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Artificial Intelligence-Driven Unmanned Aerial Vehicle Base Station Placement: Current Advances, Challenges, and Use Case 9

Artificial Intelligence-Driven Unmanned Aerial Vehicle Base ...

引用

9th IEEE international conference on Fog and Mobile Edge computing (FMEC)

作者： Abdel-Razeq, Sharief Al-Khdouer, Ayat Salameh, Haythem Bany Jararweh, Yaser Elrefae, Ghaleb Yarmouk Univ Dept Telecommun Engn Irbid Jordan Al Ain Univ Dept Commun & Networking Al Ain U Arab Emirates Jordan Univ Sci & Technol Dept Comp Sci Irbid Jordan Al Ain Univ Coll Business Al Ain U Arab Emirates

ISBN: (纸本)9798350366495;9798350366488

In recent years, there has been a lot of interest in using drones as aerial base stations (BSs), namely drone base stations (DBS), to help with ground connection under unique situations. Despite the apparent benefits of DBSs, they present some significant obstacles that must be solved in order to provide a robust drone-assisted wireless network service. Optimal placement of DBSs in the air and their movement to ensure optimum coverage is one of the most crucial difficulties. In this paper, we provide an overview of DBSs and their challenges, focusing on the main challenge, the placement, and how artificial intelligence (AI) can be utilized to solve such a challenge effectively. Finally, we present a use case in which we propose an AI-based approach for distributing multiple DBSs to serve zones within a network while allowing for dynamic behavior in user demand and services over time. this problem is solved using the RL approach based on the SARSA algorithm for exploration and continuously learns and adapts DBS allocation strategies in response to environmental interactions. the simulation results show an increase in operator profit compared to equal policy dispatch.

关键词： B5G/6G Drone Base Station Artificial Intelligence Reinforcement learning SARSA

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Hybrid AI Architecture using Edge-Cloud computing for Secure V2X Communication 9

Hybrid AI Architecture using Edge-Cloud Computing for Secure...

引用

9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Williams, K. Lalith Prasanth, Y. Durga Jeyaselvi, M. Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu Kattankulathur India

ISBN: (纸本)9798350377972

Modern vehicles increasingly rely on exchanging real-time data with other vehicles (V2V) and infrastructure (V2I) making them vulnerable to a growing set of cyber security threats, including spoofing, data manipulation, and denial of service (DoS) attacks. this paper introduces a hybrid AI architecture combining edge computing with cloud based intelligence for improving the security and the resilience of vehicle to everything (V2X) communication systems. Based on the need for prompt responses and the reduction of latency, the proposed approach takes advantage of edge computing to allow onboard AI systems to detect and mitigate threats locally in real time. At the same time, the cloud is also aggregating anonymized threat data from many edge devices using federated learning to enhance global threat models and disseminate security updates across the network. By connecting scalable cloud intelligence offering to localized decision making, this architecture provides an approach to addressing evolving threats, while still reducing communication delays and defending privacy. the study shows that this layered multi-layer AI framework can strengthen the vehicle's cybersecurity of connected vehicles and help with the secure and reliable communication required for intelligent transportation systems. © 2024 IEEE.

关键词： Blockchain

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Multi-Task Lycium barbarum Recognition and Pedicel Localization Network Based on Improved YOLOv8 9

Multi-Task Lycium barbarum Recognition and Pedicel Localizat...

引用

9th international conference on Image, Vision and computing, ICIVC 2024

作者： Li, Fen Li, Dongxiao Huang, Le Zhang, Ming College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou China Jinhua Institute Zhejiang University Jinhua China

ISBN: (纸本)9798350385991

Given the unique indeterminate inflorescence trait of Lycium barbarum, traditional indiscriminate harvesting techniques like vibration, comb-brush, or airflow methods, fail to selectively harvest mature fruits, leading to an unsatisfactory picking rate and considerable damage. While a vision system might provide assistance for precise picking, the intricate task of harvesting Lycium barbarum remains challenging due to aspects like their diminutive size, compact distribution, and the presence of various complex occlusions. the three-stage method proposed in this paper facilitates precise object recognition and semantic segmentation of Lycium barbarum and provides individually targeted segmentation of each stem on branches with mature fruits, achieved by organically arranging three operational components based on improved YOLOv8. Moreover, the proposed method has been validated with the LyciumSeg dataset, yielding respective mean average precision (mAP) scores of 97.8% for Lycium barbarum recognition and 99.5% for pedicel segmentation. © 2024 IEEE.

关键词： Semantic Segmentation

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CSPUMS: Pioneering Integrated Monitoring in Multi-Service Provider Ecosystems 9

CSPUMS: Pioneering Integrated Monitoring in Multi-Service Pr...

