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Multi-level Aggregation Heterogeneous Graph Attention network 43

Multi-level Aggregation Heterogeneous Graph Attention Networ...

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43rd Chinese control conference, CCC 2024

作者： Zhao, Chenting Li, Xin Lv, Sai Li, Qingsong Kang, Bin Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210003 China

ISBN: (纸本)9789887581581

In recent years, heterogeneous graph neural networks have garnered increasing attention due to their wide range of real-world applications. However, most existing models rely solely on the information from one meta-path while disregarding the information from other meta-paths or overlooking the significance of intermediate nodes within the meta-path, which play a crucial role in understanding the overall graph structure. To address this issue, we propose an innovative multi-level aggregated heterogeneous graph attention network (MAHGAN) that aims to more effectively integrate information from different levels. Firstly, we utilizes the self-attention mechanism to aggregate target nodes based on various meta-paths and thereby integrate information from their homogeneous neighboring nodes. Secondly, in order to better incorporate diverse information between different types of nodes, we aggregate node features of heterogeneous neighbors (first-order neighbors) for each target node in the original graph. This generates an embedding vector for each target node which is then converged with the node features obtained in the first step. Next, we combine these aggregated embedding vectors under different meta-paths according to learned weights to form a final embedding representation. Finally, through extensive experiments conducted on three datasets, it has been demonstrated that our model exhibits superior performance in node classification tasks. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Graph neural networks

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Energy Consumption of Multi-Radio Access Technology internet of Things Gateway for UAV-Based Communication Infrastructure 3

Energy Consumption of Multi-Radio Access Technology Internet...

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3rd International conference on 6G networking, 6GNet 2024

作者： Haseeb, Abdul Hanninen, Tuomo Mikhaylov, Konstantin Centre for Wireless Communications University of Oulu Oulu Finland

ISBN: (纸本)9798350378597

Unmanned Aerial Vehicles (UAVs) are essential enablers for future sustainable industrial and smart city applications. This makes UAV's autonomy based on energy-efficient operation a crucial research and engineering challenge. To address this challenge, in this paper, we advocate and experimentally study the design and energy performance of a multi-radio access technology (multi-RAT) connectivity system for UAVs. Specifically, we discuss the architecture alternatives and tradeoffs of multi-RAT gateway first. Next, we design and instrument a prototype of a multi-RAT communication gateway by integrating three selected illustrative RATs, namely (i) Fifth Generation (5G) cellular (enabling UAV to operate as 5G User Equipment (UE)), (ii) Long Range Wide Area network (LoRaWAN) (gateway to receive data from other internet of Things (IoT) devices) and, optionally, as the end node, (iii) Bluetooth Low Energy (BLE). Finally, we experimentally measure the energy consumption for each RAT transceiver separately and of all transceivers altogether. The presented results provide important insight into the design of multi-RAT communication systems for UAVs, their practical usage and the potential for further optimization. © 2024 IEEE.

关键词： Energy utilization

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International conference on Artificial Intelligence: Theory and Applications, AITA 2023

International Conference on Artificial Intelligence: Theory ...

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International conference on Artificial Intelligence: Theory and Applications, AITA 2023

ISBN: (纸本)9789819984756

The proceedings contain 38 papers. The special focus in this conference is on Artificial Intelligence: Theory and Applications. The topics include: Using Historical Trip Information to Determine the Waiting Time Required for Taxi Services;the Impact of Cesarean Section Trends and Associated Complications in the Current World: A Comprehensive Analysis Using Machine Learning Techniques;Novel Hybrid Methods for Journal Article Summarization Combining Graph Method and Rough Set TFIDF Method with Pegasus Model;differential Evolution Wrapper-Based Feature Selection Method for Stroke Prediction;parkinson’s Disease Identification from Speech Signals Using Machine Learning Models;performance Analysis of Deep CNN, YOLO, and LeNet for Handwritten Digit Classification;Data-Driven Decision Support systems in E-Governance: Leveraging AI for Policymaking;the Infrastructure Development of Contemporary Medical Devices Based on internet of Things Technology;business Intelligence System Adoption Project in the Area of Investments in Financial Assets;identifying Fake Twitter Trends with Deep Learning;feature Selection Techniques to Enhance Prediction of Clinical Appointment No-Shows Using Neural network;a Simple Recommendation Model Using the Item’s Global Popularity and Frequency-Based User Preference;An Early Detection of Autism Spectrum Disorder Using PDNN and ABIDE I&ii Dataset;Comparison of Machine Learning-Based Intrusion Detection systems Using UNSW-NB15 Dataset;comparative Analysis of Face Recognition Models for Criminal Detection;transcription of Ancient Indian Manuscripts Through Artificial Intelligence—Current Status of Technology and the Way Forward;computer Vision and Convolutional Neural network for Dense Crowd Count Detection;Evaluation Methods of CDIO Project at Duy Tan University;deciphering Stem Cell Pluripotency Using a Machine Learning Clustering Approach;extremal Trees of the Reformulated and the Entire Zagreb Indices.

