Intelligent and Converged Networks最新文献_第3页

Edge-Assisted Indexing for Highly Dynamic and Static Data in Mixed Reality Connected Autonomous Vehicles 为混合现实互联自动驾驶汽车中的高动态和静态数据建立边缘辅助索引

Intelligent and Converged Networks Pub Date : 2024-06-01 DOI: 10.23919/ICN.2024.0012

Daniel Mawunyo Doe;Dawei Chen;Kyungtae Han;Haoxin Wang;Jiang Xie;Zhu Han

{"title":"Edge-Assisted Indexing for Highly Dynamic and Static Data in Mixed Reality Connected Autonomous Vehicles","authors":"Daniel Mawunyo Doe;Dawei Chen;Kyungtae Han;Haoxin Wang;Jiang Xie;Zhu Han","doi":"10.23919/ICN.2024.0012","DOIUrl":"https://doi.org/10.23919/ICN.2024.0012","url":null,"abstract":"The integration of Mixed Reality (MR) technology into Autonomous Vehicles (AVs) has ushered in a new era for the automotive industry, offering heightened safety, convenience, and passenger comfort. However, the substantial and varied data generated by MR-Connected AVs (MR-CAVs), encompassing both highly dynamic and static information, presents formidable challenges for efficient data management and retrieval. In this paper, we formulate our indexing problem as a constrained optimization problem, with the aim of maximizing the utility function that represents the overall performance of our indexing system. This optimization problem encompasses multiple decision variables and constraints, rendering it mathematically infeasible to solve directly. Therefore, we propose a heuristic algorithm to address the combinatorial complexity of the problem. Our heuristic indexing algorithm efficiently divides data into highly dynamic and static categories, distributing the index across Roadside Units (RSUs) and optimizing query processing. Our approach takes advantage of the computational capabilities of edge servers or RSUs to perform indexing operations, thereby shifting the burden away from the vehicles themselves. Our algorithm strategically places data in the cache, optimizing cache hit rate and space utilization while reducing latency. The quantitative evaluation demonstrates the superiority of our proposed scheme, with significant reductions in latency (averaging 27%–49.25%), a 30.75% improvement in throughput, a 22.50% enhancement in cache hit rate, and a 32%–50.75% improvement in space utilization compared to baseline schemes.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 2","pages":"167-179"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research Progress of Quantum Artificial Intelligence in Smart City 量子人工智能在智慧城市中的研究进展

Intelligent and Converged Networks Pub Date : 2024-06-01 DOI: 10.23919/ICN.2024.0009

Sumin Wang;Ning Wang;Tongyu Ji;Yiyun Shi;Chao Wang

{"title":"Research Progress of Quantum Artificial Intelligence in Smart City","authors":"Sumin Wang;Ning Wang;Tongyu Ji;Yiyun Shi;Chao Wang","doi":"10.23919/ICN.2024.0009","DOIUrl":"https://doi.org/10.23919/ICN.2024.0009","url":null,"abstract":"The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence (AI). As Moore's Law approaches its limit, it is imperative to break the constraints that are holding back artificial intelligence. Quantum computing and artificial intelligence have been advancing along the highway of human civilization for many years, emerging as new engines driving economic and social development. This article delves into the integration of quantum computing and artificial intelligence in both research and application. It introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum effects. The discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives: quantum supervised learning, quantum unsupervised learning, quantum reinforcement learning, and quantum deep learning. In an effort to address the limitations of smart cities, this article explores the formidable potential of quantum artificial intelligence in the realm of smart cities. It does so by examining aspects such as intelligent transportation, urban operation assurance, urban planning, and information communication, showcasing a plethora of practical achievements in the process. In the foreseeable future, Quantum Artificial Intelligence (QAI) is poised to bring about revolutionary development to smart cities. The urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers, constructing an efficient, stable, and adaptive hybrid computing architecture that integrates quantum and classical computing, preparing quantum data as needed, and advancing controllable qubit hardware equipment to meet actual demands. The ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities, robustness, excellent generalization capabilities, and interpretability.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 2","pages":"116-133"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601663","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