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9th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2024

作者： Liu, Jiali Qin, Yuqin Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350373554

In the era of information interconnection, the evolution of enterprise digitalization necessitates the adoption of architectures that can support dynamic and complex business operations. Microservices architecture, characterized by its flexibility, scalability, and efficiency, has emerged as a pivotal solution for modern enterprises, enabling the decomposition of monolithic applications into independently deployable services. However, with the expansion of enterprises and the intricate nature of business operations, microservices architecture encounters challenges in service governance, monitoring, management, and integration in multi-vendor environments. Addressing these challenges, this paper introduces a Cross-Service Provider Unified Monitoring System (CSPUMS) tailored for microservice business platforms operating within multi-service provider ecosystems. CSPUMS, a novel three-layer system architecture, encompasses a data acquisition layer, a monitoring layer, and a data presentation layer, designed to ensure the reliability, performance, and security of service platforms across diverse cloud environments. through the deployment of Docker container technology for data aggregation and the implementation of a multi-dimensional threshold alarm template, CSPUMS enhances operational monitoring, fault management, and service continuity in complex multi-service settings. this paper significantly enhances microservice platforms by providing integrated monitoring solutions for multi-cloud and multi-service environments, ensuring operational efficiency and agility for digitally transforming enterprises. © 2024 IEEE.

关键词： Architecture

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A New Strategy for Reducing Latency with Deep Learning in Fog computing Environment 9th

A New Strategy for Reducing Latency with Deep Learning in Fo...

引用

9th international conference on Ubiquitous networking (UNet)

作者： Koundoul, Birane Kasse, Youssou Balde, Fatoumata Gueye, Bamba Univ Alioune Diop Bambey Bambey Senegal Univ Cheikh Anta Diop Dakar Senegal

ISBN: (纸本)9783031624872;9783031624889

the data generated by connected objects is becoming increasingly numerous and often cyclical. Fog computing (FC) has emerged as an attractive solution to bring data closer to the edge, meet requirements, and manage the growing demand for data. However, network congestion produced by connected devices increases latency and energy consumption. In addition, managing similar processes in fog nodes is difficult. Some processes evolve rapidly into complicated, heterogeneous, and dynamic structures. A reduction of latency, bandwidth, and energy consumption represents issues that can be addressed by neural networks. Indeed, Deep learning can offer fast, reliable processing times on huge quantities of data. therefore, integrating deep learning in a fog environment would be interesting. therefore, we proposed a new strategy that enables the selection of the best fog node within a given zone by leveraging a deep learning-based LSTM model (BRFC-LSTM) and metrics such as data size, bandwidth, and the number of layers in the node.

关键词： IoT deep learning system efficiency quality of service fog computing

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Integrative Day Trading Stock Trend Prediction using Web Scraping and Sentiment Analysis 9

Integrative Day Trading Stock Trend Prediction using Web Scr...

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9th IEEE international conference for Convergence in Technology, I2CT 2024

作者： Chennupati, Anjali Prahas, Bhamidipati Ghali, Bharadwaj Aaditya Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the digital era, data is pivotal across disciplines like business, marketing, engineering, and social sciences. this research proposes a new method of acquiring intraday stock data from the National Stock Exchangeand sentiment-laden news articles, via the web-scrapping technology. Spark's distributed computing framework that provides powerful processing capabilities has been employed to perform sentiment analysis and quantify sentiment scores for the chosen stock over the past month. Concurrently, the XGBoost algorithm processes the historical stock data to predict future values along with the cross-validation process. With the integration between the XGBoostpredictions and the sentiment score, this model offers stock recommendations to the user to either buy or sell a certain stock. the proposed model performance is evaluated for future value predictionsbased on the average RMSE score reported as *** extremely systematic take reaches across financial prediction, natural language processing, and machine learning to supply recommendations for investors. © 2024 IEEE.

关键词： Sentiment analysis

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Construction of a Load-testing Environment for distributed Systems Including Idle Computers 25

Construction of a Load-testing Environment for Distributed S...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Nagashima, Yuya Higashino, Masayuki Kawamura, Takao Tottori University Tottori Japan

ISBN: (纸本)9798350330014

In recent years, many educational institutions have adopted information systems such as e-Learning platforms and groupware. However, these existing information systems often have significant installation, operation, and maintenance costs. Moreover, on-premise systems require expensive servers and cloud computing systems can accumulate high maintenance expenses. To address this issue, we have proposed that utilizes idle resources (idle computers) in an organization's information system. One of the characteristics of idle computers is that communication lines can be connected or disconnected at arbitrary times according to the operations of the original users. In general, the distributed system uses an idle computer as an unstable resource. However, our proposed system requires tolerating them as normal behaviors, and it requires a test environment for this problem. In this paper, we propose a load-testing environment to use idle computers having unstable behaviors in a distributed system. We also implemented and applied the proposed environment to a distributed e-Learning system. the result shows efficiency in showing behavior of unstable behaviors of idle computers to developers. © 2023 IEEE.

