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Game Theoretic Resource Allocation for Information Freshness in Mobile Edge Computing

Game Theoretic Resource Allocation for Information Freshness...

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international Conference on parallel and distributed systems (ICPADS)

作者： Jingjing Gu Di Zhang Hongcheng Bao Weiwei Xing Xindong Zheng Xun Shao School of Software Engineering Beijing Jiaotong University China School of Information Science and Technology Fudan University China Department of Electrical and Electronic Information Engineering Toyohashi University of Technology Japan

Age of information (AoI) is an important metric used to quantify the freshness of data. By utilizing resources of the edge server near the source nodes, mobile edge computing (MEC) can speed up the processing of information updates and ensure data freshness. However, the resources of the edge server are usually limited, so it is necessary to study the resource allocation strategy to ensure data freshness and optimize the profit of the edge server. In this paper, we propose a game-theoretic approach for resource allocation in mobile edge computing to guarantee information freshness. First, with the purpose of ensuring information freshness, we formalize the problem as minimizing the computational cost of source nodes and maximizing the profit of the edge server. Then, we introduce a two-stage dynamic game model to simulate the competitive process. We further transform the resource allocation problem into a knapsack problem and propose an iterative resource allocation algorithm based on dynamic programming. Experimental results show that the proposed algorithm can obtain a Nash equilibrium and maximize the profit of the edge server while ensuring information freshness.

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Privacy-preserving scheme of cotton supply chain based on blockchain

Privacy-preserving scheme of cotton supply chain based on bl...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Linlin Zhang Wenwen Li Jing Huo Xuehua Bi Kai Zhao College of Software Xinjiang University Urumqi China College of Computer Science and Technology Xinjiang University Urumqi China College of Medical Engineering and Technology Xinjiang Medical University Urumqi China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the popularization and development of artificial intelligence and intelligent agriculture, the supply chain has become an effective management method. However, the complexity and variety of data types and the large amount of data in the supply chain make its privacy and security a major challenge. Especially in the cotton supply chain, due to the large number of participants involved and the varying levels of data security protection of each participant, it is easy to leak the user's identity privacy and private data in the supply chain during data sharing. Therefore, this paper is dedicated to improving privacy security in the cotton supply chain. First, we define the data in the cotton supply chain and divide it into private data and shared data, which is conducive to the secure storage and sharing of data in the cotton supply chain. Second, we introduce blockchain and advanced cryptography techniques and propose a method of the pseudo-identity encryption based on SM9, which ensures the security of data in the supply chain while ensuring the privacy and security of the identities of the data owners and data receivers, and realizes the tracking of the identities of the malicious users who have uploaded false data. Finally, we adopt the method of dual-chain storage and introduce IPFS to reduce the storage burden of blockchain as much as possible and carry out security and experimental analyses of the proposed scheme to verify its feasibility.

关键词： Smart agriculture Data privacy Privacy Supply chains Receivers Blockchains Cotton Security Protection Secure storage

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Architecture of a Low-Cost Solution for ROVs to Improve Navigation and Data Collection 20th

Architecture of a Low-Cost Solution for ROVs to Improve Navi...

引用

20th international symposium on distributed Computing and Artificial Intelligence, DCAI 2023

作者： Severino, Umberto Fortuna, Santina Lagudi, Antonio Bruno, Fabio Mišković, Nikola Djapic, Vladimir Department of Mechanical Energetic and Management Engineering University of Calabria Arcavacata di Rende87036 Italy Faculty of Electrical Engineering and Computing University of Zagreb Zagreb10 000 Croatia H2O Robotics Ltd Zagreb10 000 Croatia Institute for Artificial Intelligence Research and Development Novi Sad21000 Serbia

ISBN: (纸本)9783031383175

The documentation, conservation, and preservation of underwater archaeological sites is conventionally performed by divers in shallow waters. However, when dealing with significant depths, the sites can be inaccessible for humans, presenting significant challenges and risks, making these activities hazardous. Recently, the use of robotic platform such as remotely operated vehicles (ROVs) allows to overcome the difficulty of such a harsh environment. Nevertheless, professional-grade ROVs employed for this purpose are expensive and specifically designed with integrated advanced sensor systems for data acquisition and navigation. In the context of the MSCA-RISE H2020 Technological Consortium TO develop sustainability of underwater Cultural heritage (TECTONIC) project, a solution consisting of an optical payload and an acoustic localization system for low-cost ROV, was defined in order to support ROV pilots during documentation and monitoring activities. The project aims to preserve underwater cultural heritage (UCH) encompassing the selection of a commercially available ROV and the definition of a customized hardware/software (HW/SW) architecture. After market research, a suitable commercial off-the-shelf (COTS) low-cost ROV was defined and used as the foundation for integration. Mechanical, electric, and data interface modules were designed to seamlessly integrate and manage the sensor suite. The results of the integration demonstrate the potential of the chosen algorithms and the sensor suite for improved navigation and data collection in challenging underwater environments, where high-grade and expensive sensors are not available, thereby making more accessible the field of underwater survey missions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Costs

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Subgraph Sampling for Inductive Sparse Cloud Services QoS Prediction

Subgraph Sampling for Inductive Sparse Cloud Services QoS Pr...