JudPriNet: Video Transition Detection Based on Semantic Relationship and Monte Carlo Sampling JudPriNet：基于语义关系和蒙特卡罗采样的视频转换检测

Intelligent and Converged Networks Pub Date : 2024-06-01 DOI: 10.23919/ICN.2024.0010

Bo Ma;Jinsong Wu;Wei Qi Yan

{"title":"JudPriNet: Video Transition Detection Based on Semantic Relationship and Monte Carlo Sampling","authors":"Bo Ma;Jinsong Wu;Wei Qi Yan","doi":"10.23919/ICN.2024.0010","DOIUrl":"https://doi.org/10.23919/ICN.2024.0010","url":null,"abstract":"Video understanding and content boundary detection are vital stages in video recommendation. However, previous content boundary detection methods require collecting information, including location, cast, action, and audio, and if any of these elements are missing, the results may be adversely affected. To address this issue and effectively detect transitions in video content, in this paper, we introduce a video classification and boundary detection method named JudPriNet. The focus of this paper is on objects in videos along with their labels, enabling automatic scene detection in video clips and establishing semantic connections among local objects in the images. As a significant contribution, JudPriNet presents a framework that maps labels to “Continuous Bag of Visual Words Model” to cluster labels and generates new standardized labels as video-type tags. This facilitates automatic classification of video clips. Furthermore, JudPriNet employs Monte Carlo sampling method to classify video clips, the features of video clips as elements within the framework. This proposed method seamlessly integrates video and textual components without compromising training and inference speed. Through experimentation, we have demonstrated that JudPriNet, with its semantic connections, is able to effectively classify videos alongside textual content. Our results indicate that, compared with several other detection approaches, JudPriNet excels in high-level content detection without disrupting the integrity of the video content, outperforming existing methods.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 2","pages":"134-146"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Demand-Based Dynamic Bandwidth Allocation in Multi-Beam Satellites Using Machine Learning Concepts 利用机器学习概念在多波束卫星中实现基于需求的动态带宽分配

Intelligent and Converged Networks Pub Date : 2024-06-01 DOI: 10.23919/ICN.2024.0011

Shwet Kashyap;Nisha Gupta

{"title":"Demand-Based Dynamic Bandwidth Allocation in Multi-Beam Satellites Using Machine Learning Concepts","authors":"Shwet Kashyap;Nisha Gupta","doi":"10.23919/ICN.2024.0011","DOIUrl":"https://doi.org/10.23919/ICN.2024.0011","url":null,"abstract":"In the realm of satellite communication, where the importance of efficient spectrum utilization is growing day by day due to the increasing significance of this technology, dynamic resource management has emerged as a pivotal consideration in the design of contemporary multi-beam satellites, facilitating the flexible allocation of resources based on user demand. This research paper delves into the pivotal role played by machine learning and artificial intelligence within the domain of satellite communication, particularly focusing on spot beam satellites. The study encompasses an evaluation of machine learning's application, whereby an extensive dataset capturing user demand across a specific geographical area is subjected to analysis. This analysis involves determining the optimal number of beams/clusters, achieved through the utilization of the knee-elbow method predicated on within-cluster sum of squares. Subsequently, the demand data are equitably segmented employing the weighted k-means clustering technique. The proposed solution introduces a straightforward yet efficient model for bandwidth allocation, contrasting with conventional fixed beam illumination models. This approach not only enhances spectrum utilization but also leads to noteworthy power savings, thereby addressing the growing importance of efficient resource management in satellite communication.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 2","pages":"147-166"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Topology Design and Graph Embedding for Decentralized Federated Learning 分散式联合学习的拓扑设计和图嵌入

Intelligent and Converged Networks Pub Date : 2024-06-01 DOI: 10.23919/ICN.2024.0008