关键词： Information systems

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Optimal Placement of Tie-Switches Among Networked Microgrids Considering Annual Load Demand and distributed Energy Resource Power Supply and Absorption 9

Optimal Placement of Tie-Switches Among Networked Microgrids...

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9th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2024

作者： Huang, Wei-Tzer Lin, Wei-Chen Hsiao, Chai-Hsien Yao, Kai-Chao Li, Zong-Tai Tsai, Pei-Hsun National Changhua University of Education Department of Industrial Education and Technology Changhua City Taiwan

ISBN: (纸本)9798350394191

Guided by the standards outlined in IEEE Std 1547.4, it is feasible to organize distribution networks into a configuration known as networked microgrids, which significantly enhances the resilience of distribution network operations. this study proposes an approach for determining the optimal placement of tie-switches within these networked microgrids. this tactic is crucial for determining the boundaries of microgrids to attain superior configurations, thereby the placement and quantity of tie-switches is decided to improve the efficiency and reliability of the distribution networks. the analysis conducted in this study is based on 48 scenarios that utilize extensive big data on annual load demand as well as distributed energy resource power supply and absorption. this data is instrumental in defining the boundaries of the microgrids. the results aim not only to prevent the overloading of main transformers and distribution feeders and to improve the operational performance of distribution networks but also to facilitate efficient power restoration through the autonomous, self-regulating operation of dynamic boundaries. this approach provides an alternative to traditional restoration processes in emergency situations caused by natural disasters. © 2024 IEEE.

关键词： Power distribution networks

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9th international conference on Mobile, Secure, and Programmable networking, MSPN 2023

9th International Conference on Mobile, Secure, and Programm...

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9th international conference on Mobile, Secure, and Programmable networking, MSPN 2023

ISBN: (纸本)9783031524257

the proceedings contain 15 papers. the special focus in this conference is on Mobile, Secure, and Programmable networking. the topics include: Applying Transfer Testing to Identify Annotation Discrepancies in Facial Emotion Data Sets;multiple Person Tracking Based on Gait Identification Using Kinect and OpenPose;evaluating Image Similarity Using Contextual Information of Images with Pre-trained Models;AI vs. Dinosaurs – Automated Re-implementation of Legacy Mainframe Applications in Java by Combining Program Synthesis and GPT;fully distributed Deep Neural Network: F2D2N;StrucTemp-GNN: An Intrusion Detection Framework in IoT Networks Using Dynamic Heterogeneous Graph Neural Networks;generating Synthetic Data to Improve Intrusion Detection in Smart City Network Systems;Building the Shortest Path Database in Urban Traffic Network Using RAO Algorithm;Position Paper: Strengthening Applets on Legacy SIM Cards with Singularization, a New Moving Target Defense Strategy;a Smart Network Repository Based on Graph Database;enhancing Security in Connected Medical IoT Networks through Deep Learning-Based Anomaly Detection;Blockchain-Driven Animal Healthcare: Leveraging NFTs, IPFS, and Smart Contracts for Comprehensive Animal Medical Record;Transforming Child Health Records: Integrating Blockchain, NFTs, and IPFS for Enhanced Medical Data Management.

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9th international Symposium on Ubiquitous networking , UNet 2023

9th International Symposium on Ubiquitous Networking , UNet ...

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9th international Symposium on Ubiquitous networking , UNet 2023

ISBN: (纸本)9783031624872

the proceedings contain 14 papers. the special focus in this conference is on Ubiquitous networking. the topics include: Convergence of Blockchain Enabled Internet of things (IoT) Framework: A Survey;Design of a Microstrip Triangular Patch Array at 5.8 GHz for Enhancing UAV Autonomy in IoT Applications;modeling of Noise due to a Plasma-Based Contactless Power Transmission System for Ultra-High-Speed Transportation;Q-Learning-Augmented Grant-Free NOMA for URLLC;A Comparative Analysis of Sentence Embedding Techniques and LSTM Models in Web Page Classification;wearable IoT Sensor Combining Deep Learning for Enhanced Human Activity Recognition in Indoor and Outdoor Settings;data Balancing Process to Strengthen a Malaria Control Prediction System in Senegal;A Literature Review on Smart Greenhouse and AI: Paradigms, Opportunities and Open Issues;performance Evaluation of LoRa in a Linear Deployment Scenario;a New Strategy for Reducing Latency with Deep Learning in Fog computing Environment;on the Use of Autoencoders in Unsupervised Learning for Intrusion Detection Systems;Smart City Development through Collective Intelligence Based on Blockchain-IPFS-Data Analytics.

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