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international Conference on parallel and distributed systems (ICPADS)

作者： Jianlong Xu Zhiyu Xia Yuhui Li Yuxiang Zeng Zhidan Liu College of Engineering Shantou University Shantou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Quality-of-Service (QoS) based collaborative prediction models are emerging to select appropriate edge cloud services for users. Nevertheless, there are still challenges in the realworld QoS prediction task. First, existing QoS prediction models are mostly transductive, failing to generalize to unseen users and services. Secondly, an accurate prediction model remains unexplored under the extreme sparse data scenario, where only a few interactions are available for collaborative filtering. To address these problems, we propose -Inductive -Subgraph -Pattern -Aware Graph Neural Network (ISPA-GNN), which leverages a novel graph-based collaborative filtering method with a subgraph sampling strategy. We further optimize the embeddings components, replacing the user/service embeddings with compositional context information to enable better generalization to unseen nodes while reducing memory usage. Extensive experiments on a large-scale real-world service QoS dataset demonstrate some decent properties of our model, including high prediction accuracy, memory efficiency, and slight performance degradation even if 25% of users/services are never seen.

关键词： Fluctuations Collaborative filtering Time series analysis Memory management Collaboration Quality of service Predictive models

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Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome A...

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IEEE international symposium on Performance Analysis of systems and software

作者： Zhuren Liu Shouzhe Zhang Justin Garrigus Hui Zhao Department of Computer Science and Engineering University of North Texas

Genomic analysis is the study of genes which includes the identification, measurement, or comparison of genomic features. Genomics research is of great importance to our society because it can be used to detect diseases, create vaccines, and develop drugs and treatments. As a type of general-purpose accelerators with massive parallel processing capability, GPUs have been recently used for genomics analysis. Developing GPUbased hardware and software frameworks for genome analysis is becoming a promising research area. To support this type of research, benchmarks are needed that can feature representative, concurrent, and diverse applications running on GPUs. In this work, we created a benchmark suite called Genomics-GPU, which contains 10 widely-used genomic analysis applications. It covers genome comparison, matching, and clustering for DNAs and RNAs. We also adapted these applications to exploit the CUDA Dynamic parallelism (CDP), a recent advanced feature supporting dynamic GPU programming, to further improve the performance. Our benchmark suite can serve as a basis for algorithm optimization and also facilitate GPU architecture development for genomics analysis.

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UAV-based Reliable Optical Wireless Communication via Cooperative Multi-agent Reinforcement Learning Approach

UAV-based Reliable Optical Wireless Communication via Cooper...

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international Conference on parallel and distributed systems (ICPADS)

作者： Jiangang Liu Hanjiang Luo Rukhsana Ruby Haoran Wang Hang Tao Kaishun Wu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Hong Kong University of Science and Technology Guangzhou

In marine wireless sensor networks, swarms of unmanned aerial vehicles (UAVs) based optical communication system can be leveraged to transmit underwater real-time monitoring data which enables a variety of potential maritime missions, such as video streaming for underwater surveillance and target tracking. However, due to the high directionality of optical beams and the mechanical instability of UAVs caused by the wind, the optical link via UAVs as relays is very fragile. To deal with the challenge, in this study, we model the problem as a partially observed Markov decision process (POMDP), and propose a novel link maintenance scheme based on the cooperative multi-agent deep deterministic policy gradient (MADDPG) approach to control the swarms of UAVs, which integrates the UAV dynamics model and the optical communication model. In this scheme, multiple UAVs act as agents controlling their own states in real-time under the complex wind field, in order to keep mechanical stability through cooperation, and dynamically maintain the reliability of the optical links which maximize communication performance to achieve reliable end-to-end optical communication and reduce energy consumption. Through numerical simulations, it demonstrates that the proposed optimization scheme is effective and achieves robust performance in terms of communication quality and energy consumption.

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RectorDApp: Decentralized Application for Managing University Rector Elections 15

RectorDApp: Decentralized Application for Managing Universit...

引用

15th IEEE international Conference on Service-Oriented System engineering (SOSE)

作者： Rosa-Bilbao, Jesus Boubeta-Puig, Juan Univ Cadiz Sch Engn UCASE Software Engn Res Grp Cadiz Spain Univ Cadiz Dept Comp Sci & Engn UCASE Software Engn Res Grp Cadiz Spain

ISBN: (纸本)9781665434775

Blockchain is a distributed and secure database that can be applied to all types of transactions. Blockchain technology is growing in popularity because it allows for the development of applications whose information is traceable, immutable, transparent and reliable. Given the advantages that blockchain provides over other traditional systems, in this paper we present a decentralized application, called RectorDApp, for the management of university rector voting in a private, but transparent and immutable way, being able to verify publicly and in real time the election results. RectorDApp, capable of interacting with the Ethereum public blockchain network, was developed using the Truffle framework and the MetaMask software. The results demonstrate that RectorDApp is a highly useful application to address the digital transformation of university rector elections.

