ICT Express最新文献_第7页

On the road to the metaverse: Point cloud video streaming: Perspectives and enablers 在通往虚拟世界的道路上：点云视频流：视角和推动者

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2024.11.001

Patrick Enenche , Dong Ho Kim , Dongho You

{"title":"On the road to the metaverse: Point cloud video streaming: Perspectives and enablers","authors":"Patrick Enenche , Dong Ho Kim , Dongho You","doi":"10.1016/j.icte.2024.11.001","DOIUrl":"10.1016/j.icte.2024.11.001","url":null,"abstract":"<div><div>In the rapidly evolving metaverse ecosystem, video streaming plays a vital role, offering immersive, on-demand experiences and engaging users in dynamic 3D virtual environments. Point cloud video streaming enables detailed 3D representations for applications like 3D scanning, AR, and VR. However, high data demands and complex encoding pose challenges, requiring efficient resource and latency management. As a result, user experience (UX) is critical in the metaverse, where metrics like real-time feedback, Quality of Experience (QoE), and adaptive UX design are essential for sustaining engagement in immersive 3D environments. Therefore, this paper closely examines the latest developments in point cloud video streaming, specifically focusing on user-centric, AI-driven, and low-latency techniques, transcending traditional video streaming methods. We investigate a variety of techniques, pinpoint obstacles, and chart prospective routes for elevating both the quality and depth of immersive experiences. Furthermore, our work includes a detailed qualitative comparison of these streaming paradigms. We also shed light on pivotal metaverse enhancers, such as Mobile Edge Computing (MEC), Visible Light Communication (VLC), advanced network coding techniques, and the integration of the Data Plane Development Kit (DPDK). These enablers—particularly when combined with DPDK’s capabilities in efficient data packet processing—have the potential to optimize resource allocation, strengthen reliability, reduce latency, and improve the sustainability of point cloud video streaming. Our study, therefore, offers a holistic overview of the advancing area of point cloud video streaming and outlines paths for future research and development.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 1","pages":"Pages 93-104"},"PeriodicalIF":4.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","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 beamforming scheme for coexistence of 5G base station and radar altimeter 5G基站与雷达高度计共存的自适应波束形成方案

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-01-31 DOI: 10.1016/j.icte.2025.01.003

Jiaqi Li, Seung-Hoon Hwang

引用次数: 0

Constellation design of 32-QAM by scaling two square 16-QAMs with dual-level coding 用双级编码缩放两个方形16- qam的32-QAM星座设计

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-01-31 DOI: 10.1016/j.icte.2025.01.008

Minhui Yu, Seunggi Choi, Hosung Park

引用次数: 0

DMAE-HU: A novel deep multitasking autoencoder for hybrid hyperspectral unmixing in remote sensing DMAE-HU：一种用于遥感混合高光谱分解的新型深度多任务自编码器

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-01-28 DOI: 10.1016/j.icte.2025.01.007

Suresh Aala , Prudhvi Krishna Pavuluri , Anuj Deshpande , Sravan Kumar Sikhakolli , Karthikeyan Elumalai , Sunil Chinnadurai , Eswar Panchakarla , Md. Abdul Latif Sarker , Dong Seog Han

{"title":"DMAE-HU: A novel deep multitasking autoencoder for hybrid hyperspectral unmixing in remote sensing","authors":"Suresh Aala , Prudhvi Krishna Pavuluri , Anuj Deshpande , Sravan Kumar Sikhakolli , Karthikeyan Elumalai , Sunil Chinnadurai , Eswar Panchakarla , Md. Abdul Latif Sarker , Dong Seog Han","doi":"10.1016/j.icte.2025.01.007","DOIUrl":"10.1016/j.icte.2025.01.007","url":null,"abstract":"<div><div>Hyperspectral unmixing (HU) is crucial for extracting material information from hyperspectral images (HSI) obtained through remote sensing. Although linear unmixing methods are widely used due to their simplicity, they only address linear mixing effects. Nonlinear mixing models, while more complex, often focus solely on the nonlinear aspects affecting individual pixels. However, in practice, light reflected from materials within a pixel experiences linear and nonlinear interactions, necessitating a hybrid mixing model (HMM) that leverages spatial and spectral information. This work proposes a novel deep learning-based autoencoder (AE) with dual-stream decoders to enhance spectral unmixing. Our approach employs multitask learning (MTL) to process spatial and spectral information concurrently. Specifically, one decoder stream performs linear unmixing from HSI patches, while the other stream utilizes fully connected layers to capture and model the nonlinear interactions within the data. By integrating linear and nonlinear information, our method improves the accuracy of unmixing the mixed spectrum. We validate the effectiveness of our architecture on three real-world HSI datasets and compare its performance against various baseline methods. Experimental results consistently demonstrate that our approach outperforms existing methods, as evidenced by superior spectral angle distance (SAD) and mean squared error (MSE) metrics.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 2","pages":"Pages 329-334"},"PeriodicalIF":4.1,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling low-latency digital twins for large-scale UAV networks using MQTT-based communication framework 使用基于mqtt的通信框架为大规模无人机网络启用低延迟数字孪生