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17th International conference on Risks and Security of internet and systems, CRiSIS 2022

17th International Conference on Risks and Security of Inter...

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17th International conference on Risks and Security of internet and systems, CRiSIS 2022

ISBN: (纸本)9783031311079

The proceedings contain 18 papers. The special focus in this conference is on Risks and Security of internet and systems. The topics include: Security Analysis: From Model to System Analysis;modeling Train systems: From High-Level Architecture Graphical Models to Formal Specifications;How IT Infrastructures Break: Better Modeling for Better Risk Management;ioT Security Within Small and Medium-Sized Manufacturing Companies;an Incentive Mechanism for Managing Obligation Delegation;virtual Private network Blockchain-based Dynamic Access control Solution for Inter-organisational Large Scale IoT networks;pseudonym Swapping with Secure Accumulators and Double Diffie-Hellman Rounds in Cooperative Intelligent Transport systems;benchmark performance of the Multivariate Polynomial Public Key Encapsulation Mechanism;Errors in the CICIDS2017 Dataset and the Significant Differences in Detection performances It Makes;A Comparative Study of Attribute Selection Algorithms on Intrusion Detection System in UAVs: A Case Study of UKM-IDS20 Dataset;PRIAH: Private Alerts in Healthcare;Tool Paper - SEMA: Symbolic Execution Toolchain for Malware Analysis;blockchain Survey for Security and Privacy in the e-Health Ecosystem;towards a Dynamic Testing Approach for Checking the Correctness of Ethereum Smart Contracts;blockchain Olive Oil Supply Chain;Impact of EIP-1559 on Transactions in the Ethereum Blockchain and Its Rollups.

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AI Meets Cyber Defense: Enhancing network Security with GAN-Driven NIDS 11

AI Meets Cyber Defense: Enhancing Network Security with GAN-...

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11th IEEE International conference on Advances in Computing and Communications, ICACC 2024

作者： Sarika, S.P. Paul, Arya Kumar, Ajay George, Anju Adi Shankara Institute of Engineering & Technology Electronics & Communication Engineering Ernakulam India

ISBN: (纸本)9798350379136

The field of cybersecurity has witnessed a growth in the breadth and intricacy of cyber threats due to the advancement of communication technology, which calls for the implementation of strong network intrusion detection systems (NIDS). Regrettably, a wide range of potential hazards might put computer users at risk when using the internet. Users stand to lose a great deal, including control over machines, private information, and potentially even identity theft. The ongoing problem of inconsistent and changing threat data persists despite notable improvements in NIDS models. As more and more research has realized the need for improved detection skills, artificial intelligence (AI) has become a viable answer. In order to meet the challenges presented by emerging cyber threats, this study presents a thorough investigation into the integration of AI, specifically generative adversarial networks (GANs), into NIDS. GANs are a machine learning breakthrough that provides a fresh method for creating new data instances that closely mimic real data. This skill is especially useful in the context of network intrusion detection systems (NIDS), where identifying anomalies necessitates a sophisticated comprehension of intricate and dynamic threat patterns. Our study expands on earlier research in the field that mostly concentrated on deep learning methods like autoencoders, convolutional neural networks (CNN), and long short-term neural networks (LSTM), as well as conventional machine learning models like decision trees (DT) and support vector machines (SVM). Although these methods have shown potential, there hasn't been much of a practical use for them in actual NIDS systems. A promising approach to improving NIDS performance is provided by the suggested NIDS makes use of GANs, especially when it comes to anomaly detection. Through the creation of artificial data instances that closely resemble real-world threats, GANs can enhance the resilience and flexibility of NIDS in dynami

关键词： network intrusion

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can-sleuth: Investigating and Evaluating Automotive Intrusion Detection Datasets 24

can-sleuth: Investigating and Evaluating Automotive Intrusio...

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European Interdisciplinary Cybersecurity conference (EICC)

作者： Kidmose, Brooke Meng, Weizhi Tech Univ Denmark Kongens Lyngby Denmark

ISBN: (纸本)9798400716515

The modern automobile is a network-specifically, a controller area network (CAN)-of computers. Automotive computers manage the engine (e.g., fuel injection), the transmission (e.g., automatic shifting), the vehicle speed (e.g., cruise control), and many, many more systems. Therefore, a vehicle's CAN bus is safety critical;by design, it is robust, reliable, and error tolerant. Unfortunately, it is not secure;it was developed in the 1980s, and, at that time, it was a closed system-no internet access. The modern automobile is not a closed system, yet the CAN bus remains insecure. Automotive researchers are gravitating toward intrusion detection as one possible solution to the problem of automotive [in]security. To build and evaluate an intrusion detection system (IDS), however, researchers need adequate training and testing data. In this paper, we investigate and evaluate the following automotive intrusion detection datasets: (1) the HCRL Car Hacking dataset, (2) the HCRL Survival Analysis dataset, and (3) the can-train-and-test dataset. The HCRL Car Hacking dataset (hcrl-ch) and the HCRL Survival Analysis dataset (hcrl-sa) are well-established in the literature, whereas the can-train-and-test dataset is a promising new dataset. First, we investigate the can-train-and-test dataset-in particular, we evaluate the impacts of various features on the performance of sixteen machine learning IDSs. Second, we compare can-train-and-test to hcrl-ch and hcrl-sa. We find that, compared to the two established datasets, can-train-and-test provides new and greater insights to researchers interested in automotive intrusion detection, automotive firewalls & filtering, and more. With an order of magnitude more training and testing data, can-train-and-test enables the data-intensive machine learning models to demonstrate their full potential, with eight of the sixteen models achieving an average F1-score above 0.9. Moreover, can-train-and-test maintains ample differentiation power;the st