关键词： Blockchain DApp Digital transformation Smart Contract University Voting

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DSG:A Robustness-Enhanced Self-Supervised Depth Estimation Method

DSG:A Robustness-Enhanced Self-Supervised Depth Estimation M...

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ACIS international Conference on software engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Changhao Zhao Jingyi Zhao Yikun Xu Jing An School of Computer and Cyber Sciences Communication University of China Beijing China College of Information Science and Engineering/College of Artificial Intelligence China University of Petroleum (Beijing) Beijing China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

Depth information in images is crucial for applications in autonomous driving, 3D reconstruction, and robot navigation. Self-supervised depth estimation methods have gained considerable attention because they rely solely on unlabeled video inputs. Traditional sensing equipment, such as LiDAR, incurs high costs and has limited effectiveness in adverse driving conditions, making image-based depth estimation a more economical and versatile alternative. However, real-world applications often involve diverse and noisy environmental conditions, whereas most existing models are trained on clean, ideal datasets. Our paper presents an advanced deep-learning method for robust self-supervised depth estimation, specifically designed to operate effectively under various dynamic scenes and noise conditions, which is called DSG. Our approach takes into account the noise and disturbances that can occur in 3 driving scenarios. Results on standard datasets demonstrate the model’s effectiveness, showing excellent robustness in handling noisy and diverse conditions. These strategies enhance the decision-making and safety evaluation capabilities of autonomous vehicles, facilitate the widespread adoption of low-cost sensing devices, and significantly improve model robustness in variable environments. The proposed self-supervised depth estimation method substantially enhances the reliability and accuracy of deep learning models, especially in dynamically changing application scenes.

关键词： Accuracy Biological system modeling Computational modeling Noise Estimation Robot sensing systems Robustness

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Structured Design of Multilevel Simulation Models

Structured Design of Multilevel Simulation Models

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international Workshop/symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Luca Serena Moreno Marzolla Gabriele D’Angelo Department of Computer Science and Engineering University of Bologna Italy Center for Inter-Department Industrial Research ICT University of Bologna Italy

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

Multilevel modeling is a design technique that allows complex models to be specified in terms of simpler ones. An obvious advantage of multilevel modeling is the possibility of reusing existing models, therefore reducing development cost. However, multilevel modeling entails more than the mere application of the ubiquitous principle of decomposition. Indeed, in a multilevel model the same entity may be described at different levels of detail during execution. This allows, for example, to focus on areas of interest by switching to a more detailed model, and revert back to a faster but less accurate one when possible. In this paper we propose GEMMA (GEneric Multilevel Modeling Abstraction), a methodology inspired by DEVS that can help the designer to produce sound and correct multilevel models. We propose a practical realization of GEMMA as a concrete software architecture that can be instantiated in different programming languages. We illustrate a simple use case where different sub-models written in different programming languages are combined according to the GEMMA methodology. Our ultimate goal is to bridge the gap between theory and practice of multilevel modeling, to foster a broader adoption of this useful technique.

关键词： Computer languages Accuracy Costs Codes software architecture Unified modeling language Switches Real-time systems Information exchange

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AdaptTrack: Multi-Object Tracking by Adaptive Correlation

AdaptTrack: Multi-Object Tracking by Adaptive Correlation

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Kunpeng Li Lei Wang Hongjun Ren Yangjie Cao School of Cyber Science and Engineering Zhengzhou University Zhengzhou China Hanwei Electronics GroupCorporation Zhengzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The main idea of Multi-Object tracking is to accurately identify and track the position and motion status of multiple objects in a video sequence in real time. The current mainstream tracking algorithm is to associate the detection boxes with the trajectory in a comprehensive and violent way, as a way to accurately determine and maintain the unique identity mark of the moving object. However, implementing a unified association process for detection boxes with different confidence levels, this suffers from non-negligible low information utilization, long association time, and high resource consumption. To solve this problem, we propose a faster, simple and effective association algorithm ADAPT, dividing the queue of detection boxes and trajectories by confidence level to achieve adaptive association of detection boxes and trajectories in the queue, so as to realize tracking, shorten the association time, and improve tracking effect. When applied to six different trackers, our method achieved significant improvements in both MOTA and IDF1 scores. In order to improve the MOT performance, we designed a simple and powerful tracker, AdaptTrack, and evaluated it on 3090GPU for VisDrone-MOT datasets, achieving 41.3 MOTA, 53.6 IDF1, and 42.9 HOTA. AdaptTrack on UAVDT datasets also shows the same excellent results of 69.8 MOTA, 78.9 IDF1, 66.3 HOTA, and achieves a tracking speed of 18.2 FPS.

关键词： distributed processing Correlation Accuracy Video sequences Robustness Real-time systems Trajectory Object tracking Object recognition

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