Mobile Networks and Applications最新文献_第10页

Emo-AEN: A Lightweight Network for Brand Image Design Based on Aesthetic Evaluation Emo-AEN：基于审美评价的品牌形象设计轻量级网络

Mobile Networks and Applications Pub Date : 2024-04-01 DOI: 10.1007/s11036-024-02314-y

Honglei Cheng, Haorui Yi, Guipeng Lan, Shuai Xiao

引用次数: 0

DRL Empowered On-policy and Off-policy ABR for 5G Mobile Ultra-HD Video Delivery DRL 为 5G 移动超高清视频传输增强策略内和策略外 ABR 功能

Mobile Networks and Applications Pub Date : 2024-03-28 DOI: 10.1007/s11036-024-02311-1

{"title":"DRL Empowered On-policy and Off-policy ABR for 5G Mobile Ultra-HD Video Delivery","authors":"","doi":"10.1007/s11036-024-02311-1","DOIUrl":"https://doi.org/10.1007/s11036-024-02311-1","url":null,"abstract":"<h3>Abstract</h3> Fifth generation (5G) and beyond 5G networks support high-throughput ultra-high definition (UHD) video applications. This paper examines the use of dynamic adaptive streaming over HTTP (DASH) to deliver UHD videos from servers to 5G-capable devices. Due to the dynamic network conditions of wireless networks, it is particularly challenging to provide a high quality of experience (QoE) for UHD video delivery. Consequently, adaptive bit rate (ABR) algorithms are developed to adapt the video bit rate to the network conditions. To improve QoE, several ABR algorithms are developed, the majority of which are based on predetermined rules. Therefore, they do not apply to a broad variety of network conditions. Recent research has shown that ABR algorithms powered by deep reinforcement learning (DRL) based vanilla asynchronous advantage actor-critic (A3C) methods are more effective at generalizing to different network conditions. However, they have some limitations, such as a lag between behavior and target policies, sample inefficiency, and sensitivity to the environment’s randomness. In this paper, we propose the design and implementation of two DRL-empowered ABR algorithms: (i) on-policy proximal policy optimization adaptive bit rate (PPO-ABR), and (ii) off-policy soft-actor critic adaptive bit rate (SAC-ABR). We evaluate the proposed algorithms using 5G traces from the Lumos 5G dataset and show that by utilizing specific properties of on-policy and off-policy methods, our proposed methods perform much better than vanilla A3C for different variations of QoE metrics.","PeriodicalId":501103,"journal":{"name":"Mobile Networks and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140323186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Alzheimer's detection by Artificial Bee Colony and Convolutional Neural Network at Mobile Environment 人工蜂群和卷积神经网络在移动环境中检测阿尔茨海默氏症

Mobile Networks and Applications Pub Date : 2024-03-26 DOI: 10.1007/s11036-024-02313-z

Dan Shan, Fanfeng Shi, Tianzhi Le

引用次数: 0

Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks 利用双深度 Q 网络在智能工厂中部署雾计算和边缘计算，实现分布式灵活作业车间调度

Mobile Networks and Applications Pub Date : 2024-03-25 DOI: 10.1007/s11036-024-02302-2

Chun-Cheng Lin, Yi-Chun Peng, Zhen-Yin Annie Chen, Yu-Hong Fan, Hui-Hsin Chin

{"title":"Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks","authors":"Chun-Cheng Lin, Yi-Chun Peng, Zhen-Yin Annie Chen, Yu-Hong Fan, Hui-Hsin Chin","doi":"10.1007/s11036-024-02302-2","DOIUrl":"https://doi.org/10.1007/s11036-024-02302-2","url":null,"abstract":"Flexible job shop scheduling (FJSP) has garnered enormous attention within the realm of smart manufacturing, where, beyond job sequencing, the selection of machines holds considerable importance. As smart factories progress with the Internet of things (IoT) and cyber-physical systems (CPS), scheduling methodologies are advancing towards intelligent decentralization. However, with the expansion of factories, conventional cloud computing struggles to manage the substantial influx of data. To tackle this issue, this work incorporates a fog computing and edge computing framework into the distributed FJSP workstations. In this framework, the workstations each of which consists of multiple machines are categorized based on the different nature of the accommodated machines, and operate independently to reduce unnecessary information transmission, in which each machine is equipped with edge computing capacity. The fusion of fog computing and edge computing allows for the offloading of computational tasks from cloud computing, effectively reducing latency. While previous solutions for FJSP have predominantly relied on linear programming or metaheuristic algorithms, this work proposed a novel distributed approach based on a dual deep Q networks (dual DQN) architecture, integrating deep learning (DL) with reinforcement learning (RL). Within the cloud center, the initial neural network determines the machine selection rules for fog computing, while the secondary neural network decides the job dispatching rules for edge computing devices. Edge computing devices execute the schedule and provide feedback to the cloud, which refines the results through an iterative training process, so that to minimize the makespan. The experimental findings indicate that employing dual DQNs outperforms the methods of utilizing only one single machine selection rule.","PeriodicalId":501103,"journal":{"name":"Mobile Networks and Applications","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Analysis of Spatially Multiplexed Hybrid BPSK-MPPM Modulated Optical Interconnect System Using Triangular Index Multicore Fiber 使用三角索引多芯光纤的空间复用混合 BPSK-MPPM 调制光互连系统的性能分析

