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distributed realtime rendering in decentralized network for mobile web augmented reality

FUTURE GENERATION computer systems-THE international JOURNAL OF ESCIENCE 2024年 158卷 530-544页

作者： Zhang, Huabing Li, Liang Lu, Qiong Yue, Yi Huang, Yakun Dustdar, Schahram Key Lab Film & TV Media Technol Zhejiang Prov Hangzhou 310018 Zhejiang Peoples R China Commun Univ Zhejiang Sch Media Engn Hangzhou 310018 Zhejiang Peoples R China China Unicom Res Inst Beijing Peoples R China Natl Engn Res Ctr Next Generat Internet Broadband Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Tech Univ Wien Distributed Syst Grp A-1040 Vienna Austria

Low-latency, real-time rendering of 3D objects is critical for mobile web-based augmented reality (MWAR) applications. While cloud-based or edge-based server rendering offloading can reduce the latency for mobile devices, a high volume of user requests in user aggregation scenarios can overload servers and transmission channels. It can result in a poor user experience due to resource consumption and high latency. This paper presents a decentralized and collaborative real-time rendering offloading network architecture (CRCDnet) to address this issue. CRCDnet makes contributions in the following three areas: (1) In the user aggregation scenarios, mobile devices that perform the same services are used as service nodes to perform the offloading of rendering computing and a collaborative rendering computing network is established. (2) For data exchange in decentralized networks, a data sharing middle layer based on blockchain key indexes separates sensitive service data and ensures a secure, reliable exchange mechanism and efficient data exchange. (3) A data request and computing offloading scheduling approach is proposed for the collaborative rendering computing network to optimize the rendering computation delay.

关键词： Mobile web-based augment reality Real-time rendering computing Decentralized network distributed collaborative computing Mobile computing Device-to-Device

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Explaining Graph-based Decision Learning for Autonomous Exploration in Unknown Environments 22

Explaining Graph-based Decision Learning for Autonomous Expl...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Chen, Liping Duan, Shihong Xu, Cheng Zhang, Heng Wang, Ran Computer Science and Technology Beijing China

ISBN: (纸本)9798331509712

In unknown emergency environments, agents use range sensors for localization and mapping, making optimal decisions among numerous uncertain candidate target locations. Exploration graphs can significantly reduce state space dimensionality. By employing deep reinforcement learning models with graph neural networks to predict actions, the agents can autonomously explore unknown environments with varying scales of landmarks. However, the opacity of strategy learning and prediction complicates understanding temporal and spatial correlations of state transitions and state-action relationships. To address this, we propose a multidimensional visualization framework to analyze high-dimensional state-action vector correlations. A post-hoc explanation tool based on feature attribution was designed to create importance sub-graphs, describing causal effects among nodes and explaining the agent's decision-making. The framework's effectiveness was validated using correlation analysis, node uncertainty entropy reduction, and navigation performance metrics. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comparative Analysis of Breast Cancer Classification using Ensemble and Multi Classifier Algorithms

A Comparative Analysis of Breast Cancer Classification using...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Jain, Garima Jain, Sanat Phulre, Ajay Kumar Sharma, Rahul Joshi, Suneet School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Sehore India School of Computer Science and Engineering Vit Bhopal University Sehore India School of Computer Science and Engineering Parul University Gujarat Vadodara India

ISBN: (纸本)9798350375442

To help and monitor the patients, the initial diagnosis and type prediction of cancer should become mandatory in cancer research. Machine learning (ML) approaches are being studied and evaluated by several research teams from the biomedical sector due to the relevance of classifying cancer patients into two risk groups. It has been suggested that base classifiers be used in conjunction with ensemble methods to predict breast cancer. This work, provide detailed forecasts using data on breast cancer in a novel setting. In order to create precise predictions, this research investigates the many classification-based data mining techniques. Additionally, this study evaluates the dataset using a variety of classifiers to forecast which model will perform the best. The cancer dataset worked upon is the Wisconsin Dataset with 569 instances. This Dataset is given to classifiers like SVM, J48, REP tree and Random Forest after pre-processing and cross validation is applied later, new models are tested subsequently. The results were assessed across multiple parameters, and the analysis indicates that among all the classifiers, the REP tree focusing on node caps delivers the most in-depth predictions and achieves the highest accuracy. This model outperforms other approaches, including Boosting with Random Forest (node caps), Boosting with Random Forest (INV nodes), and Boosting with J48, in terms of yielding superior and precise results. © 2024 IEEE.

关键词： Lung cancer

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Ambient Light Impact on Power Reception in Outdoor VLC: A Distance-Based Analysis

Ambient Light Impact on Power Reception in Outdoor VLC: A Di...

