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A Review on AI-Driven Aerial Access Networks: Challenges and Open Research Issues 5

A Review on AI-Driven Aerial Access Networks: Challenges and...

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5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Lakew, Demeke Shumeye Tran, Anh-Tien Masood, Arooj Dao, Nhu-Ngoc Cho, Sungrae Chung Ang Univ Sch Comp Sci & Engn Seoul 06974 South Korea Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea

ISBN: (纸本)9781665456456

Aerial access networks (AANs) consisting of low altitude platforms (LAPs) and high altitude platforms (HAPs) have been considered as emerging wireless networking technologies to enhance both the capacity and coverage of future wireless networks, especially in remote and hard to reach areas with lack of terrestrial base stations. However, the limited onboard resources and high dynamicity of the network make challenging to optimally manage both the communication and computation resources for an efficient aerial networking infrastructure. On the other hand, artificial intelligence (AI), especially reinforcement learning- and deep reinforcement learning-based networking, are attracting significant attention to capture the network dynamicity and long-term resource management performance, recently. Thus, in this paper, we first provide a taxonomy of AI-driven aerial access networks and then, present a review and discussion on the state-of-the-art researches on AI-driven AANs from the communication and computation perspective. Moreover, we identify existing research challenges and provide future research direction for further investigations.

关键词： Aerial access network UAV HAP edge computing reinforcement learning deep reinforcement learning

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Image Retrieval with Hard Negative Centers and Quadruplet Center Loss in Deep Hashing

Image Retrieval with Hard Negative Centers and Quadruplet Ce...

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2024 IEEE international conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Konstantinou, Nikolaos Semertzidis, Theodoros Dimou, Anastasios Daras, Petros The Visual Computing Lab Centre for Research and Technology Hellas Thessaloniki Greece

ISBN: (纸本)9798350391213

In this work, we enhance image retrieval performance by building upon the Central Similarity Quantization method in deep learning, which uses 'hash centers' - points in a K-dimensional Hamming space fulfilling certain distance criteria. Our approach is inspired by the concept of selecting hard negatives in deep learning, aiming to boost model performance. We introduce an alternative strategy for quadruplet loss training that employs corresponding positive/negative hash centers instead of positive/negative samples. For each sample, we compute the Hamming distances between its hash code and all hash centers and select the two closest centers as negative centers. The positive center is its random assigned center and is used to guide the sample towards convergence. We then define a quadruplet loss function that encourages the learned hash codes to be both discriminative and compact, minimizing the distances between the sample and its positive center while maximizing the distances between the sample and the negative centers. Our method is evaluated using mAP and Recall@K metrics on the Hotels50k, Stanford Dogs, and CUB200 datasets, demonstrating promising results in image retrieval tasks by effectively utilizing hard negative centers and quadruplet center loss. © 2024 IEEE.

关键词： Hamming distance

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Unleashing the Potential of Blockchain and Internet of Things (IoT) convergence: A Comprehensive Study 20

Unleashing the Potential of Blockchain and Internet of Thing...

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20th IEEE international Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Kadir, Khairunnisa A. Wahab, Nur Haliza Abdul Hartomom, Kristoko Dwi Harun, Siti Zaleha College Of Computing Informatics And Mathematics Pahang Branch Raub Campus Pahang Raub27600 Malaysia Universiti Teknologi Malaysia Faculty Of Computing Johor Skudai Malaysia Satya Wacana Christian University Faculty Of Technology Information Salatiga Indonesia