Mobile Networks and Applications Pub Date : 2024-03-19 DOI: 10.1007/s11036-024-02309-9

Ankita Kumari, Prakash Pareek, Jitendra K. Mishra

引用次数: 0

Efficient Power Prediction for Intersatellite Optical Wireless Communication System Using Artificial Neural Network 利用人工神经网络进行卫星间光学无线通信系统的高效功率预测

Mobile Networks and Applications Pub Date : 2024-03-16 DOI: 10.1007/s11036-024-02308-w

Subhash Suman, Ayush Kumar Singh, Prakash Pareek, Jitendra K. Mishra

{"title":"Efficient Power Prediction for Intersatellite Optical Wireless Communication System Using Artificial Neural Network","authors":"Subhash Suman, Ayush Kumar Singh, Prakash Pareek, Jitendra K. Mishra","doi":"10.1007/s11036-024-02308-w","DOIUrl":"https://doi.org/10.1007/s11036-024-02308-w","url":null,"abstract":"Intersatellite optical wireless communication (IsOWC) system has garnered global attention for facilitating high-speed data transfer between two space-based satellites. However, accurately predicting received or output signal power in a lower earth orbit trajectory is challenging due to factors such as background light, scintillation, pointing error, and optical crosstalk. To overcome this problem, a technique based on artificial neural networks (ANN) is proposed to enhance the efficiency of received signal power in the IsOWC system. The input features for an IsOWC system include propagation distance, scintillation attenuation, wavelength, pointing error, and input power, ranging from 1 to 25 km, 0 to 6 dB, 800 to 1600 nm, 0 to 1 µradian, and 0 to 4.77 dBm, respectively. The output feature i.e., received signal power, ranges from − 100 to 34.99 dBm. Before training, exploratory data analysis is performed on 2100 datasets generated by 16-quadrature amplitude modulation based IsOWC system. Furthermore, an ANN model is trained, resulting in a low mean squared error (MSE) of 4.8 × 10− 6 compared to other machine learning model. The impact of hyperparameter tuning on the MSE curve is rigorously discussed. Additionally, the scatter plot between true power and ANN power prediction, along with an error density plot analysis are thoroughly explored. The proposed technique is intended to efficiently predict the received signal power and find applications in terrestrial communication, military operations, 5G beyond communication, underwater communication, and more for global internet connectivity.","PeriodicalId":501103,"journal":{"name":"Mobile Networks and Applications","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140146908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-DGI: Multi-head Pooling Deep Graph Infomax for Human Activity Recognition Multi-DGI：用于人类活动识别的多头汇集深度图 Infomax

Mobile Networks and Applications Pub Date : 2024-03-13 DOI: 10.1007/s11036-024-02306-y

Yifan Chen, Haiqi Zhu, Zhiyuan Chen

{"title":"Multi-DGI: Multi-head Pooling Deep Graph Infomax for Human Activity Recognition","authors":"Yifan Chen, Haiqi Zhu, Zhiyuan Chen","doi":"10.1007/s11036-024-02306-y","DOIUrl":"https://doi.org/10.1007/s11036-024-02306-y","url":null,"abstract":"Human Activity Recognition (HAR) is a crucial research domain with substantial real-world implications. Despite the extensive application of machine learning techniques in various domains, most traditional models neglect the inherent spatio-temporal relationships within time-series data. To address this limitation, we propose an unsupervised Graph Representation Learning (GRL) model named Multi-head Pooling Deep Graph Infomax (Multi-DGI), which is applied to reveal the spatio-temporal patterns from the graph-structured HAR data. By employing an adaptive Multi-head Pooling mechanism, Multi-DGI captures comprehensive graph summaries, furnishing general embeddings for downstream classifiers, thereby reducing dependence on graph constructions. Using the UCI WISDM dataset and three basic graph construction methods, Multi-DGI delivers a minimum enhancement of 2.9%, 1.0%, 7.5%, and 6.4% in Accuracy, Precision, Recall, and Macro-F1 scores, respectively. The demonstrated robustness of Multi-DGI in extracting intricate patterns from rudimentary graphs reduces the dependence of GRL on high-quality graphs, thereby broadening its applicability in time-series analysis. Our code and data are available at https://github.com/AnguoCYF/Multi-DGI.","PeriodicalId":501103,"journal":{"name":"Mobile Networks and Applications","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140124321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CSO-CNN: Cat Swarm Optimization-guided Convolutional Neural Network for Mobile Detection of Breast Cancer CSO-CNN：用于乳腺癌移动检测的猫群优化引导的卷积神经网络

