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Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

computers, Materials & Continua 2024年第11期81卷 2357-2394页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Razaz Waheeb Attar Farman Ali Ahmed Alhomoud Babar Shah School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science College of ScienceNorthern Border UniversityArar91431Saudi Arabia College of Technological Innovation Zayed UniversityDubai19282United Arab Emirates

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control *** these networks,nodes function as senders,receivers,and *** such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely *** the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power *** optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in *** paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in *** energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient *** link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of *** simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing *** evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

关键词： FANET UAV internet of UAVs relay mechanism energy consumption routing IEE-DLSA

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SEMU5G: an Emulator for Improving Model Accuracy and Performance Evaluation of 5G Network Core Architectures based on SDN 10

SEMU5G: an Emulator for Improving Model Accuracy and Perform...

引用

10th International Conference on Wireless and Telematics, ICWT 2024

作者： Zaki Hamidi, Eki Ahmad Lazuardi, Arga Valen Rahmansyah, Muhammad Litfan Novel Bajri, Salman Alfarizi Tulloh, Rohmat Negara, Ridha Muldina Ahdan, Syaiful Jupriyadi UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia Telkom University Telecommunication Technology School of Applied Science Bandung Indonesia The University Center of Excellence Intelligent Sensing-IoT Telkom University Indonesia Telkom University School of Electrical Engineering Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia

ISBN: (纸本)9798350377057

The rapid advancement of 5G Radio Access Network (RAN) architecture is facilitating the construction of 5G networks, marking a significant milestone in telecommunications evolution. Given the complexity of the 5G core architecture, traditional simulation methods are insufficient, necessitating novel approaches. Emulator systems are crucial for creating dynamic, controlled environments that enable exploration of real-world scenarios without physical constraints. SEMU5G, a 5G core emulator, SDN controller, and RAN simulator, utilizes Open5GS implemented in Docker Container for test system flexibility and isolation. Additionally, an SDN controller is integrated to monitor data flows in the User Plane Function (UPF) and gNB simulated by UERANSIM in Mininet-WiFi. This comprehensive integration facilitates effective and flexible real-world exploration, providing a dynamic and controlled test environment for 5G core research. Scenario testing comprises two stages: firstly, a fixed network topology is employed to compare resource usage and confirm successful SEMU5G integration. Secondly, a mobile network topology is utilized to implement a mobile device scenario and compare the Quality of Service (QoS) of SEMU5G with other available emulators. These stages ensure thorough evaluation of SEMU5G's performance and its comparative advantage over existing solutions. © 2024 IEEE.

关键词： Wi-Fi

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A Pragmatical Approach to Anomaly Detection Evaluation in Edge Cloud Systems

A Pragmatical Approach to Anomaly Detection Evaluation in Ed...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Sotiris Skaperas Georgios Koukis Ioanna Angeliki Kapetanidou Vassilis Tsaoussidis Lefteris Mamatas Department of Applied Informatics University of Macedonia Thessaloniki Greece Athena Research and Innovation Center Greece Department of Electrical and Computer Engineering Democritus University of Thrace Greece

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Anomaly detection (AD) has been recently employed in the context of edge cloud computing, e.g., for intrusion detection and identification of performance issues. However, state-of-the-art anomaly detection procedures do not systematically consider restrictions and performance requirements inherent to the edge, such as system responsiveness and resource consumption. In this paper, we attempt to investigate the performance of change-point based detectors, i.e., a class of lightweight and accurate AD methods, in relation to the requirements of edge cloud systems. Firstly, we review the theoretical properties of two major categories of change point approaches, i.e., Bayesian and cumulative sum (CUSUM), also discussing their suitability for edge systems. Secondly, we introduce a novel experimental methodology and apply it over two distinct edge cloud test-beds to evaluate the performance of such mechanisms in real-world edge environments. Our experimental results provide important insights and trade-offs for the applicability and the online performance of the selected change point detectors.

关键词： Measurement Cloud computing Reviews Image edge detection Conferences Memory management Intrusion detection

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Analyzing darknet traffic through machine learning and neucube spiking neural networks

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Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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Novel Noise Filter Techniques and Dynamic Ensemble Selection for Classification

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Recent Advances in computer Science and Communications 2022年第1期15卷 48-59页

作者： Nguyen, Thi Ngoc A. Nhu, Quynh P. Solanki, Vijender K. School of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam CMC Institute of Science and Technology 11 Duy Tan Cau Giay Hanoi Viet Nam Department of Computer Science and Engineering CMR Institute of Technology TS Hyderabad India

Background: Ensemble selection is one of the most researched topics for ensemble learning. Researchers have been attracted to selecting a subset of base classifiers that may perform more helpful than the whole ensemble system classifiers. Dynamic Ensemble Selection (DES) is one of the most effective techniques in classification problems. DES systems select the most appropriate classifiers from the candidate classifier pool. Ensemble models that balance diversity and accuracy in the training process improve performance than the whole classifiers. Objective: In this paper, novel techniques are proposed by combining Noise Filter (NF) and Dynamic Ensemble System (DES) to have better predictive accuracy. In other words, a noise filter and DES make the data cleaner and DES improves the performance of classification. Methods: The proposed NF-DES model, which was demonstrated on twelve datasets, especially has three credit scoring datasets and a performance measure accuracy. Results: The results show that our proposed model is better than other models. Conclusion: The novel noise filer and dynamic ensemble learning with the aim to improve the classification ability are presented. To improve the performance of classification, noise filter with dynamic ensemble learning makes the noise data toward the correct class. Then, novel dynamic ensemble learning chooses the appropriate subset classifiers in the pool of base classifiers. © 2022 Bentham Science Publishers.