关键词： automotive vehicle controller area network in-vehicle network intrusion detection system can-train-and-test dataset HCRL Car Hacking dataset HCRL Survival Analysis dataset machine learning

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International conference on Artificial Intelligence: Theory and Applications, AITA 2023

International Conference on Artificial Intelligence: Theory ...

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International conference on Artificial Intelligence: Theory and Applications, AITA 2023

ISBN: (纸本)9789819984787

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Application of internet of Thing (IoT) for Precision Farming 16

Application of Internet of Thing (IoT) for Precision Farming

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16th International conference on Signal-Image Technology and internet-Based systems (SITIS)

作者： Songruk, Apidat Arunrungrusmi, Somchai Tunlasakun, Khanchai Suwannakhum, Sirimonpak Mungkungline, Narong Poonthong, Wittawat Yuji, Toshifumi King Mongkuts Univ Technol Thonburi Dept Elect technol Educ Bangkok Thailand King Mongkuts Univ Technol Thonburi Fac Ind Educ & Technol Bangkok Thailand

ISBN: (纸本)9781665464956

With the increasing popularity of the internet of Things (IOT) and devices getting smarter, this research has presented an idea to reform the weather information for precision farming. In this study, the information of weather forecast was prepared for air quality control systems using the sensor of network systems to increase the quality of organic vegetable in Thailand under the condition of Thailand weather The prototype model was described by the devices of embedded systems, which is gotten from the temperature and humidity sensor. Therefore, if there is any data from the input sensor, the users are allowed to access and granted to use this system, then, the log data are transmitted by the internet to the designation website by using a Wi-Fi module (ESP8266). As a result, it can be used the machine learning algorithms for implementing of monitor controller, which is applied for analyzing and collecting data to air quality control in green house

关键词： internet of Thing Arduino smart agriculture

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Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology 20

Enhancing IoT Intelligence: A Transformer-based Reinforcemen...

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20th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Rjoub, Gaith Islam, Saidul Bentahar, Jamal Almaiah, Mohammed Amin Alrawashdeh, Rana Aqaba Univ Technol Fac Informat Technol Aqaba Jordan Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ Canada Khalifa Univ Dept Comp Sci Abu Dhabi U Arab Emirates Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Appl Sci Private Univ Fac Informat Technol Amman Jordan King Fand Univ Petr & Minerals Dept Informat & Comp Sci Dhahran Saudi Arabia

ISBN: (纸本)9798350361261;9798350361278

The proliferation of the internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents' capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decision-making efficiency and adaptability. Our contributions include a detailed exploration of the transformer's role in processing heterogeneous IoT data, a comprehensive evaluation of the framework's performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape.

关键词： internet of Things (IoT) Reinforcement Learning (RL) Proximal Policy Optimization (PPO) Transformers

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IoT Monitoring for Grid Voltage Regulation Using Model Predictive control in Wind systems 2

IoT Monitoring for Grid Voltage Regulation Using Model Predi...

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2nd International conference on Edge Computing and Applications, ICECAA 2023

作者： Sharma, P. V. V. Raghava Prasanna, D. Sai Sirisha, Grandhi Engineering College Department of Electrical and Electronics Engineering Telangana Hyderabad India

ISBN: (纸本)9798350347579

This research provides a novel approach for achieving efficient grid voltage regulation in wind power systems using Model Predictive control (MPC) and internet of Things (IoT) monitoring. The suggested system makes use of an MPC controller that controls the power generated by wind turbines according to real-time measurements of the grid voltage that are collected via an IoT network of sensors and devices for communication. The structure of the system is created to ensure that the grid voltage maintains within a certain range, improving the stability and dependability of the power grid. Extensive MATLAB simulations are used to prove how effective the suggested system is, showing that it performs better in terms of voltage regulation and system stability than traditional control approaches. The feasibility and cost-effectiveness of the suggested system's installation are also examined. According to the research, compared to conventional control methods, the suggested system is simple to set up and maintain and provides considerable cost savings. The performance, dependability, and effectiveness of the power grid may be improved by using the technique presented in the present article, which is feasible and effective for grid voltage control in wind power systems. © 2023 IEEE.

关键词： Model predictive control

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