Quality of Experience Optimization for AR Service in an MEC Federation System by Huong Mai do, Tuan Phong Tran, Myungsik Yoo

“…Augmented reality (AR) in the internet of things requires ultra-low latency, high-resolution video, and fairness in multi-user environments, which pose challenges for traditional cloud and edge computing. To address this shortcoming, we studied AR subtask offloading and resource allocation in a multi-hop, multi-access edge computing federation. …”
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LEO computing satellite constellation design for heterogeneous QoS requirements by PENG Yuming, SUN Yijing, DI Boya

“…Ultra-dense low earth orbit satellite constellations have emerged as a promising solution to address the limited coverage area of terrestrial networks and the insufficient computing capabilities of user terminals. …”
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Empowering drones in vehicular network through fog computing and blockchain technology. by Shivani Wadhwa, Divya Gupta, Shalli Rani, Maha Driss, Wadii Boulila

“…This research paper addresses this gap by proposing a safe, reliable, and real-time drone network service architecture, emphasizing collaboration with fog computing. …”
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Utility-driven virtual machine allocation in edge cloud environments using a partheno-genetic algorithm by Jie Cao, Cuicui Zhang, Ping Qi, Kekun Hu

“…To address these challenges, this paper proposes a novel virtual machine allocation framework designed to maximize the utility of edge cloud service provisioning under QoS constraints. …”
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Flexible Edge-AI Software Execution Architecture Based on Cloud-Connected Incremental Learning by Myeongjin Kang, Daejin Park

“…The proposed architecture updates weights and models through the cloud to adapt to external changes in edge AI in runtime. Also, offloading resource-intensive learning stages to the cloud with an event-driven approach reduces the load on edge. …”
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Fast and fair split computing for accelerating deep neural network (DNN) inference by Dongju Cha, Jaewook Lee, Daeyoung Jung, Sangheon Pack

“…To address these issues, we formulate an optimization problem to determine one or two split points that minimize inference latency while ensuring fair offloading among MDs. …”
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Virtual Node-Driven Cloud–Edge Collaborative Resource Scheduling for Surveillance with Visual Sensors by Xinyang Gu, Zhansheng Duan, Guangyuan Ye, Zhenjun Chang

“…Experimental results show that all the scheduling algorithms can effectively address the challenge of offloading multiple priority tasks under resource constraints. …”
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Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networks by Wei Zheng, Pengshan Ren, Qing Li

“…Particularly, the proposed optimization framework jointly optimizes UAV trajectories, subcarrier allocation, IRS reflection coefficients, task offloading ratios, task priorities and contextual relevance to maximize semantic utility and system energy efficiency while dynamically ensuring task demands. …”
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Lightweight and delay-aware resource management scheme in smart grid IoT networks by Danni Liu, Shengda Wang, Xiaofu Sun, Chunyan An, Weijia Su, Jiakang Liu

“…However, the challenge of meeting online service requirements within the constraints of limited resources and strict task processing delay persists. To address this, we propose an online delay-aware online mobile computation offloading scheme consisting of four crucial algorithms, which firstly classify users into heterogeneous networks and then design the online resource allocation methods on the macro base station (MBS) and small base stations (SBSs), respectively, and finally design the updating strategy of the control parameters to ensure the load balancing among bases. …”
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EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks by Sakshi Patni, Joohyung Lee

“…EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. …”
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An Intelligent Distributed Channel Selection Framework with Hybrid Mode Selection for Interference Mitigation in D2D based 5G Networks by Abdullilah A. Alotaibi, Salman A. AlQahtani

“…It is likely to aid in satisfying increasing capacity and effectively offloading traffic from the BS by distributing the transmission between D2D users from one side and cellular users and the BS from the other side. …”
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SSL-XIoMT: Secure, Scalable, and Lightweight Cross-Domain IoMT Sharing With SSI and ZKP Authentication by Lyhour Hak, Somchart Fugkeaw