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-01-14 DOI: 10.1016/j.icte.2025.01.001

Dohyun An , Hyeontae Joo , Hwangnam Kim

{"title":"Enabling low-latency digital twins for large-scale UAV networks using MQTT-based communication framework","authors":"Dohyun An , Hyeontae Joo , Hwangnam Kim","doi":"10.1016/j.icte.2025.01.001","DOIUrl":"10.1016/j.icte.2025.01.001","url":null,"abstract":"<div><div>In recent developments within large-scale Unmanned Aerial Vehicle (UAV) networking, ensuring the accuracy and timeliness of real-time status updates has become increasingly critical. This paper proposes a new Centralized Message Queuing Telemetry Transport (MQTT)-based Communication framework(CMQC) specifically designed to improve real-time data transmission within a simulated large-scale UAV network. The primary objective of this study is to demonstrate the superior performance of the proposed framework in comparison to traditional communication methods such as MQTT, Transmission Control Protocol (TCP) socket-based communication, and Constrained Application Protocol (CoAP). Key performance metrics such as inter-process time interval, total completion time, jitter, and Age of Information (AoI) are evaluated to substantiate these improvements. Experimental results confirm that the MQTT-based framework significantly enhances real-time update capabilities, reduces latency, and improves overall network efficiency. These findings suggest that the proposed framework is a viable solution for enabling low-latency digital twins in large-scale UAV networks.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 2","pages":"Pages 264-269"},"PeriodicalIF":4.1,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Power Allocation for Beam Alignment in Millimeter Wave IoT Communications 毫米波物联网通信中波束对准的功率分配

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-01-03 DOI: 10.1016/j.icte.2024.12.006

Jiho Song

{"title":"Power Allocation for Beam Alignment in Millimeter Wave IoT Communications","authors":"Jiho Song","doi":"10.1016/j.icte.2024.12.006","DOIUrl":"10.1016/j.icte.2024.12.006","url":null,"abstract":"<div><div>The lower transmission power capability of internet of things (IoT) devices in millimeter-wave (mmWave) networks necessitates the activation of beamforming techniques by employing multiple antennas. To ensure robust directional transmissions at mmWave frequencies, it is essential to accurately align narrow beams with the dominant channel direction. However, directional communications necessitate aligning the beam with the channel direction, resulting in significant transmit power consumption. We aim to reduce the transmit power required to achieve a desired beam-alignment performance, taking into account the power limitations of IoT devices.We propose an analytical framework for optimizing resource utilization in the hierarchical beam alignment algorithm, designed to progressively reduce the size of the beamformer candidate set. Based on the analytical studies, we develop a resource allocation algorithm that determines the optimal transmit power and constructs the optimal set of beamformer candidates at each beam alignment stage. Numerical simulations demonstrate that the proposed power allocation algorithm effectively reduces the transmit power to meet the performance targets for IoT networks.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 2","pages":"Pages 305-309"},"PeriodicalIF":4.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedWT: Federated Learning with Minimum Spanning Tree-based Weighted Tree Aggregation for UAV networks FedWT：基于最小生成树加权树聚合的无人机网络联邦学习

