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QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing: An Online Joint Optimization Approach

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wu, Qingqing Kang, Jiawen Niyato, Dusit Han, Zhu Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Automation Guangdong University of Technology Guangzhou510006 China The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446701 Korea Republic of The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on fixed infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, unmanned aerial vehicle (UAV)-assisted MEC is emerging as a new promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy consumption constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory planning approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results provide empirical evidence supporting the superior system performance of the proposed OJTRTA in comparison to alternative approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

Usage: Uncertain Flow Graph and Spatio-Temporal Graph Network-Based Saturation Attack Detection Method

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SSRN

SSRN 2023年

作者： Wang, Kaixi Cui, Yunhe Qian, Qing Chen, Yi Guo, Chun Shen, Guowei Engineering Research Center of Text Computing & Cognitive Intelligence Guizhou University China State Key Laboratory of Public Big Data Guizhou University China Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guizhou University China College of Computer Science and Technology Guizhou University China School of Information Guizhou University of Finance and Economics China

With the development of software-Defined Networking (SDN), its security problems have become important research content. Among these security problems, saturation attack is one of the most destructive problems. Saturation attacks can destroy SDN networks. Currently, neural graph networks have already been used for detecting saturation attacks in SDN. When constructing the graph based on K-Nearest Neighbors (KNN) algorithm, the number of edges in the graph is fixed. That cannot reflect the different nodes’ relationship well. In addition, when using the Graph Convolutional Network (GCN) to detect saturation attack against SDN, only a partial graph structure and node features will be learned, causing the flow graph nodes information cannot be fully utilized. To overcome these issues, in this work, we propose USAGE, an uncertain flow graph and spatio-temporal graph network-based saturation attack detection method. More specifically, USAGE designs the CRAM algorithm and EGG model. The CRAM algorithm is designed to generate an uncertain flow graph for each traffic flow, which can represent the uncertain relationship among different nodes. EGG is a spatio-temporal graph convolutional network model, which is proposed to detect saturation attacks towards SDN switches. The evaluation results demonstrate that USAGE can generate a better graph than KNN and achieve high detection performance. Meanwhile, USAGE outperforms other methods in terms of TPR, TNR, accuracy, precision, recall, F-score, confusion matrix, ROC curves, and PR curves. © 2023, The Authors. All rights reserved.

关键词： Flow graphs

DG-Trans: Automatic Code Summarization via Dynamic Graph Attention-based Transformer

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DG-Trans: Automatic Code Summarization via Dynamic Graph Att...

IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Jianwei Zeng Tao Zhang Zhou Xu Faculty of Information Technology Macau University of Science and Technology Macau China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665458146

Automatic code summarization is an important topic in the software engineering field, which aims to automatically generate the description for the source code. Based on Graph Neural Networks (GNN), most existing methods apply them to Abstract Syntax Tree (AST) to achieve code summarization. However, these methods face two major challenges: 1) they can only capture limited structural information of the source code; 2) they did not effectively solve Out-Of-Vocabulary (OOV) problems by reducing vocabulary size. In order to resolve these problems, in this paper, we propose a novel code summarization model named Dynamic Graph attention-based Transformer (DG-Trans for short), which effectively captures abundant information of the code subword sequence and utilizes the fusion of dynamic graph attention mechanism and Transformer. Extensive experiments show that DG-Trans is able to outperform state-of-the-art models (such as Ast-Attendgru, Transformer, and CodeGNN) by averagely increasing 8.39% and 8.86% on BLEU scores and ROUGUE-L, respectively.

