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IoT data analytic algorithms on edge-cloud infrastructure:A review

Digital Communications and Networks 2023年第6期9卷 1486-1515页

作者： Abel E.Edje M.S.Abd Latiff Weng Howe Chan School of Computing Faculty of EngineeringUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia Department of Computer Science Delta State UniversityAbrakaP.M.B 01 AbrakaDelta StateNigeria UTM Big Data Centre Ibnu Sina Institute of Scientific and Industrial ResearchUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia

The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime ***,it is constrained by limited data storage and processing *** offloads its massive data stream to edge devices and the cloud for adequate storage and *** further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data *** paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load *** review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load *** addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been *** classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.

关键词： Internet of things Cloud platform Edge Analytic algorithms Processes Network communication protocols

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Game-Theoretic Analysis on CBDC Adoption 1st

Game-Theoretic Analysis on CBDC Adoption

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1st BenchCouncil International Federated Intelligent computing and Block Chain Conferences, FICC 2020

作者： Mou, Chenqi Tsai, Wei-Tek Jiang, Xiaofang Yang, Dong LMIB – School of Mathematics and Systems Science Beihang University Beijing100191 China SKLSDE – School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

ISBN: (纸本)9789811611599

As an important blockchain application, CBDC (Central Bank Digital Currency) has received significant worldwide attention as it can restructure financial market, affect national currency policies, and introduce new regulation policies and mechanisms. It is widely predicted that CBDC will introduce numerous digital currency competitions in various aspects of the global financial market, and winners will lead the next wave of digital currency market. This paper applies the game theory to study the competitions between different countries, in particular to analyze whether they should adopt the CBDC program. We propose two game-theoretic models for CBDC adoption, both analyzing whether to adopt the CBDC program via the Nash equilibrium. Both game-theoretic models draw the same conclusion that each country should adopt the CBDC program regardless of the choices of other counties. In other words, current currency leaders should adopt CBDC because it may lose the premier status, and other countries should adopt CBDC otherwise they risk of getting even further behind in the digital economy. According to our game-theoretic models, the current market leader who has 90% of market shares may lose about 19.2% shares if it is not the first mover. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Game theory

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge computing Networks

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2202-2216页

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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Neural TTS-Based Dynamic data Augmentation for Improved Speech Separation

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2457-2470页

作者： Kai Wang Cuicui Zhu Lili Yin Sheng Li Madina Mansurova Hao Huang School of Computer Science and Technology Xinjiang University Urumqi China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi China School of Engineering Institute of Science Tokyo Kanagawa Japan Department of Artificial Intelligence and Big Data Al-Farabi Kazakh National University Almaty Kazakhstan

Text-to-speech (TTS) synthetic data augmentation has been widely used in various speech processing tasks, but its effectiveness in speech separation remains understudied. In this paper, we present SpeakerAugment+ (SA+), a neural TTS-based dynamic data augmentation framework for speech separation. The SA+ framework consists of two modules: speaker module, which learns a Gaussian Mixture Model (GMM) to characterize the distribution over speaker embeddings in training data and samples unseen speaker embeddings during inference; speech module, which conditions speech synthesis using speaker embeddings with controllable speaker parameters. SA+ incorporates three augmentation techniques: speaker generation, parameter manipulation and utterance generation, enhancing speaker and utterance diversity from different perspectives. Following the SA+ framework, we design FS2-SA+ and Matcha-SA+, which are based on FastSpeech 2 and Matcha-TTS, respectively. We evaluate SA+ across multiple separation models and datasets, and the results demonstrate a substantial improvement in speech separation performance. Matcha-SA+ generates higher-quality speech and achieves better separation performance in intra-corpus tests. Conversely, FS2-SA+ supports a broad range of speaker parameter adjustments, leading to better generalization. Besides, the relationship between harmonicity and speech separation is a widely researched topic, and our findings indicate that speech lacking an explicit harmonic structure, when generated by neural TTS, can function as augmented data to improve speech separation. This research underscores the effectiveness of neural TTS-based data augmentation in speech separation tasks. We hope that our work can offer insights for future studies investigating data augmentation strategies within speech separation.