ISBN: (纸本)9798350382310

This comprehensive review explores the synergistic intersection of blockchain technology and the Internet of Things (IoT), revealing how the fusion of these two innovative technologies is revolutionizing both network security and applications. As the IoT ecosystem expands and becomes increasingly complex, the necessity for robust and efficient security measures escalates. Herein, we elucidate how Blockchain technology, renowned for its secure, decentralized nature, can play an instrumental role in safeguarding IoT devices and networks from potential threats and breaches. We conduct an in-depth exploration of the various Blockchain-enabled IoT applications, shedding light on examples that span numerous sectors such as healthcare, supply chain, smart homes, and more. These practical implementations exemplify the enhanced security, transparency, and improved efficiency that Blockchain can introduce to the IoT landscape. This review also identifies and discusses challenges faced when integrating Blockchain with IoT, such as scalability issues and energy consumption, providing suggestions for future research and developments in this area. As the digital age advances, the fusion of Blockchain and IoT holds immense potential not only to fortify security but also to redefine conventional application models. Our analysis presents a clear trajectory of progress and innovation, offering researchers, practitioners, and stakeholder's valuable insights into the future of Blockchain-enabled IoT. © 2024 IEEE.

关键词： Blockchain

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Research on Link Prediction Algorithm Integrating High-order Information and Node Centrality in Network 8

Research on Link Prediction Algorithm Integrating High-order...

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8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Fu, Kang Chang, Wenwen Yan, Guanghui Luo, Hao Lanzhou Jiaotong University School of Electronic and Information Engineering Lanzhou China Key Laboratory of Media Convergence Technology and Communication of Gansu Province Lanzhou China

ISBN: (纸本)9798350302455

Link prediction is an important branch in the field of complex network research. Traditional similarity-based link prediction algorithms mainly calculate the similarity between nodes based on single features such as common neighbors and degrees, without considering the impact of higher-order network structures on link formation. To address this issue, this paper proposes a link prediction algorithm called (H-CCNC) and its extended version (FH-CCNC), which integrates high-order information and node centrality by considering node degree, node centrality, and high-order clustering coefficient from both local and global perspectives in the network. Experiments were conducted on real datasets with different densities, using the area under the curve (AUC) and precision as evaluation metrics and comparing with traditional link prediction algorithms. The results show that H-CCNC and FH-CCNC can predict missing links in the network more accurately, with an AUC improvement ranging from 1% to 22% on different datasets. © 2023 IEEE.

关键词： Complex networks

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Research on Neural Network Structure Optimization Under the Framework of Computer Deep Learning

Research on Neural Network Structure Optimization Under the ...

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2024 international conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Hou, Xianqiang Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350369212

This paper systematically explores the optimization strategies and methods of neural network structure in deep learning environment, aiming to improve model performance, efficiency and generalization ability, and cope with problems such as computing resource consumption, too long training time and overfitting risk caused by large-scale and high-complexity models. The core part of this paper focuses on various strategies and methods of neural network structure optimization. Firstly, the design principles and effects of the depth, width and dynamic adjustment mechanism, as well as the promoting effect of modular design on information flow and feature selection are discussed in detail. The advantages and application scenarios of typical network structures such as CNN, RNN, LSTM and Transformer in specific tasks are compared and analyzed. Secondly, this paper discusses the application of model compression and acceleration technology in neural network structure optimization in detail, and expounds the weight pruning, quantization, knowledge distillation, low-rank decomposition and other model lightweight methods, aiming at reducing the number of parameters and reducing the computational complexity, so as to adapt to the hardware resource constraints and improve the reasoning speed. Thirdly, the paper discusses the value of NAS as an innovative research path in the optimization of neural network structure. Machine learning algorithm is used to automatically explore the optimal network architecture, reduce the burden of manual design, and improve the efficiency of model construction. In addition, this paper also pays attention to the optimization strategies in the training process of neural networks, such as the optimizer's selection learning rate scheduling strategy, early stop rule, etc. These strategies can affect the convergence speed and final performance of the model, and are an indispensable part of the structure optimization. Finally, the paper looks forward to the

关键词： Structural optimization

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Performance of Enhanced CNN-LSTM Prediction Model for Vehicle-Mounted Air Quality Monitoring 15

Performance of Enhanced CNN-LSTM Prediction Model for Vehicl...