关键词： Vocabulary Codes Fuses Semantics software quality Syntactics Transformers

IoT-Enhanced local attention dual networks for pathological image restoration in healthcare

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Measurement: Sensors 2024年 33卷

作者： Hassan, Abdelwahab Said Thakare, Anuradha Bhende, Manisha Prasad, K.D.V. Singh, Pavitar Parkash Byeon, Haewon Department of Mathematics Faculty of Science Suez Canal University Egypt Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune411044 India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Hyderabad Pune India Department of Management Lovely Professional University India Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of

High-resolution pathological images play a pivotal role in accurate disease diagnosis and are important in precision medicine. However, obtaining real-time high-resolution images faces challenges due to hardware limitations and scanning time constraints. Conventional image super-resolution restoration algorithms struggle to provide satisfactory results for pathological images, resulting in blurred and unrealistic restorations. In response to this challenge, this research proposes a pioneering approach by integrating Internet of Things (IoT) technology with Local Attention Dual Network (IOT-LAT) for super-resolution restoration of pathological images. The enhanced IOT-LATincorporates IoT-based data acquisition and processing, IoT-Enhanced non-local attention mechanisms, Gaussian constraint, and parameter-sharing strategies in up-sampling and down-sampling branches. This integration enables real-time super-resolution restoration of pathological images with improved accuracy. The reconstructed images exhibit a structural similarity of 0.914 and a peak signal-to-noise ratio of 30.84 dB. These results validate the effectiveness of the suggested approach in precisely reconstructing high-frequency details and improving modelling efficiency via a lightweight non-local attention mechanism enhanced by the Internet of Things. This work discusses IoT and non-local attention dual networks to improve pathological image super-resolution restoration. This allows for faster and more accurate disease diagnosis and treatment in precision medicine. © 2024 The Authors

关键词： Internet of things

Faster OreFSDet: A Lightweight and Effective Few-shot Object Detector for Ore Images

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arXiv 2023年

作者： Zhang, Yang Cheng, Le Peng, Yuting Xu, Chengming Fu, Yanwei Wu, Bo Sun, Guodong School of Mechanical Engineering Hubei University of Technology Wuhan430068 China Hubei Key Laboratory of Modern Manufacturing Quality Engineering Hubei University of Technology Wuhan430068 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Data Science Fudan University Shanghai200433 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China

For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled data is time-consuming and expensive. General object detection methods often suffer from severe over-fitting with scarce labeled data. Despite their ability to eliminate over-fitting, existing few-shot object detectors encounter drawbacks such as slow detection speed and high memory requirements, making them difficult to implement in a real-world deployment scenario. To this end, we propose a lightweight and effective few-shot detector to achieve competitive performance with general object detection with only a few samples for ore images. First, the proposed support feature mining block characterizes the importance of location information in support features. Next, the relationship guidance block makes full use of support features to guide the generation of accurate candidate proposals. Finally, the dual-scale semantic aggregation module retrieves detailed features at different resolutions to contribute with the prediction process. Experimental results show that our method consistently exceeds the few-shot detectors with an excellent performance gap on all metrics. Moreover, our method achieves the smallest model size of 19MB as well as being competitive at 50 FPS detection speed compared with general object detectors. The source code is available at https://***/MVME-HBUT/Faster-OreFSDet. © 2023, CC BY-NC-SA.

关键词： Object detection

A Certificateless and Privacy-Preserving Authentication with Fault-Tolerance for Vehicular Sensor Networks

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A Certificateless and Privacy-Preserving Authentication with...

IEEE Conference on Dependable and Secure Computing

作者： Yang Zhao Guohang Dan Ankang Ruan Jicheng Huang Hu Xiong Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

The vehicular sensor network has been developed to improve traffic, enhance the driving experience and so on, and is an inevitable technology trend. In vehicular sensor network, large amounts of data generated by vehicles need to be transmitted in the open channel, so it is important to ensure the integrity of information and vehicular privacy. For such a harsh condition, the aggregate signature technology is a very suitable solution, because it not only provides message integrity and authentication, but is also well suited to resource-constrained environments. Thus, in this paper, for vehicular sensor networks, we construct a certificateless and fault-tolerant aggregate signature (CLFTAS) scheme based on the elliptic curve cryptosystem. The certificateless nature makes our scheme free from the certificate management issue in traditional public key cryptography and the key escrow issue in identity-based cryptography. Fault tolerance greatly makes up for the aggregate signature's defect that adding an invalid individual signature to a valid aggregate invalidates the entire aggregation. Moreover, our scheme can protect the privacy of the vehicle' identity. In the end, we give the security proof and performance evaluation. In the random oracle model, our scheme is secure under the assumption that solving the elliptic curve discrete logarithm problem is hard, and has great performance optimization and functionality compared with the state-of-the-art.

