., tilt, moisture, and tensiometer sensors), that simulate a failure-prone slope to evaluate the effectiveness of our system. A user-friendly GUI is developed to enable users to utilize the system remotely. The collected sensory data for four months allowed us to analyze the soil hydrologic dynamics and the slope performance by detecting changes in the slope tilting both parallel (pitch) and perpendicular (roll). The experimental results showed that our system could detect negligible movement (1mm) and rotation (0.02°) of slopes.

关键词： Road transportation Working environment noise Transportation Artificial neural networks Sensor systems Vectors Internet of Things Remote monitoring Intelligent sensors Graphical user interfaces

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Fed-MoE: Efficient Federated Learning for Mixture-of-Experts Models via Empirical Pruning 25th

Fed-MoE: Efficient Federated Learning for Mixture-of-Expert...

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25th international conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Zou, Yifei Qi, Senmao Yuan, Yuan Wang, Dawei Shen, Shikun Wu, Libing Guo, Shaoyong Yu, Dongxiao School of Computer Science and Technology Shandong University Qingdao China School of Cyber Science and Engineering Wuhan University Wuhan China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819642069

In recent years, the combination of Federated Learning (FL) framework and Mixture of Experts (MoE) architecture has shown promise for large-scale model pre-training while preserving data privacy. However, the significant computational and communication requirement of MoE-based models presents substantial challenges for FL systems. To address this, we propose Fed-MoE, an efficient federated learning framework designed for MoE models through empirical pruning. Fed-MoE reduces computational overhead by pruning multiple experts at the client side while retaining general components, thus converting the model into dense sub-models. These sub-models are locally trained and later aggregated by the parameter server into a sparse MoE model. Our method optimizes the use of sparse activation in MoE models and reduces resource consumption without compromising performance. Extensive experimental results demonstrate that Fed-MoE significantly improves the efficiency of FL without compromising model performance, offering an effective solution for deploying large-scale MoE models in federated learning environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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A Bayesian Belief Network Framework for Protecting Generative AI-driven Attack in Smart Grid Communication

A Bayesian Belief Network Framework for Protecting Generativ...

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international conference on computing, networking and Communications (ICNC)

作者： Md Shirajum Munir Sravanthi Proddatoori Manjushree Muralidhara Sachin Shetty School of Computing Analytics and Modeling Univesity of West Georgia Carrollton GA USA Dept. of Computer Science Old Dominion University Norfolk VA USA Dept. of ECE Old Dominion University Norfolk VA USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

In the era of artificial intelligence (AI), native generative AI (GenAI) has become prominent in generating intelligent cyber-attacks in a smart grid environment by imitating operational parameters and metrics. As a result, it is imperative to understand the attack generation capabilities of native GenAI models, such as Generative Adversarial Networks (GANs) and Autoencoders in a smart grid environment. This research aims to explore the potential of GenAI models to accurately imitate and analyze the behavior of cyberattacks within a smart grid environment. The goal is to understand the risks posed by these intelligent attacks while developing strategies to protect against disruptive distributed Energy Resources (DER) events, such as incorrect load shifting, imbalanced demand supply, and unstable price forecasting. First, this work leverages two customized native GenAI models, namely GAN and Autoencoder, to synthesize DERs control message parameters such as nominal power consumption, price elasticity coefficients, and communication data packets, among others, to introduce highly unpredictable new intelligent attack vectors. Second, this research proposes and develops a new Bayesian Belief Networks (BBNs) framework by creating correlation dependency nodes among DER control message parameters to understand the risks of intelligent attack vectors better while monitoring and mitigating their impact on smart grid operations. In particular, by leveraging the customized BBN, the system can observe and analyze the uncertain behaviors in DER operations that GenAI introduces, and the proposed framework can help mitigate grid vulnerabilities by understanding the poisonous parameters through mutual information. Finally, the experiment results show that the Autoencoder outperformed GAN in reproducing intelligent attacks with an MSE about 98.984% lower than that of GANs. Additionally, BBN can explain prominent parameters of the intelligent attacks’ by quantifying dependencies thro

关键词： Generative AI Autoencoders Generative adversarial networks Vectors Data models Smart grids distributed power generation Bayes methods Mutual information Load modeling

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A Multi-Level Parallel Algorithm for Detection of Single Scatterers in SAR Tomography

A Multi-Level Parallel Algorithm for Detection of Single Sca...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Massimiliano Russo Mehwish Nisar Antonio Pauciullo Pasquale Imperatore Marco Lapegna Diego Romano Dept. of Mathematics and Applications University of Naples Federico II Naples Italy Institute for the Electromagnetic Sensing of the Environment (IREA) National Research Council Naples Italy Inst. for High Performance Computing and Networking National Research Council Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth’s surface. However, the computational complexity of SAR tomography algorithms often restricts their application to large-scale datasets. To address this issue, we introduce a multi-level parallel implementation of a single scatterer detection algorithm specifically designed to exploit the capabilities of modern heterogeneous High-Performance computing (HPC) systems. By efficiently distributing the computational workload at different levels across multiple processing units, our parallel approach significantly reduces processing time, facilitating the analysis of extensive SAR datasets. We assess the performance of our parallel implementation using real-world SAR data, showcasing its effectiveness in enhancing both the efficiency and scalability of SAR tomography. Our work contributes to advancing remote sensing techniques and offers valuable insights into the application of HPC for large-scale environmental monitoring.

关键词： High performance computing Scalability Graphics processing units Tomography Performance analysis Parallel algorithms Synthetic aperture radar Surface treatment Remote sensing Optimization

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