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Serverless Microservice Architecture for Cloud-Edge intelligence in Sensor Networks

ieee SENSORS JOURNAL 2025年第5期25卷 7875-7885页

作者： Loconte, Davide Ieva, Saverio Gramegna, Filippo Bilenchi, Ivano Fasciano, Corrado Pinto, Agnese Loseto, Giuseppe Scioscia, Floriano Ruta, Michele Di Sciascio, Eugenio Polytech Univ Bari Dept Elect & Informat Engn I-70125 Bari Italy LUM Giuseppe Degennaro Univ Dept Engn I-70010 Casamassima Italy

Machine learning (ML) is increasingly exploited in a wide range of application areas to analyze data streams from large-scale sensor networks, train predictive models, and perform inference. The cloud-edge intelligence (CEI) computing paradigm integrates cloud infrastructures for resource-intensive ML tasks with devices at the border of a local network for distributed data preprocessing, small-scale model training, and prediction tasks. This can achieve a tunable trade-off of ML accuracy with improved data privacy, response latency, and bandwidth usage. Prevalent CEI architectures are based on microservices encapsulated in containers, but serverless computing is emerging as an alternative model. It is based on stateless event-driven functions to facilitate the development and provisioning of application components, increase the infrastructure elasticity, and reduce management effort. This article proposes a novel CEI framework for sensor-based applications, exploiting serverless computing for data management and ML tasks. Small-scale model training occurs at the edge with local data for quick prediction response, while large-scale models are trained in the cloud with the full sensor network data, and then, they are fed back to edge nodes for a progressive accuracy improvement. A fully functional prototype has been built by leveraging open-source software tools, selected devices for field sensing and edge computing (EC), and a commercial cloud platform. Experiments validate the feasibility and sustainability of the proposal, compared with an existing container-oriented microservice architecture.

关键词： Cloud computing Sensors Training Computer architecture Microservice architectures computational modeling data models Accuracy Internet of Things Intelligent sensors Cloud-edge intelligence (CEI) machine learning (ML) microservices sensor networks serverless computing

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Joint Task Partitioning and Resource Allocation in UAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning

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ieee Internet of Things Journal 2025年第11期12卷 15453-15466页

作者： Liang, Hongbin Zhang, Han Ale, Laha Hong, Xintao Wang, Lei Jia, Qiong Zhao, Dongmei Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Chinese Academy of Sciences Laboratory of Noise and Vibration Research Institute of Acoustics Beijing100190 China McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs' restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods. © 2014 ieee.

关键词： Convex optimization

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Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

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ieee Transactions on computational Social Systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 ieee.

关键词： Matrix algebra

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Symmetric Fine-Tuning for Improving Few-Shot Object Detection

Symmetric Fine-Tuning for Improving Few-Shot Object Detectio...

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Mpampis, Emmanouil Passalis, Nikolaos Tefas, Anastasios Aristotle University of Thessaloniki Computational Intelligence and Deep Learning Group AlIA Lab. Department of Informatics Thessaloniki Greece

ISBN: (纸本)9781665430654

Object detection plays a crucial role in automated image analysis by identifying and localizing objects within an image. One-stage Deep Learning (DL)-based object detectors have achieved impressive results, primarily due to large-scale datasets available for training them. However, these approaches rely heavily on abundant labeled data, posing challenges when only a few samples per class are available. To this end, few-shot object detection approaches have been proposed. Among them, fine-tuning the final detection head while keeping the feature extractor/backbone frozen is a commonly used approach for few-shot object detection. This approach effectively utilizes pre-existing knowledge encoded in the backbone, using a small number of samples to learn new object categories. However, in this paper, we argue that fine-tuning only the last layers may limit accuracy and lead to overfitting if the initial layers of the detection head are not adapted for the new task. The data processing inequality, which states that information lost in early network layers cannot be recovered in subsequent ones, supports this argument. To address this issue, we propose a symmetric fine-tuning method that involves both the first and last layers of the detection head, aiming to maintain a fixed trainable parameter budget while strategically selecting parameters for fine-tuning. Experimental results demonstrate the effectiveness and efficiency of this approach and open up several interesting future research directions. © 2023 ieee.