关键词： Performance evaluation Fault tolerance Privacy Aggregates Fault tolerant systems Authentication Elliptic curve cryptography

Multi-objective Optimization for Multi-UAV-assisted Mobile Edge Computing

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arXiv 2024年

作者： Sun, Geng Wang, Yixian Sun, Zemin Wu, Qingqing Kang, Jiawen Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China School of Automation Guangdong University of Technology Guangzhou510006 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computing-intensive and latency-sensitive demands of users poses a significant challenge due to the limited resources of UAVs. To address this challenge, we present a multi-objective optimization approach for multi-UAV-assisted MEC systems. First, we formulate a multi-objective optimization problem aiming at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total amount of offloaded tasks by jointly optimizing task offloading, computation resource allocation, and UAV trajectory control. Since the problem is a mixed-integer non-linear programming (MINLP) and NP-hard problem which is challenging, we propose a joint task offloading, computation resource allocation, and UAV trajectory control (JTORATC) approach to solve the problem. However, since the decision variables of task offloading, computation resource allocation, and UAV trajectory control are coupled with each other, the original problem is split into three sub-problems, i.e., task offloading, computation resource allocation, and UAV trajectory control, which are solved individually to obtain the corresponding decisions. Moreover, the sub-problem of task offloading is solved by using distributed splitting and threshold rounding methods, the sub-problem of computation resource allocation is solved by adopting the Karush-Kuhn-Tucker (KKT) method, and the sub-problem of UAV trajectory control is solved by employing the successive convex approximation (SCA) method. Simulation results show that the proposed JTORATC has superior performance compared to the other benchmark methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Exploring data and knowledge combined anomaly explanation of multivariate industrial data

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arXiv 2021年

作者： Ding, Xiaoou Wang, Hongzhi Wang, Chen Li, Zijue Liang, Zheng School of Computer Science and Technology Harbin Institute of Technology China National Engineering Laboratory for Big Data Software EIRI Tsinghua University China

—The demand for high-performance anomaly detection techniques of IoT data becomes urgent, especially in industry field. The anomaly identification and explanation in time series data is one essential task in IoT data mining. Since that the existing anomaly detection techniques focus on the identification of anomalies, the explanation of anomalies is not well-solved. We address the anomaly explanation problem for multivariate IoT data and propose a 3-step self-contained method in this paper. We formalize and utilize the domain knowledge in our method, and identify the anomalies by the violation of constraints. We propose set-cover-based anomaly explanation algorithms to discover the anomaly events reflected by violation features, and further develop knowledge update algorithms to improve the original knowledge set. Experimental results on real datasets from large-scale IoT systems verify that our method computes high-quality explanation solutions of anomalies. Our work provides a guide to navigate the explicable anomaly detection in both IoT fault diagnosis and temporal data cleaning. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

A two-stage learning approach for wind power prediction using dynamic graph convolution network hybrid model 8

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A two-stage learning approach for wind power prediction usin...

8th IEEE Conference on Energy Internet and Energy System Integration, EI2 2024

作者： Li, Hailong Wang, Yongsheng Guan, Shijie Wang, Xuehui Bao, Daorina Liu, Xiaojun Chen, Haotian Inner Mongolia University of Technology College of Data Science and Application Huhhot010080 China China Agricultural University College of Information and Electrical Engineering Beijing100083 China Inner Mongolia University of Technology College of Energy and Power Engineering Hohhot010080 China Inner Mongolia Wuchuan County Water Affairs Bureau Hohhot011799 China Dalian University of Technology School of International Information and Software Dalian116024 China

ISBN: (纸本)9798331523527

This paper proposes an effective two-stage learning method to improve the accuracy of wind power prediction. The complex time dependency relationship between meteorological data variables is modelled using a dynamic graph convolutional network, and the wind power prediction is completed using the XGBoost model. Experimental results show that the two-stage learning method can effectively extract key time series features and has good prediction accuracy. © 2024 IEEE.

关键词： Network theory (graphs)