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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

Regression prediction algorithm for energy consumption regression in cloud computing based on horned lizard algorithm optimised convolutional neural network-bidirectional gated recurrent unit

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arXiv 2024年

作者： Li, Feiyang Cao, Zinan Yu, Qixuan Tang, Xirui Department of Computer Science University of Illinois Urbana-Champaign ChampaignIL61820 United States Department of General Systems Studies The University of Tokyo Tokyo113-8654 Japan College of Computing Georgia Institute of Technology AtlantaGA30332-0280 United States College of Computer Sciences Northeastern University BostonMA02115 United States

For this paper, a prediction study of cloud computing energy consumption was conducted by optimising the data regression algorithm based on the horned lizard optimisation algorithm for Convolutional Neural Networks-BiDirectional Gated Recurrent Units. Firstly, through Spearman correlation analysis of CPU, usage, memory usage, network traffic, power consumption, number of instructions executed, execution time and energy efficiency, we found that power consumption has the highest degree of positive correlation with energy efficiency, while CPU usage has the highest degree of negative correlation with energy efficiency. In our experiments, we introduced a random forest model and an optimisation model based on the horned lizard optimisation algorithm for testing, and the results show that the optimisation algorithm has better prediction results compared to the random forest model. Specifically, the mean square error (MSE) of the optimisation algorithm is 0.01 smaller than that of the random forest model, and the mean absolute error (MAE) is 0.01 smaller than that of the random forest.3 The results of the combined metrics show that the optimisation algorithm performs more accurately and reliably in predicting energy efficiency. This research result provides new ideas and methods to improve the energy efficiency of cloud computing systems. This research not only expands the scope of application in the field of cloud computing, but also provides a strong support for improving the energy use efficiency of the system. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

Reversible Data Hiding and Encryption with a Blockchain-Based Security Architecture for Digital Image Communication

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SSRN

SSRN 2023年

作者： Bano, Mahwish Habib, Umair Shahid, Usman Hussain, Saddam Iqbal, Jawaid Uddin, Mueen Abdelhaq, Maha Alsaqour, Raed Department of Mathematics Air University Islamabad44000 Pakistan School of Digital Science Universiti Brunei Darussalam Jalan Tungku Link GadongBE1410 Brunei Faculty of Computing Riphah International University Islamabad45210 Pakistan College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Technology College of Computing and Informatics Saudi Electronic University Riyadh93499 Saudi Arabia

In both existing and future networks, security is a crucial concern. Blockchain scheme seems to be the simplest and safest mode for exchanging information in the upcoming-generation networks. The primary target of cyberattacks is digital photographs. The problem of securing digital data embedded inside the images which are transmitted through the network from attackers or third parties is the main purpose of this research work. For digital photo exchange in multi-user networks, a security framework based on blockchain is suggested. The proposed architecture includes encryption and reversible data hiding. A novel and reversible way to hide large amounts of data is also recommended for storing digital photos. To ensure the originality, secrecy, and integrity of digital images encryption and reversible data concealment are implemented. To keep the information secret, first, we compressed the digital image to make space for information storage. After compression, the user signature is inserted, and the picture as a whole is enciphered. For high-capacity compression, lossy JPEG compression is used. Once the image is encrypted, a random block is selected and data is inserted in that particular block. RC4 cipher is used in this work for the encryption of the image which provides higher security. Trial results indicate that the suggested blockchain-based framework offers high security, while the reversible information-hiding strategy and random block selection offer higher embedding capacity, comparable picture conditions, and file size to those of related strategies. © 2023, The Authors. All rights reserved.

关键词： Cryptography

Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning

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arXiv 2024年

作者： Yang, Fuchao Cheng, Jianhong Liu, Hui Dong, Yongqiang Jia, Yuheng Hou, Junhui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Yam Pak Charitable Foundation School of Computing and Information Sciences Saint Francis University Hong Kong Department of Computer Science City University of Hong Kong Hong Kong China

In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are randomly generated (instance-independent), while in practical scenarios, the noisy labels are always instance-dependent and are highly related to the sample features, leading to the instance-dependent partial label learning (IDPLL) problem. Instance-dependent noisy label is a double-edged sword. On one side, it may promote model training as the noisy labels can depict the sample to some extent. On the other side, it brings high label ambiguity as the noisy labels are quite undistinguishable from the ground-truth label. To leverage the nuances of IDPLL effectively, for the first time we create class-wise embeddings for each sample, which allow us to explore the relationship of instance-dependent noisy labels, i.e., the class-wise embeddings in the candidate label set should have high similarity, while the class-wise embeddings between the candidate label set and the non-candidate label set should have high dissimilarity. Moreover, to reduce the high label ambiguity, we introduce the concept of class prototypes containing global feature information to disambiguate the candidate label set. Extensive experimental comparisons with twelve methods on six benchmark data sets, including four fine-grained data sets, demonstrate the effectiveness of the proposed method. The code implementation is publicly available at https://***/Yangfc-ML/CEL. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