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2024 international symposium on networks, computers and Communications, ISNCC 2024

作者： Ahmed, Sayeed Yeap, Tet Ahmed, Sabbir Kiringa, Iluju School of Electrical Engineering and Computer Science University of Ottawa ON Canada School of Applied Computer Science and It Conestoga College Ital ON Canada

ISBN: (纸本)9798350364910

Environmental sustainability is crucial for ensuring the long-term health and well-being of our planet and its in-habitants. Precise navigation of autonomous and semi-autonomous vehicles in agricultural usage, therefore, becomes a crucial com-ponent in ensuring such sustainability. It is no surprise that the use of visible light communication (VLC) in navigation is steadily expanding, driven primarily by its remarkable precision in indoor environments. VLC has significant potential as a su-perior alternative to traditional navigation systems, including the global positioning system (GPS), particularly in dynamic outdoor environments. However, despite its indoor success, addressing the challenges posed by ambient light remains critical, particularly in outdoor scenarios where natural lighting conditions vary substan-tially. This article investigates the complex relationship between ambient light and outdoor VLC technology. The study aims to quantify the impact of ambient light on the receiving power level at varying distances in outdoor VLC systems by employing advanced simulation and modelling techniques. It provides a detailed analysis of the effects of sunlight, offering valuable insights for optimizing the positioning of light transmitters and receivers to enhance performance and reliability in real-world applications. The results discussed have significant implications for advancing outdoor VLC technology. They provide a pathway for creating more resilient and efficient navigation systems that function seamlessly across various environmental conditions. © 2024 IEEE.

关键词： Sustainable development

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Reinforcement Learning based Edge-Assisted Inference for Maritime UAV networks 22

Reinforcement Learning based Edge-Assisted Inference for Mar...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Wang, Chuxuan Li, Jieling Ye, Liqing Liu, Yu Xiao, Liang Inst. of Artificial Intelligence Xiamen University Xiamen China Xiamen University Dept. of Information & Communication Engineering Xiamen China Xiamen University Inst. of Artificial Intelligence Dept. of Information & Communication Engineering Xiamen China

ISBN: (纸本)9798331509712

Edge-assisted inference that enables each unmanned aerial vehicle (UAV) to offload marine tasks for maritime applications such as target tracking and data collection, but the inference speed and energy efficiency are affected by sea surface movement and wave occlusions under broad area maritime environment with instability channel. In this paper, we propose an RL-based edge-assisted inference scheme for maritime UAV networks to optimize the deep neural networks partition point, the transmit power and the collaborative edge server to enhance the utility as the weighted sum of the data-related energy consumption and the inference latency. Based on the average sea wave height, the channel gain, the number of marine tasks and the battery level, the self-correcting mechanism makes a trade-off between the overestimation and the underestimation in collaborative inference policy without additional computational cost. The bounds of the data-related energy consumption and the inference latency are derived under the specific data rate and inference computation amounts. Simulation results show the effectiveness of the proposed edge-assisted inference scheme. © 2024 IEEE.

关键词： Energy utilization

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AIO: Automating I/O Optimization Pipeline for Data-Intensive Applications in HPC 22

AIO: Automating I/O Optimization Pipeline for Data-Intensive...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Wang, Wanxin Wu, Huijun Yang, Lihua Liu, Zhangyu Zheng, Bin Changsha University of Science and Technology School of Computer and Communication Engineering China National University of Defense Technology College of Computer Science and Technology China

ISBN: (纸本)9798331509712

High-performance computing (HPC) systems has entered the exascale era, but I/O performance has lagged behind due to storage hardware limitations, creating a "storage wall effect"that hinders HPC systems full potential. Modern HPC storage systems employ a layered storage architecture with local node storage, burst buffers, and global parallel file systems to improve I/O performance. However, application workloads vary, and fast storage layers may not always outperform global parallel file systems, while a static storage software stack configuration may not achieve optimal performance across different applications. Traditional I/O optimization is time-consuming, laborintensive, and prone to human error. To address this issue, this paper proposes AIO, a data-driven I/O optimization method. AIO automatically selects the optimal storage hierarchy and software stack based on the task's I/O patterns, and further enhances performance by identifying and applying the most efficient configuration settings. We evaluated AIO with eight applications in a cluster that features shared burst buffers, local burst buffers, and a global shared file system. The experiments demonstrate that AIO effectively optimizes I/O performance, achieving an overall speedup of 2.74. © 2024 IEEE.