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15th international conference on Information and Communication technology convergence, ICTC 2024

作者： Llamelo, Ceferino Medina, Ruji Fajardo, Arnel Graduate Programs Technological Institute of the Philippines Quezon City Philippines School of Architecture Computing and Engineering University of Saint Louis Tuguegarao City Philippines

ISBN: (纸本)9798350364637

CNN-LSTM has been established for predictive modeling in urban air pollution. However, while the method is accurate for time series forecasting, it struggles with accurately capturing spatial variability in air quality data. To address this limitation, we propose an Enhanced CNN-LSTM Prediction Model for vehicle-mounted air quality monitoring systems. This model integrates Convolutional Neural Networks (CNNs), spatial attention mechanisms, and Long Short-Term Memory (LSTM) networks to enhance the capture of both spatial and temporal dependencies in the data. Performance evaluation shows a Mean Absolute Error (MAE) of 5.2 μg/m3for PM2.5, 1.3 μg/m3for PM10, 0.1 ppm for CO, 0.1 ppm for VOCs, and 0.68°C for temperature, with Root Mean Squared Error (RMSE) values of 7.8μg/m3for PM2.5, 1.5μg/m3for PM10, 0.10 ppm for CO, 0.10 ppm for VOCs, and 0.75°C for temperature. Compared to traditional models like Linear Regression and standalone LSTM networks, the Enhanced CNN-LSTM model demonstrates substantial improvements in prediction accuracy. These findings highlight the model's superior performance in delivering accurate, real-time air quality predictions, offering a robust framework for pollution management and public health interventions. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Congestion Control Algorithm based on Switch Queue Length Change Detection 30

Design of Congestion Control Algorithm based on Switch Queue...

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30th IEEE international conference on Telecommunications, ICT 2024

作者： Li, Wei Wu, Zhaonan Zhang, Bosheng Zheng, Li Zhao, Weibo Zou, Jiaqi Sun, Songlin School of Information and Communication Engineering Beijing University of Posts and Telecommunications China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology China

ISBN: (纸本)9798350356694

Remote Direct Memory Access (RDMA) has emerged as the predominant networking technology and plays an important role in the Artificial Intelligence Generated Content (AIGC) industry, which requires effective congestion control algorithms to unleash its performance. Since the Data Center Quantized Congestion Notification (DCQCN) protocol was introduced in 2015, it has quickly become the standard congestion control solution across RDMA networks and an essential component of high-speed clusters and data center networks. However, as a congestion control (CC) algorithm that primarily relies on explicit congestion notification (ECN), DCQCN is challenged by delayed bandwidth convergence and sluggish congestion detection. To address these challenges, this paper proposes an optimized congestion control algorithm solution based on DCQCN. Specifically, we propose more accurate switch queue length monitoring, which is divided into two main parts: a congestion detection phase and a congestion control phase. Our algorithm utilizes the change rate of the switch queue length to refine and identify the degree of congestion and adjust the data sending rate after congestion occurs based on this change rate. Experimental results demonstrate that the proposed algorithm achieves better average flow completion time (FCT) in different scenarios including incast and actual network load, compared with DCQCN and other CC algorithms. © 2024 IEEE.

关键词： Queueing theory

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Multifactor Recommendation-based Video Caching Strategy in Mobile Edge computing 21

Multifactor Recommendation-based Video Caching Strategy in M...

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21st IEEE international conference on Ubiquitous computing and Communications / 5th IEEE international conference on Data Science and Computational Intelligence / 21st IEEE international conference on Computer and Information technology

作者： Liu, Qian Wu, Ying Liu, Qilie Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Chongqing Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China Minist Educ Mobile Commun Technol Key Lab Chongqing 400065 Peoples R China