IF 4.1 3区计算机科学

ICT Express Pub Date : 2024-12-24 DOI: 10.1016/j.icte.2024.12.005

Geonhui Kim, Jiha Kim, Yongho Kim, Hwan Kim, Hyunhee Park

{"title":"FedWT: Federated Learning with Minimum Spanning Tree-based Weighted Tree Aggregation for UAV networks","authors":"Geonhui Kim, Jiha Kim, Yongho Kim, Hwan Kim, Hyunhee Park","doi":"10.1016/j.icte.2024.12.005","DOIUrl":"10.1016/j.icte.2024.12.005","url":null,"abstract":"<div><div>In recent years, advances in communication technology, hardware, and deep learning have led to significant advancements in Unmanned Aerial Vehicles (UAVs). However, applying federated learning in UAV environments is challenging due to network instability and dependency on central server. In this paper, the Federated Learning with Minimum Spanning Tree (MST)-based Weighted Tree Aggregation (FedWT) is proposed to address transmission failure, delayed model updates, and single point of failure problems. FedWT uses MST to minimize model exchange during local aggregation, and addresses data heterogeneity through dynamic weighted averaging. It includes a decentralized federated learning method for UAVs, model sharing path scheduling to reduce communication overhead, and a flexible weight-based aggregation approach to handle data heterogeneity. Simulation results demonstrate the superior performance and communication efficiency of FedWT compared to traditional federated learning methods. In particular, FedWT achieves up to 2% higher prediction accuracy and reduces communication traffic by up to 84.98% in highly heterogeneous data scenarios. Under different network topologies, FedWT consistently outperformed other federated learning methods in terms of learning accuracy and loss reduction. Future work will optimize the dynamic weight adjustment and validate the robustness of FedWT under various scenarios.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 2","pages":"Pages 275-280"},"PeriodicalIF":4.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GMT: Gzip-based Memory-efficient Time-series classification GMT：基于gzip的内存效率时间序列分类

IF 4.1 3区计算机科学

ICT Express Pub Date : 2024-12-13 DOI: 10.1016/j.icte.2024.12.003

Sungmin Lee , Kichang Lee , JaeYeon Park , JeongGil Ko

引用次数: 0

Optimizing proactive content caching with mobility aware deep reinforcement & asynchronous federate learning in VEC 在VEC中优化具有移动性感知的深度强化和异步联邦学习的主动内容缓存

IF 4.1 3区计算机科学

ICT Express Pub Date : 2024-12-02 DOI: 10.1016/j.icte.2024.11.006

Afsana Kabir Sinthia, Nosin Ibna Mahbub, Eui-Nam Huh

{"title":"Optimizing proactive content caching with mobility aware deep reinforcement & asynchronous federate learning in VEC","authors":"Afsana Kabir Sinthia, Nosin Ibna Mahbub, Eui-Nam Huh","doi":"10.1016/j.icte.2024.11.006","DOIUrl":"10.1016/j.icte.2024.11.006","url":null,"abstract":"<div><div>Edge caching in the Internet of Vehicles (IoV) can reduce backhaul strain and content access delay. However, due to the constant changes in vehicle requests, offloading applications to edge servers is crucial for efficiently anticipating and caching popular content. Additionally, conventional data-sharing techniques are inadequate for this task due to their inability to preserve the privacy of vehicular users (VU). To overcome these issues, we propose a cooperative proactive content caching system incorporating Asynchronous federated learning and Deep reinforcement learning named <strong>PCAD</strong> that leverages the strengths of Dueling Deep Q-Networks and Prioritized Experience Replay in vehicular edge computing. PCAD lowers the latency of content access by prefetching contents that are popular beforehand caching them on edge nodes and cutting the waiting time for every vehicle to complete training as well as uploading local models before updating the global model. Additionally, we investigate intelligent caching decisions based on content prediction. Comprehensive experimental evaluations indicate that our proposed approach significantly outperforms existing benchmark caching techniques. More specifically, our suggested approach works better than DDQN, c-<span><math><mi>ϵ</mi></math></span>-greedy, and PCAD without DRL methods and the cache hit rate improves by approximately 4.25%, 11.23%, and 25.82%, respectively, as the cache capacity hits 400 MB.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 2","pages":"Pages 293-298"},"PeriodicalIF":4.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Instantaneous received signal strength-based sensor activation for energy-efficient distributed cooperative sensor networks 基于瞬时接收信号强度的传感器激活，实现高能效分布式合作传感器网络

IF 4.1 3区计算机科学

ICT Express Pub Date : 2024-12-01 DOI: 10.1016/j.icte.2024.05.004

Jingon Joung , Jian Zhao

引用次数: 0