A Continuous Variable Quantum Switch

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arXiv 2022年

作者： Tillman, Ian Vasantam, Thirupathaiah Seshadreesan, Kaushik P. College of Optical Sciences University of Arizona Tucson United States Department of Computer Science Durham University Durham United Kingdom Department of Informatics & Networked Systems School of Computing & Information University of Pittsburgh Pittsburgh United States

The continuous quadratures of a single mode of the light field present a promising avenue to encode quantum information. By virtue of the infinite dimensionality of the associated Hilbert space, quantum states of these continuous variables (CV) can enable higher communication rates compared to single photon-based qubit encodings. Quantum repeater protocols that are essential to extend the range of quantum communications at enhanced rates over direct transmission have also been recently proposed for CV quantum encodings. Here we present a quantum repeating switch for CV quantum encodings that caters to multiple communication flows. The architecture of the switch is based on quantum light sources, detectors, memories, and switching fabric, and the routing protocol is based on a Max-Weight scheduling policy that is throughput optimal. We present numerical results on an achievable bipartite entanglement request rate region for multiple CV entanglement flows that can be stably supported through the switch. We elucidate our results with the help of exemplary 3-flow networks. © 2022, CC BY-NC-ND.

关键词： Scheduling

Accurate Estimation of Individual Transmission Rates Through Contact Analytics Using UWB Based Indoor Location Data

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Accurate Estimation of Individual Transmission Rates Through...

International Conference on Smart Communications and Networking (SmartNets)

作者： Ravi Chandra Thota Sarawat Murtaza Sara Md Yusuf Sarwar Uddin Majid Bani-Yaghoub Gary Sutkin Department of Computer Science University of Missouri-Kansas City Kansas City USA Division of Computing Analytics and Mathematics University of Missouri-Kansas City Kansas City USA Department of Biomedical and Health Informatics University of Missouri-Kansas City University Health Kansas City Kansas City USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Effective control and prevention of infectious dis-eases often require detailed social network data categorized by individual movements and contact structures. Social network data is primarily accessible on a macro scale through sources such as RFID, Bluetooth, and mobile location data. This has limited our ability to accurately estimate transmission rates and identify significant patterns of disease spread within social gatherings. This study aims to address this gap by formulating a methodology that enables precise estimation of the disease transmission rates of each individual. Our methodology consists of the following steps (i) Installing a real-time location system and collection of indoor location data from two IRB-approved social events for the college students; (ii) Estimating infection probabilities as a function of distance between individuals calibrated with COVID-19 transmission data; (iii) Conducting a comprehensive analysis of contacts, including contact events, intensity, and duration; (iv) Utilizing the insights from the preceding steps to compute infection transmission rates for each participant. Our study revealed that the majority of contacts among participants are transient, typically lasting between 1 to 30 seconds within a proximity of 6 feet. However, infection transmission is associated with different types of contact durations and frequency, all hap-pening within a 6-foot area. Furthermore, a comparative analysis of the data from two indoor social events indicates a strong correlation between the duration of contacts and the nature of the social gathering (e.g., student orientation session versus informal meeting), alongside the density of participants (e.g., crowded versus sparsely populated environments). The proposed methodology is the first step toward accurately estimating in-dividual transmission rates, leveraging individual characteristics and the nature of the social event as key determinants. This will potentially enable epidemiolog

关键词： Analytical models Accuracy Social networking (online) Infectious diseases Sociology Estimation Real-time systems

A connectivity index based on adjacent vertices in cubic fuzzy graph with an application

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11025-11040页

作者： Guan, Hao Sadati, Seyed Hossein Talebi, Ali Asghar Shafi, Jana Khan, Aysha Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Computer Science of Information Technology Qiannan Normal University for Nationalities Duyun China Department of Mathematics University of Mazandaran Babolsar Iran Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Wadi Alddawasir Saudi Arabia Department of Mathematics Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