ISBN: (纸本)9781665477260

Mobile edge computing (MEC) can provide users with high-quality video services by placing computing and storage capacity close to the user. Transferring large size video files usually consumes more time and energy, and the emergence of edge caching can effectively solve this problem. Current caching schemes generally consider the impact of only one or two factors among attributes such as ratings and reviews, without considering the impact of multiple factors together on the recommender system. In this paper, we propose a video caching strategy based on multi-factor recommendation (VCSMFR) to solve the above problem. First, the video file ratings and corresponding rankings are obtained by a recommendation algorithm that fuses multi-factor data (e.g., reviews, directors, and actors). Then, an optimized particle swarm algorithm is used to make caching decisions for files stored on the edge MEC server to solve the problem that traditional particle swarm algorithms are prone to local convergence. Simulation results show that the recommendation algorithm proposed in this paper can analyze user and video information more carefully by fusing multiple factors, and improve the cache hit rate by 11% over the traditional caching scheme. In addition, the greedy algorithm is introduced into the optimized particle swarm algorithm, which improves the local search ability as well as the convergence of the algorithm and achieves faster caching decisions.

关键词： mobile edge computing recommender systems video caching particle swarm algorithm multi-factor recommendations

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User-Aware Resource Allocation Algorithm for Virtual Reality in Metaverse

User-Aware Resource Allocation Algorithm for Virtual Reality...

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international conference on Information and Communication technology convergence (ICTC)

作者： Jaechan Lee Yumi Kim Haneul Ko Department of Electronics and Information Convergence Engineering Kyung Hee University Yongin Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Even though introducing the edge computing to support metaverse services, sophisticated resource allocation algorithm is needed due to varied required computation resources and operating environments. In this paper, we introduce a user-aware resource allocation algorithm (UARA) for the metaverse and examine a resource allocation strategy for edge cloud computing. The objective of the resource allocation strategy is to maximize the aggregated quality of experiences (QoEs) for users while ensuring that each user's QoE remains above a certain threshold. Evaluation results show that the proposed resource allocation policy enhances users' QoE.

关键词： Cloud computing Metaverse Markov decision processes Virtual reality Linear programming Information and communication technology Resource management Quality of experience Standards Edge computing

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Optimizing Team Composition: Genetic Algorithms vs. Linear Programming in Resource Allocation

Optimizing Team Composition: Genetic Algorithms vs. Linear P...

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2023 international conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Rajapriya, M. Rohini, V. Krishnan, S. Arul Prakash, K. Gayathri, G. Sugerthana Helena Seles, D. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Management Studies Chennai India S.A. Engineering College Department of Management Studies Tamilnadu Chennai India Srm Institute of Science and Technology Faculty of Management Ramapuram Chennai India Alpha Arts & Science College Department of Computer Science Porur Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Administration Avadi Chennai India

ISBN: (纸本)9798350348910

The allocation of resources is a crucial undertaking in various fields, and selecting an optimization approach plays a central role in attaining the intended results. The present work aims to conduct a comparative analysis of the performance of Genetic Algorithms (GA) and Linear Programming (LP) in the specific domain of optimizing team composition by means of resource allocation. Several tests and analyses were done to assess the merits and limitations of these two methodologies. The results of our study indicate that the utilization of resources by GA is significantly higher, reaching roughly 92.5%. This finding suggests that GA holds promise as a suitable option when the maximization of resource usage is of utmost importance. Nevertheless, it can be observed that LP regularly exhibits superior performance compared to GA in terms of team composition score. LP achieves an average score of roughly 84.9, which suggests that the solutions generated by LP are more closely matched with the desired team composition. Additionally, analyzing resource allocation among individual team members reveals that GA tends to allocate resources more equitably. In contrast, LP displays a modest inclination towards favoring specific team members. The Genetic Algorithm (GA) exhibits speedier convergence times, with an average convergence time of roughly 321 seconds, in contrast to the average convergence time of 423 seconds observed in Linear Programming (LP). Nevertheless, it is important to acknowledge that the computing resources required for GA are marginally higher, around 95.2%, compared to LP which demands approximately 91.8% of resources. © 2023 IEEE.

关键词： Genetic algorithms

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