关键词： data augmentation Training Speech synthesis Speech recognition Speech processing Training data Vocoders Speech enhancement Harmonic analysis Optimization

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DOANet: Point Cloud Registration with Deep Overlap Attention

DOANet: Point Cloud Registration with Deep Overlap Attention

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2021 China Automation Congress, CAC 2021

作者： Guan, Haining Gao, Qing Zhang, Baochang School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665426473

Pairwise point cloud registration is a fundamental task in 3D computer vision and various approaches have been proposed to handle this problem in recent years. Despite the fast evolution of registration algorithms, little attention has been drawn to align point cloud pairs with a low-overlap ratio (≤30%). In this paper, we propose a new neural network architecture named the DOANet, an end-to-end trainable fully convolutional neural network, to deal with the problem of registration with low overlap. In particular, we leverage sparse convolutions to extract robust and discriminative dense features, and further utilize attention mechanisms to explicitly predict the overlap region for point cloud fragments. The receptive field of our local point patches is constrained within the overlap region generated on the fly, which prevents the local point features from being contaminated by geometric information outside the overlap region. Experiments indicate the effectiveness and advantages of our method on both 3DLoMatch (all pairs under 30% overlap-ratio) and 3DMatch and illustrate obvious improvement over the existing results. © 2021 IEEE

关键词： Convolution

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Network Situation Awareness Model Based on Incomplete Information Game 5th

Network Situation Awareness Model Based on Incomplete Inform...

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5th International Conference on Security and Privacy in New computing Environments, SPNCE 2022

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Wang, Yanxia Liu, Zhen School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science and Technology Shijiazhuang050000 China Hebei Geological Workers’ University Shijiazhuang050081 China

ISBN: (纸本)9783031306228

Game theory has been widely used in network security situational awareness. However, most of the currently proposed game-based offensive and defensive situational awareness methods are for traffic data, and there are fewer models or methods for analysis using vulnerability data. To overcome these issues, this paper proposes collecting periodic security vulnerability information in the network and utilizing the change in vulnerability status to achieve network security situational awareness. At this time, a network attack and defense game model based on incomplete information is proposed, which uses the state changes of the vulnerability life cycle to model the attack and defense behavior, calculates the benefits of both attack and defense through the evaluation of the exploitability of the vulnerability, and then quantifies the security situation value. We carried out the experiments using the vulnerability dataset, which was obtained by scanning the IP addresses of several enterprises in Hebei Province, China. The experimental results show that the approach of using network security vulnerabilities to assess network security status is feasible. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

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Shared Incentive System for Clinical Pathway Experience

Shared Incentive System for Clinical Pathway Experience

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Weiqi Dai Wenhao Zhao Xia Xie Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Hainan University Haikou China

The phenomenon of unbalanced regional medical resources has led to large differences in the implementation experience of clinical pathways in hospitals with different medical levels. However, due to the fear of privacy leakage and the lack of sharing motivation, there is a lack of effective collaborative communication channels for clinical pathways among medical institutions. In response to these problems, we propose a blockchain-based sharing incentive scheme for clinical pathway experience, which uses the clinical pathway implementation effect evaluation system to evaluate the quality of clinical pathway experience data. Specifically, we designe a two-phase cross-domain shared transaction model for the transaction of clinical pathway experience data. Moreover, we introduce the shared transaction alliance committee to verify and review the transaction, and solve the problems in the transaction in the arbitration phase. Finally, the functional test results show that the system meets the experience sharing incentive requirements, and the performance test results show that the TPS of sharing clinical pathway experience read and writed can reach around 600 and 1000, and the transaction latency of each phase is within 3 s.

关键词： Privacy Hospitals Collaboration Communication channels data models Security Incentive schemes

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Hybrid Gradient Descent and Sea Lion Optimization Algorithm (H-GD-SLnO) to Optimize Task Scheduling in Fog computing Environment

Hybrid Gradient Descent and Sea Lion Optimization Algorithm ...