Yubin Duan;Xiuqi Li;Jie Wu

{"title":"Topology Design and Graph Embedding for Decentralized Federated Learning","authors":"Yubin Duan;Xiuqi Li;Jie Wu","doi":"10.23919/ICN.2024.0008","DOIUrl":"https://doi.org/10.23919/ICN.2024.0008","url":null,"abstract":"Federated learning has been widely employed in many applications to protect the data privacy of participating clients. Although the dataset is decentralized among training devices in federated learning, the model parameters are usually stored in a centralized manner. Centralized federated learning is easy to implement; however, a centralized scheme causes a communication bottleneck at the central server, which may significantly slow down the training process. To improve training efficiency, we investigate the decentralized federated learning scheme. The decentralized scheme has become feasible with the rapid development of device-to-device communication techniques under 5G. Nevertheless, the convergence rate of learning models in the decentralized scheme depends on the network topology design. We propose optimizing the topology design to improve training efficiency for decentralized federated learning, which is a non-trivial problem, especially when considering data heterogeneity. In this paper, we first demonstrate the advantage of hypercube topology and present a hypercube graph construction method to reduce data heterogeneity by carefully selecting neighbors of each training device—a process that resembles classic graph embedding. In addition, we propose a heuristic method for generating torus graphs. Moreover, we have explored the communication patterns in hypercube topology and propose a sequential synchronization scheme to reduce communication cost during training. A batch synchronization scheme is presented to fine-tune the communication pattern for hypercube topology. Experiments on real-world datasets show that our proposed graph construction methods can accelerate the training process, and our sequential synchronization scheme can significantly reduce the overall communication traffic during training.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 2","pages":"100-115"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601665","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Cache Policy Optimization Through Deep Reinforcement Learning in Dynamic Cellular Networks 通过动态蜂窝网络中的深度强化学习实现自适应缓存策略优化

Intelligent and Converged Networks Pub Date : 2024-06-01 DOI: 10.23919/ICN.2024.0007

Ashvin Srinivasan;Mohsen Amidzadeh;Junshan Zhang;Olav Tirkkonen

{"title":"Adaptive Cache Policy Optimization Through Deep Reinforcement Learning in Dynamic Cellular Networks","authors":"Ashvin Srinivasan;Mohsen Amidzadeh;Junshan Zhang;Olav Tirkkonen","doi":"10.23919/ICN.2024.0007","DOIUrl":"https://doi.org/10.23919/ICN.2024.0007","url":null,"abstract":"We explore the use of caching both at the network edge and within User Equipment (UE) to alleviate traffic load of wireless networks. We develop a joint cache placement and delivery policy that maximizes the Quality of Service (QoS) while simultaneously minimizing backhaul load and UE power consumption, in the presence of an unknown time-variant file popularity. With file requests in a time slot being affected by download success in the previous slot, the caching system becomes a non-stationary Partial Observable Markov Decision Process (POMDP). We solve the problem in a deep reinforcement learning framework based on the Advantageous Actor-Critic (A2C) algorithm, comparing Feed Forward Neural Networks (FFNN) with a Long Short-Term Memory (LSTM) approach specifically designed to exploit the correlation of file popularity distribution across time slots. Simulation results show that using LSTM-based A2C outperforms FFNN-based A2C in terms of sample efficiency and optimality, demonstrating superior performance for the non-stationary POMDP problem. For caching at the UEs, we provide a distributed algorithm that reaches the objectives dictated by the agent controlling the network, with minimum energy consumption at the UEs, and minimum communication overhead.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 2","pages":"81-99"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601662","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cascaded Channel Decoupling Based Solution for RIS Regulation Matrix 基于级联通道去耦的 RIS 调节矩阵解决方案

Intelligent and Converged Networks Pub Date : 2024-03-28 DOI: 10.23919/ICN.2024.0002

Yajun Zhao

引用次数: 0

Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems 带电池储能系统的光伏系统在线调度的深度强化学习

Intelligent and Converged Networks Pub Date : 2024-03-28 DOI: 10.23919/ICN.2024.0003