Mobile Networks and Applications Pub Date : 2024-03-05 DOI: 10.1007/s11036-024-02298-9

Xiaoyan Jiang, Zuojin Hu, Zhaozhao Xu

{"title":"CSO-CNN: Cat Swarm Optimization-guided Convolutional Neural Network for Mobile Detection of Breast Cancer","authors":"Xiaoyan Jiang, Zuojin Hu, Zhaozhao Xu","doi":"10.1007/s11036-024-02298-9","DOIUrl":"https://doi.org/10.1007/s11036-024-02298-9","url":null,"abstract":"Breast cancer has become the most common cancer in the world. Early diagnosis and treatment can greatly improve the survival rate of breast cancer patients. Computer diagnostic technology based on convolutional neural networks (CNNs) can assist in detecting breast cancer based on medical images, effectively improving detection accuracy. Hyperparameters in CNN will affect model performance, so hyperparameter tuning is necessary for model training. However, traditional tuning methods can get stuck in local minimums. Therefore, the weights and biases of artificial neural networks are usually trained using global optimization algorithms. Our research introduces cat swarm optimization (CSO) to construct a cat swarm optimization-guided convolutional neural network (CSO-CNN). The model can quickly obtain the optimal combination of hyperparameters and stably get closer to the global optimal. The statistical results of CSO-CNN obtained a sensitivity of 93.50% ± 2.42%, a specificity of 92.20% ± 3.29%, a precision of 92.35% ± 3.01%, an accuracy of 92.85% ± 2.49%, an F1-score of 92.91% ± 2.44%, Matthews correlation coefficient of 85.74% ± 4.94%, and Fowlkes-Mallows index was 92.92% ± 2.43%. Our CSO-CNN algorithm is superior to five state-of-the-art methods. In addition, we tested the CSO-CNN algorithm on the local computer to simulate the mobile environment and confirmed that the algorithm can be transplanted to the network servers.","PeriodicalId":501103,"journal":{"name":"Mobile Networks and Applications","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140032568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mobile Diagnosis of COVID-19 by Biogeography-based Optimization-guided CNN 基于生物地理学优化引导的 CNN 对 COVID-19 进行移动诊断

Mobile Networks and Applications Pub Date : 2024-03-04 DOI: 10.1007/s11036-024-02301-3

Xue Han, Zuojin Hu

{"title":"Mobile Diagnosis of COVID-19 by Biogeography-based Optimization-guided CNN","authors":"Xue Han, Zuojin Hu","doi":"10.1007/s11036-024-02301-3","DOIUrl":"https://doi.org/10.1007/s11036-024-02301-3","url":null,"abstract":"Since 2019, COVID-19 has profoundly impacted human health around the world. COVID-19 is extremely contagious, so fast automated diagnosis is necessary. In the field of COVID-19 detection, there are many studies based on convolutional neural networks (CNN). This article introduces the Biogeography-based Optimization (BBO) algorithm to tune three hyperparameters of CNN: ({beta }_{1}) for calculating the exponential decay rate of the past gradient, ({beta }_{2}) for calculating the exponential decay rate of the square of the past gradient and the learning rate (mathrm{alpha }). A mobile COVID-19 diagnosis application based on BBO-CNN is developed. The sensitivity of BBO-CNN is 94.46% ± 1.45%, the specificity is 93.72% ± 1.86%, the precision is 93.80% ± 1.64%, the accuracy is 94.09% ± 0.92%, the F1-score is 94.11% ± 0.88%, the Matthews Correlation Coefficient (MCC) is 88.21% ± 1.81%, and the Fowlkes-Mallows Index (FMI) is 94.12% ± 0.88%. Compared with six other deep learning-based state-of-the-art methods, BBO-CNN performs superior. BBO-CNN automates COVID-19 detection. The developed mobile diagnosis application helps to diagnose COVID-19 quickly in remote areas where radiologists are scarce.","PeriodicalId":501103,"journal":{"name":"Mobile Networks and Applications","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140032480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction: A Joint Caching and Offloading Strategy Using Reinforcement Learning for Multi-access Edge Computing Users 更正：利用强化学习为多接入边缘计算用户提供联合缓存和卸载策略

Mobile Networks and Applications Pub Date : 2024-02-29 DOI: 10.1007/s11036-024-02295-y

Yuanhong Yuan, Wei Su, Gaofeng Hong, Haoru Li, Chang Wang

引用次数: 0