关键词： Buffer storage

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A Pruned Multi-Scale Skip Connected and Asymmetric-Convolution-Based U-Net Model for Land Cover Classification

A Pruned Multi-Scale Skip Connected and Asymmetric-Convoluti...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Almusawi, Muntather Parameshachari, B.D. Sunil, G. Reddy Muvva, Vijaya Bhaskar Siri, Dharmapuri The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Sr University Department of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences -Shinas College of Computing and Information Sciences Department of Information Technology Shinas Oman Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

Recently, land cover classification became an evolutionary study in Remote Sensing (RS) satellite interpretations. While extracting features from satellite images, spatial information causes the overfitting issue. In addition, computational complexity is occurring in land cover segmentation due to the usage of complex architectures. Thus, in this research, Multi-scale Skip connected and Asymmetric-Convolution-based U-Net (MACUNet) with pruning method is introduced to reduce the computational complexity. To reduce the overfitting, Batch Normalization (BN) layer is employed, simultaneously 3D Convolutional Neural Network (3D CNN) for feature extraction. Initially, the input is taken as satellite images from the GID dataset, where spatial and temporal features are extracted using 3D CNN model with BN layer. After that, these features are given as input to the MACUNet method to improve segmentation performance. Then, classification is done with class_num value utilization based on identified pixel region maps and classified into six classes such as build up, forest, water, farmland, meadows and others. From the results, the proposed pruned-MACUNet model produces better results than existing GeohashNet in terms of Mean Intersection over Union (MIoU) as 80.45% and f1-score as 90.23% respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning for Enhanced Spectrum Sensing in Cognitive Radio networks with Blockchain-based Security 3

Deep Learning for Enhanced Spectrum Sensing in Cognitive Rad...

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3rd IEEE international Conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Solanki, Surendra Brahma, Banalaxmi Manipal University Jaipur Department of Artificial Intelligence & Machine Learning Rajasthan Jaipur India Dr. B. R Ambedkar National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354348

The growing need for wireless communication requires the optimum use of spectrum. Cognitive Radio (CR) technology enables the utilization of unused frequency channels through the identification and exploitation of these bands. Nevertheless, conventional spectrum sensing approaches are plagued by drawbacks such as instances of missed detections and false alarms. This study presents a new framework that combines deep learning and blockchain technology to improve the accuracy of spectrum sensing and ensure safe spectrum management in cognitive radio networks. Deep learning models are highly proficient at acquiring intricate signal characteristics, which allows them to attain exceptional performance in spectrum sensing. Blockchain technology, characterized by its decentralized recordkeeping system, inability to be altered, and methods for reaching agreement, promotes confidence and thwarts tampering with spectrum sensing data and user access privileges. Our platform utilizes deep learning models on CR devices to achieve precise spectrum sensing. Additionally, it leverages the secure data storage and access control features of blockchain. This integration improves the accuracy of spectrum sensing, guarantees secure and transparent management of the spectrum, and encourages efficient utilization of the spectrum in cognitive radio networks. The evaluation technique and anticipated results are well defined, setting the stage for future investigations in this field. © 2024 IEEE.

关键词： Radio systems

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CNN-LSTM based Multimodal Models for Music Generation 22

CNN-LSTM based Multimodal Models for Music Generation

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Zhang, Man Liu, Dongning South China University of Technology School of Electronics and Information Guangzhou China Guangdong University of Technology Guangzhou School of Computer Science and Technology Guangzhou China

ISBN: (纸本)9798331509712

In this article, we tackle the dual challenges of efficiency and quality in music generation. We aim to create a model that produces high-quality music efficiently while keeping the model lightweight and the music authenticity and creativity. This research proposes a model that combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for music generation. LSTM units capture long-term dependencies in music sequences. CNN units are included to extract musical features, enhancing the model's grasp of structure and style. We train and valid the model on the ADL Piano MIDI datasets which is a sub set of the Lakh MIDI datasets. Finding that our CNN-LSTM model excels in music creation. It reduces computational complexity, boosting operational efficiency. This paves the way for deployment on resource-limited devices like mobiles or edge computing platforms. Combining these methods ensures the model learns form rich musical features, generating coherent, stylistically consistent pieces. © 2024 IEEE.

关键词： Convolutional neural networks

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Decentralized Federated Deep Reinforcement Learning Framework for Energy-Efficient Base Station Switching Control 11

Decentralized Federated Deep Reinforcement Learning Framewor...

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11th international symposium on Telecommunication, IST 2024

作者： Movahedkor, Narges Shahbazian, Reza Department of Computer Engineering Tehran Iran Department of Informatics Rende Italy

ISBN: (纸本)9798350356250

G cellular networks have achieved significant improvements in capacity and performance, while they have incurred a substantial increase in energy consumption at the base station (BS) level. To address this escalating energy consumption in dense 5G networks, this paper proposes a decentralized federated learning (DFL)-enhanced Deep Reinforcement Learning (DRL) framework for intelligent BS switching. Our approach leverages the distributed nature of 5G networks to collaboratively learn optimal BS on/off policies without centralized control. By combining DFL with DRL, we enable efficient knowledge sharing among BSs while maintaining privacy and reducing communication overhead. The cost function designed in this paper aims to balance energy savings as well as Quality of Service (QoS) requirements. Moreover, to enhance exploration and accelerate convergence, we incorporate an exploration network into the DRL agent and adopt a novel approach of model training. Performed simulations demonstrate the effectiveness of our proposed framework in achieving significant energy reduction of over 23% and total cost of 19%, while maintaining satisfactory QoS performance compared to the existing methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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