A cubic fuzzy graph is a type of fuzzy graph that simultaneously supports two different fuzzy memberships. The study of connectivity in cubic fuzzy graph is an interesting and challenging topic. This research generalized the neighborhood connectivity index in a cubic fuzzy graph with the aim of investigating the connection status of nodes with respect to adjacent vertices. In this survey, the neighborhood connectivity index was introduced in the form of two numerical and distance values. Some characteristics of the neighborhood connectivity index were investigated in cubic fuzzy cycles, saturated cubic fuzzy cycle, complete cubic fuzzy graph and complementary cubic fuzzy graph. The method of constructing a cubic fuzzy graph with arbitrary neighborhood connectivity index was the other point in this research. The results showed that the neighborhood connectivity index depends on the potential of nodes and the number of neighboring nodes. This research was conducted on the Central Bank's data regarding inter-bank relations and its results were compared in terms of neighborhood connectivity index. © 2024-IOS Press. All rights reserved.

关键词： Graph theory

Storage Matched Systems for Single-Click Photo Recognition Using CNN

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Storage Matched Systems for Single-Click Photo Recognition U...

Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Sunil Kumar R Shantha Mary Joshitta Deepak Dasaratha Rao Harinakshi Syeda Masarath Vivek Nivruttira Waghmare Department of Computer Science And Engineering School of Computing Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India Jayaraj Annapackiam college for women (Autonomous) Periyakulam Computer Science Mother Teresa Women's University Kodaikanal Tamilnadu India Consultant R&D City Plano USA Computer Science St Philomenas College (Autonomous) Mysore India

During this research, we provide two novel concepts: learners to acquire network characteristics for the unlabelled pictures over the spot in one-shot education, while supplementing the CNNs using memories. We particularly introduce Memories Matched Networking (MM-Net), a revolutionary deep design that investigates learning under the premise that learning and testing circumstances require being identical. In order to fully utilise the information contained in the collection, MM-Net essentially stores the characteristics of a collection of named pictures (the reference group) into memories and returns from memories while doing inferences. A Cognitive Learner, however, uses the storage spaces sequentially in predicting the CNN settings for unlabelled pictures. Whenever just a few instances from every category are shown at each stage while the instruction is switched across mini-batch into mini-batch, the entire system is taught. This is designed for quick learning whenever merely a few instances representing novel groups are displayed during testing. In contrast to traditional one-shot training techniques, our own MM-Net might generate a single, cohesive network regardless of the quantity of photos or classes. Using 2 open records, Omniglot while miniImageNet, thorough analyses are undertaken, and better outcomes are presented when contrasted with cutting-edge methods. Furthermore, the privilege of MM-Net raises one-shot performance on Omniglot via 98.95% into 99.28% as well as miniImageNet via 49.21% into 53.37%.

关键词：

Spatial Attention Cooperative-Based Graph Convolutional Model for Person Re-Identification

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SSRN

SSRN 2023年

作者： Xiong, Mingfu Xiong, Jiefan Wang, Zhongyuan Chen, Jia Hu, Ruimin Ser, Javier Del Muhammad, Khan Xiong, Zixiang School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan430200 China School of Cyber Science and Engineering Wuhan University Wuhan430072 China Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of Derio48160 Spain Bilbao48013 Spain The Department of Electrical and Computer Engineering Texas A&M University College StationTX7784 United States

Recently, person re-identification (person Re-ID) equipped with an attention mechanism has gradually emerged as one of the most active areas of study in the field of computer vision and neural processing. Despite the upsurge in related research, existing attention-based methods still face two major challenges when recognizing different persons with similar appearances in unpredictable realistic environments: (1) the adaptability of a single local salient feature to hostile scenes cannot be guaranteed, and (2) most existing methods originating from attention features usually find a simple linear transformation or implement variation strategies, which makes it difficult to discriminate different pedestrians with similar appearance attributes. To address these issues, this paper proposes a spatial attention cooperative-based graph convolutional model (SACGCM) to examine the relevance of local body regions for individuals. The proposed framework encompasses a multi-dimensional attention model (MAM) and spatial adaptive graph convolutional model (SAGCM). In particular, the MAM comprises a dual-attention (local and global) module and feature channel-attention stage, which are used to capture multiple key points for each person. Then, SAGCM is used to construct spatial nonlinear transformation relationships among the various body regions for each person using graph optimization. Extensive experiments were conducted using public person Re-ID datasets (Market-1501, DukeMTMC-reid, and CUHK-03). The results demonstrate that the performance of the proposed approach is superior to that of state-of-the-art methods by between 2 and 10\% at Rank-1 (mAP). © 2023, The Authors. All rights reserved.

关键词： Linear transformations