引用

Advanced computing Technologies (ICoACT), International Conference on

作者： Rakesh Premkumar N. Jagadish Kumar L Maria Michael visuwasam Gopalakrishnan Arjunan A. Vinothini C. Tharun Kumar School of informatics and computing Indiana University Bloomington USA Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Dept of AI & DS RMK College of engineering and technology Puduvoyal Chennai Accenture Bangalore India Dept of CSE Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Dept of CSBS Sri Sai Ram Engineering college Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The architectures associated with cloud and fog computing are fundamentally important for the decentralization of computational tasks, as they facilitate the efficient processing of various user demands while simultaneously supporting extensive Internet of Things (IoT) networks that are increasingly prevalent in today's technological landscape. As the complexity and scale of computational loads continue to expand at a rapid pace, it becomes imperative to implement effective task scheduling mechanisms across these heterogeneous architectures, as this is vital for the optimization of resource utilization and the significant reduction of latency that can adversely affect system performance. This Paper centers on optimization, highlighting the novel integration of Gradient Descent (GD) and the Sea Lion Optimization Algorithm (SLnO). It proposes a harmonious methodology for addressing task scheduling challenges in Fog computing by utilizing the accuracy of GD for localized searches and the exploratory strengths of SLnO for overarching optimization. The hybrid methodology (H-GD-SLnO) was evaluated using synthetic datasets and real grid workloads, including DAS-2 and AuverGrid, as well as Hadoop tasks like Terasort. The simulation outcomes reveal that (H-GD-SLnO) surpasses numerous pre-existing algorithms, realizing reductions in mean job completion time as well as average scheduling delay and enhancements in resource utilization and throughput. These results emphasize the efficacy of our proposed methodology in meeting the rigorous task scheduling demands within cloud and fog computing environments.

关键词： Cloud computing Processor scheduling Throughput Virtual machines Scheduling Resource management Internet of Things Marine animals Optimization Edge computing

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multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Information

arXiv

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arXiv 2024年

作者： Zhu, Hongjun Huang, Jiaohang Chen, Kuo Ying, Xuehui Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, and monitoring the progression of brain tumors. However, due to the variability in tumor appearance, size, and intensity across different MRI modalities, automated segmentation remains a challenging task. In this study, we propose a novel Transformer-based framework, multiPI-TransBTS, which integrates multi-physical information to enhance segmentation accuracy. The model leverages spatial information, semantic information, and multi-modal imaging data, addressing the inherent heterogeneity in brain tumor characteristics. The multiPI-TransBTS framework consists of an encoder, an Adaptive Feature Fusion (AFF) module, and a multi-source, multi-scale feature decoder. The encoder incorporates a multi-branch architecture to separately extract modality-specific features from different MRI sequences. The AFF module fuses information from multiple sources using channel-wise and element-wise attention, ensuring effective feature recalibration. The decoder combines both common and task-specific features through a Task-Specific Feature Introduction (TSFI) strategy, producing accurate segmentation outputs for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET) regions. Comprehensive evaluations on the BraTS2019 and BraTS2020 datasets demonstrate the superiority of multiPI-TransBTS over the state-of-the-art methods. The model consistently achieves better Dice coefficients, Hausdorff distances, and Sensitivity scores, highlighting its effectiveness in addressing the BraTS challenges. Our results also indicate the need for further exploration of the balance between precision and recall in the ET segmentation task. The proposed framework represents a significant advancement in BraTS, with potential implications for improving clinical outcomes for brain tumor patients. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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An autoencoder-like nonnegative matrix co-factorization for improved student cognitive modeling 24

An autoencoder-like nonnegative matrix co-factorization for ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Shenbao Yu Yinghui Pan Yifeng Zeng Prashant Doshi Guoquan Liu Kim-Leng Poh Mingwei Lin College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University UK Intelligent Thought and Action Lab School of Computing University of Georgia Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

ISBN: (纸本)9798331314385

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models.

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