Yaze Li;Jingxian Wu;Yanjun Pan

{"title":"Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems","authors":"Yaze Li;Jingxian Wu;Yanjun Pan","doi":"10.23919/ICN.2024.0003","DOIUrl":"https://doi.org/10.23919/ICN.2024.0003","url":null,"abstract":"A new online scheduling algorithm is proposed for photovoltaic (PV) systems with battery-assisted energy storage systems (BESS). The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather conditions. The proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging activities. The scheduling algorithm is developed by using deep deterministic policy gradient (DDPG), a deep reinforcement learning (DRL) algorithm that can deal with continuous state and action spaces. One of the main contributions of this work is a new DDPG reward function, which is designed based on the unique behaviors of energy systems. The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation. The new scheduling algorithm is tested through case studies using real world data, and the results indicate that it outperforms existing algorithms such as Deep Q-learning. The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 1","pages":"28-41"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10484537","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security 基于自适应海洋捕食者优化算法（AOMA）和深度监督学习分类（DSLC）的城域网安全 IDS 框架

Intelligent and Converged Networks Pub Date : 2024-03-28 DOI: 10.23919/ICN.2024.0001

M. Sahaya Sheela;A. Gnana Soundari;Aditya Mudigonda;C. Kalpana;K. Suresh;K. Somasundaram;Yousef Farhaoui

{"title":"Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security","authors":"M. Sahaya Sheela;A. Gnana Soundari;Aditya Mudigonda;C. Kalpana;K. Suresh;K. Somasundaram;Yousef Farhaoui","doi":"10.23919/ICN.2024.0001","DOIUrl":"https://doi.org/10.23919/ICN.2024.0001","url":null,"abstract":"Due to the dynamic nature and node mobility, assuring the security of Mobile Ad-hoc Networks (MANET) is one of the difficult and challenging tasks today. In MANET, the Intrusion Detection System (IDS) is crucial because it aids in the identification and detection of malicious attacks that impair the network's regular operation. Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET. However, it still has significant flaws, including increased algorithmic complexity, lower system performance, and a higher rate of misclassification. Therefore, the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models. Here, the minmax normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields, which increases the overall intrusion detection performance of classifier. Then, a novel Adaptive Marine Predator Optimization Algorithm (AOMA) is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier. Moreover, the Deep Supervise Learning Classification (DSLC) mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations. During evaluation, the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 1","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10484536","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy and IRLNC-Based Routing Approach to Improve Data Storage and System Reliability in IoT 基于模糊和 IRLNC 的路由方法，提高物联网中的数据存储和系统可靠性

Intelligent and Converged Networks Pub Date : 2024-03-28 DOI: 10.23919/ICN.2024.0006

U. Indumathi;A. R. Arunachalam

{"title":"Fuzzy and IRLNC-Based Routing Approach to Improve Data Storage and System Reliability in IoT","authors":"U. Indumathi;A. R. Arunachalam","doi":"10.23919/ICN.2024.0006","DOIUrl":"https://doi.org/10.23919/ICN.2024.0006","url":null,"abstract":"Internet of Things (IoT) based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation. While performing this entire process, there is a high possibility for data corruption in the mid of transmission. On the other hand, the network performance is also affected due to various attacks. To address these issues, an efficient algorithm that jointly offers improved data storage and reliable routing is proposed. Initially, after the deployment of sensor nodes, the election of the storage node is achieved based on a fuzzy expert system. Improved Random Linear Network Coding (IRLNC) is used to create an encoded packet. This encoded packet from the source and neighboring nodes is transmitted to the storage node. Finally, to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector (DSDV) algorithm. Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics. Average residual energy, packet delivery ratio, compression ratio and storage time achieved for the proposed work are 8.8%, 0.92%, 0.82%, and 69 s. Based on this analysis, it is revealed that better data storage system and system reliability is attained using this proposed work.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 1","pages":"68-80"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10484538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0