关键词： Few-shot Fine-Tuning Object detection Symmetric Fine-Tuning

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Efficient Layer Compression Without Pruning

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ieee TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 4689-4700页

作者： Wu, Jie Zhu, Dingshun Fang, Leyuan Deng, Yue Zhong, Zhun Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Beihang Univ Sch Astronaut Beijing 102206 Peoples R China Sci & Technol Space Intelligent Control Lab Beijing 100191 Peoples R China Beihang Univ Inst Artificial Intelligence Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

Network pruning is one of the chief means for improving the computational efficiency of Deep Neural Networks (DNNs). Pruning-based methods generally discard network kernels, channels, or layers, which however inevitably will disrupt original well-learned network correlation and thus lead to performance degeneration. In this work, we propose an Efficient Layer Compression (ELC) approach to efficiently compress serial layers by decoupling and merging rather than pruning. Specifically, we first propose a novel decoupling module to decouple the layers, enabling us readily merge serial layers that include both nonlinear and convolutional layers. Then, the decoupled network is losslessly merged based on the equivalent conversion of the parameters. In this way, our ELC can effectively reduce the depth of the network without destroying the correlation of the convolutional layers. To our best knowledge, we are the first to exploit the mergeability of serial convolutional layers for lossless network layer compression. Experimental results conducted on two datasets demonstrate that our method retains superior performance with a FLOPs reduction of 74.1% for VGG-16 and 54.6% for ResNet-56, respectively. In addition, our ELC improves the inference speed by 2x on Jetson AGX Xavier edge device.

关键词： Deep neural networks layer compression pruning image classification

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Domain-Specific Large Language Model Finetuning using a Model Assistant for Financial Text Summarization

Domain-Specific Large Language Model Finetuning using a Mode...

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Avramelou, Loukia Passalis, Nikolaos Tsoumakas, Grigorios Tefas, Anastasios Computational Intelligence and Deep Learning Group Aiia Lab Aristotle University of Thessaloniki Intelligent Systems Laboratory Department of Informatics Thessaloniki Greece

ISBN: (纸本)9781665430654

The financial market and public opinion are cor-related. This means that changes in the financial market can result in changes to public opinion and changes to public opinion can result in changes to the financial market. Accordingly, it is essential for understanding and interacting with the financial market to gather text content from online sources and process it. As a result of the rapid growth of social media and other online sources, we have seen an exponential rise in data, particularly textual data, in recent years. It can be difficult for a person to read, let alone process, the massive volumes of data generated every day. This indicates that we need automated methods for processing textual data and extracting useful information. Automated text summarization is a method of shortening huge amounts of text without losing essential information. Transformers, which can efficiently manage and analyze textual data, are state-of-the-art text summarization models. However, developing such an automated text summarization model specialized in a domain (e.g. finance) can be challenging since we lack necessary domain-specific summarization datasets. In this work, we propose a pipeline for fully automating the finetuning of a text summarization model in a specific domain, namely cryptocurrency domain, without the involvement of human annotators. To this end, we introduce a novel method for self-improvement of text summarization models which relies on a model assistant which encodes domain knowledge, enabling finetuning text summarization models in specific domains in which we lack specific-domain summarization datasets. The proposed method is evaluated on a cryptocurrency-related text summarization problem and three well-known Large Language Models (LLMs) used for text summarization. © 2023 ieee.

关键词： Finetuning Large Language Models Natural Languange Processing Text Summarization

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From Cloud-Edge to Edge-Edge Continuum: the Swarm-Based Edge Computing Systems 20

From Cloud-Edge to Edge-Edge Continuum: the Swarm-Based Edge...