关键词： Support vector machines

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A Pragmatical Approach to Anomaly Detection Evaluation in Edge Cloud Systems

arXiv

引用

arXiv 2024年

作者： Skaperas, Sotiris Koukis, Georgios Kapetanidou, Ioanna Angeliki Tsaoussidis, Vassilis Mamatas, Lefteris Department of Applied Informatics University of Macedonia Thessaloniki Greece Athena Research and Innovation Center Greece Department of Electrical and Computer Engineering Democritus University of Thrace Greece

Anomaly detection (AD) has been recently employed in the context of edge cloud computing, e.g., for intrusion detection and identification of performance issues. However, state-of-the-art anomaly detection procedures do not systematically consider restrictions and performance requirements inherent to the edge, such as system responsiveness and resource consumption. In this paper, we attempt to investigate the performance of change-point based detectors, i.e., a class of lightweight and accurate AD methods, in relation to the requirements of edge cloud systems. Firstly, we review the theoretical properties of two major categories of change point approaches, i.e., Bayesian and cumulative sum (CUSUM), also discussing their suitability for edge systems. Secondly, we introduce a novel experimental methodology and apply it over two distinct edge cloud test-beds to evaluate the performance of such mechanisms in real-world edge environments. Our experimental results reveal important insights and trade-offs for the applicability and the online performance of the selected change point detectors. © 2024, CC BY.

关键词： Anomaly detection

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Improving Norwegian Power Transmission Flexibility Using a New Matheuristic Optimization Framework

Improving Norwegian Power Transmission Flexibility Using a N...

引用

Clean and Green Energy engineering (CGEE), International Conference on

作者： Sander Meland Mojtaba Yousefi Ahmad Hemmati Troels Bojesen Department of Informatics University of Bergen & Equinor Stavanger Norway Department of Computer Science Electrical Engineering & Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Informatics University of Bergen Bergen Norway

ISBN: (数字)9798350377491

ISBN: (纸本)9798350377507

There is an increasing demand for transmission system operators to expand their participation in the electricity reserve markets, encompassing smaller businesses and house-holds that are currently excluded due to market constraints. These smaller entities require an aggregator's assistance to pool the loads of several minor actors, enabling their participation in the markets through an aggregator that can optimize bids. This paper introduces a matheuristic framework aimed at maximizing revenue in the Norwegian power reserve markets, showcasing the potential effectiveness of aggregators. The framework is utilized to analyze the optimal bidding strategies for an aggregator within the Norwegian Reserve Markets over two distinct time periods, using real market prices, flexible power capacity, and customer consumption data. The heuristics are evaluated and the outcomes are compared with those of a mathematical model. The matheuristic models achieved results ranging from 88–94 % relative to the corresponding mathematical model.

关键词： Green energy Electricity Power transmission Mathematical models Distance measurement Optimization Load modeling Business

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Hybrid Models for Recognizing Indonesian Textual Entailment 7

Hybrid Models for Recognizing Indonesian Textual Entailment

引用

7th International Conference on informatics and Computational Sciences, ICICoS 2024

作者： Ahmadian, Hendri Abidin, Taufik Fuadi Riza, Hammam Muchtar, Kahlil Universitas Syiah Kuala Graduate School of Mathematics and Applied Sciences Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia National Research and Innovation Agency Research Center for Artificial Intelligence and Cyber Security Jakarta Indonesia Universitas Syiah Kuala Faculty of Engineering Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9798350375886

A paragraph raises pertinent issues about linguistic disparities in understanding the semantic relationships between sentences within the natural language processing domain (NLP) Addressing this challenge necessitates the development of robust NLP models, with a specific emphasis on text entailment. This research endeavors to enhance the performance of Indonesian NLP benchmarks in text entailment by harnessing a variant of the Bidirectional Encoder Representations from Transformer (BERT) model tailored explicitly for the Indonesian language, known as IndoBERT. The approach in this study adopts a hybrid methodology, amalgamating the IndoBERT layer modification model by summing the last four hidden layers with neural networks. The performance evaluation of the hybrid models, combining the IndoBERT layer modification model with the Bidirectional Gated Recurrent Unit (BiGRU) model and the IndoBERT layer modification model with the Bidirectional Long Short-Term Memory (BiLSTM) model, yields the F1-scores of 0.87 and 0.84, respectively. © 2024 IEEE.

关键词： Semantics

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Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

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computers, Materials & Continua 2022年第2期70卷 2761-2775页

作者： Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Faculty of Engineering Port Said UniversityPort Fuad CityEgypt

In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.

关键词： Video surveillance weapon detection you only look once convolutional neural networks

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Privacy-Preserving Utilization of Distribution System Flexibility for Enhanced TSO-DSO Interoperability: A Novel Machine Learning-Based Optimal Power Flow Approach

arXiv

引用

arXiv 2025年

作者： Dindar, Burak Saner, Can Berk Çakmak, Hüseyin K. Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Electrical and Computer Engineering National University of Singapore Singapore117581 Singapore

Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs)—in our case, distributed generators (DGs)-in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on nonsensitive data, the transfer of sensitive information between stakeholders is prevented. Consequently, even if reverse engineering is applied to these ML models, no sensitive data can be extracted. This allows

关键词： Reverse engineering

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