“…Additionally, we optimize CP-ABE by offloading complex computations to fog nodes, which pre-compute attributes and logical operations. …”
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Collaborative Federated Learning of Unmanned Aerial Vehicles in Space–Air–Ground Integrated Network by Huibo Li, Peng Gong, Siqi Li, Weidong Wang, Yu Liu, Xiang Gao, Dapeng Oliver Wu, Duk Kyung Kim, Guangwei Zhang, Jihao Zhang

“…In this paper, a collaborative federated learning (FL) scheme based on device-to-device (D2D) communication in unmanned aerial vehicle (UAV)-assisted SAGIN is proposed to address the issue of heterogeneity. Aerial devices with limited communication and computation resource can offload partial nonprivacy data samples to proximity D2D pair, which can assist to train FL models. …”
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Adaptive Resource Allocation and Mode Switching for D2D Networks With Imperfect CSI in AGV-Based Factory Automation by Safiu A. Gbadamosi, Gerhard P. Hancke, Adnan M. Abu-Mahfouz

“…Device-to-device (D2D) technology can enhance industrial network performance by offloading traffic and improving resource utilization. …”
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Design of an Immersive Basketball Tactical Training System Based on Digital Twins and Federated Learning by Xiongce Lv, Ye Tao, Yifan Zhang, Yang Xue

“…To address the challenges of dynamic adversarial scenario modeling distortion, insufficient cross-institutional data privacy protection, and simplistic evaluation systems in collegiate basketball tactical education, this study proposes and validates an immersive instructional system integrating digital twin and federated learning technologies. …”
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Beyond boundaries a hybrid cellular potts and particle swarm optimization model for energy and latency optimization in edge computing by Dinesh Sahu, Nidhi, Shiv Prakash, Priyanshu Sinha, Tiansheng Yang, Rajkumar Singh Rathore, Lu Wang

“…These challenges has been addressed in this paper with the introduction of a new integrated method that assumes the Cellular Potts Model and Particle Swarm Optimization. …”
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Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular Environments by Samuel Thornton, Nithin Santhanam, Rajeev Chhajer, Sujit Dey

“…However, achieving real-time collaborative perception is a difficult task due to the dynamic availability of vehicular sensing and computing and the highly variable nature of vehicular communications. To address these challenges, we propose a Heterogeneous Adaptive Collaborative Perception (HAdCoP) framework which utilizes a Context-aware Latency Prediction Network (CaLPeN) to intelligently select which vehicles should transmit their sensor data, the specific individual and collaborative perception tasks, and the amount of computational offloading that should be utilized given information about the current state of the environment. …”
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From 6G to <i>SeaX-G</i>: Integrated 6G TN/NTN for AI-Assisted Maritime Communications—Architecture, Enablers, and Optimization Problems by Anastasios Giannopoulos, Panagiotis Gkonis, Alexandros Kalafatelis, Nikolaos Nomikos, Sotirios Spantideas, Panagiotis Trakadas, Theodoros Syriopoulos

“…This paper presents a comprehensive review of how 6G-enabling technologies can be adapted to address the unique challenges of Maritime Communication Networks (MCNs). …”
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Loka: A Cross-Platform Virtual Reality Streaming Framework for the Metaverse by Hsiao-Wen Kao, Yan-Cyuan Chen, Eric Hsiao-Kuang Wu, Shih-Ching Yeh, Shih-Chun Kao

“…VR streaming offers a potential solution by offloading computational tasks to remote servers, enabling high-quality VR experiences on lower-end devices and enhancing accessibility to a broader audience. …”
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Unified 3D Networks: Architecture, Challenges, Recent Results, and Future Opportunities by Mohamed Rihan, Dirk Wubben, Abhipshito Bhattacharya, Marina Petrova, Xiaopeng Yuan, Anke Schmeink, Amina Fellan, Shreya Tayade, Mervat Zarour, Daniel Lindenschmitt, Hans Schotten, Armin Dekorsy

“…Proposals encompass federated learning mechanisms, advanced beamforming techniques, and energy-efficient computational offloading strategies, aimed at enhancing network performance and resilience. …”
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