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ieee Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive intelligence and Computing, Intl Conf on Cloud and big data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)

作者： Carnevale, Lorenzo Ortis, Alessandro Fortino, Giancarlo Battiato, Sebastiano Villari, Massimo Univ Messina Dept Math & Comp Sci Phys & Hearth Sci Messina Italy Univ Catania Dept Math & Comp Sci Catania Italy Univ Calabria Dept Informat Modeling Elect & Syst Arcavacata Di Rende Italy

ISBN: (数字)9781665462976

ISBN: (纸本)9781665462976

Modern cloud-edge-device computational platforms does not match the needs of artificial intelligence at the edge of the network. Indeed, the lack of computing power allows that only some AI processes can be performed on edge devices, having also to consider their constrained energy capacity. Moreover, the lack of computing continuum between nodes of the same layer, i.e., edge-to-edge, allows to only operate independently within the layer by sensing the environment where nodes stay. In this paper, we propose a lightweight framework for collaborative nodes with decentralized edge intelligence. Organized like a swarm, the groups of nodes emphasize the edge-to-edge continuum of the device-edge-cloud paradigm. This supports a paradigm shift from programming environments for individual devices to dynamic and cooperating groups of nodes. The nodes' coordination relays on green overlay and offloading mechanisms. Innovative mesh architectures with mixed topologies allow building overlays for having swarm coordination. Tasks offloading exploits the overlays to balance the swarm in near-real-time, according to forecasted energy consumption. Stemming from the proposed reference architecture, we also discuss a series of open challenges, which we believe represent relevant research directions in the nearest future.

关键词： Cloud-Edge Continuum Swarm Computing Distributed intelligence Offloading Overlay Network Energy-Aware

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ieee SSCI 2014 - 2014 ieee symposium Series on computational intelligence - cibd 2014: 2014 ieee symposium on computational intelligence in big data, Proceedings

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational...

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2014 ieee symposium on computational intelligence in big data, cibd 2014

ISBN: (纸本)9781479945412

The proceedings contain 18 papers. The topics discussed include: sparse Bayesian approach for feature selection;sentiment analysis for various SNS media using naive Bayes classifier and its application to flaming detection;increasing big data front end processing efficiency via locality sensitive bloom filter for elderly healthcare;challenges in designing an online healthcare platform for personalised patient analytics;application of sparse matrix clustering with convex-adjusted dissimilarity matrix in an ambulatory hospital specialist service;microarray big data integrated analysis for the prediction of robust diagnostics signature for triple-negative breast cancer;mining the prescription-symptom regularity of TCM for HIV/AIDS based on complex network;regularity of herbal formulae for HIV/AIDS patients with syndromes based on complex networks;and methods and technologies of traditional Chinese medicine clinical information datamation in real world.

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Welcome Message from the Chairs

Proceedings - IEEE International Symposium on Circuits and S...

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Proceedings - ieee International symposium on Circuits and Systems 2023年 2023-May卷

作者： Rabaey, Jan Murmann, Boris Ieee Iscas 2023

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Deep Neural Architectures for Highly Imbalanced data in Bioinformatics

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第8期31卷 2857-2867页

作者： Bugnon, Leandro A. Yones, Cristian Milone, Diego H. Stegmayer, Georgina Univ Nacl Litoral UNL Natl Sci & Tech Res Council CONICET Res Inst Signals Syst & Computat Intelligence Sin RA-3000 Santa Fe Argentina

In the postgenome era, many problems in bioinformatics have arisen due to the generation of large amounts of imbalanced data. In particular, the computational classification of precursor microRNA (pre-miRNA) involves a high imbalance in the classes. For this task, a classifier is trained to identify RNA sequences having the highest chance of being miRNA precursors. The big issue is that well-known pre-miRNAs are usually just a few in comparison to the hundreds of thousands of candidate sequences in a genome, which results in highly imbalanced data. This imbalance has a strong influence on most standard classifiers and, if not properly addressed, the classifier is not able to work properly in a real-life scenario. This work provides a comparative assessment of recent deep neural architectures for dealing with the large imbalanced data issue in the classification of pre-miRNAs. We present and analyze recent architectures in a benchmark framework with genomes of animals and plants, with increasing imbalance ratios up to 1:2000. We also propose a new graphical way for comparing classifiers performance in the context of high-class imbalance. The comparative results obtained show that, at a very high imbalance, deep belief neural networks can provide the best performance.

关键词： Neurons Self-organizing feature maps Bioinformatics Training Genomics Computer architecture Task analysis Bioinformatics deep neural architectures high-class imbalance precursor microRNA (pre-miRNA) classification

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