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Push-Sum on Random Graphs: Almost Sure Convergence and Convergence Rate

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第3期65卷 1295-1302页

作者： Rezaienia, Pouya Gharesifard, Bahman Linder, Tamas Touri, Behrouz Univ British Columbia Sauder Sch Business Vancouver BC V6T 1Z4 Canada Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

In this paper, we study the problem of achieving average consensus over a random time-varying sequence of directed graphs by extending the class of so-called push-sum algorithms to such random scenarios. Provided that an ergodicity notion, which we term the directed infinite flow property, holds and the auxiliary states of agents are uniformly bounded away from zero infinitely often, we prove the almost sure convergence of the evolutions of this class of algorithms to the average of initial states. Moreover, for a random sequence of graphs generated using a so-called time-varying -irreducible probability matrix, we establish convergence rates for the proposed push-sum algorithm.

关键词： Convergence Heuristic algorithms Random sequences Protocols Optimization machine learning algorithms Communication networks Distributed optimization optimization algorithms random networks

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Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

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IEEE ACCESS 2023年 11卷 38864-38874页

作者： Belcastro, Loris Marozzo, Fabrizio Orsino, Alessio Talia, Domenico Trunfio, Paolo Univ Calabria Dept Informat Modeling Elect & Syst DIMES I-87036 Arcavacata Di Rende Italy

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning-based algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge- or cloud-only architectures.

关键词： Internet of Things Cloud computing Computer architecture Task analysis Public transportation machine learning algorithms Data models Edge-cloud architecture IoT infrastructure edge computing urban computing smart cities urban mobility

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Design on Early Warning System for Renal Cancer Recurrence Based on CNN-Based Internet of Things

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IEEE ACCESS 2022年 10卷 34835-34845页

作者： Liu, Dong Shao, Jinkai Liu, Honggang Cheng, Wei Shanxi Prov Peoples Hosp Dept Urol Taiyuan 030012 Peoples R China Shanxi Med Univ Shanxi Bethune Hosp Dept Urol Hosp 3 Taiyuan 030032 Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Dept Urol Tongji Med Coll Wuhan 430030 Peoples R China

Kidney cancer is a type of urinary system tumor. The incidence of kidney cancer, which is second only to bladder cancer, has shown an overall upward trend in recent years. However, the early judgment of kidney cancer is still in the imaging biomarker discovery stage. Early detection and treatment cannot be achieved. Based on the natural advantages of the Internet of Things in the medical field, we focused on an intelligent early warning model of renal cancer recurrence and built a renal cancer early warning system integrated with the Internet of Things. We integrated the influencing factors of renal cell carcinoma, constructed a sample set, conducted data analysis and optimized the dataset. Aiming at the instability of renal cancer recurrence, five supervised learning prediction algorithms, including multiple linear regression, Bayesian ridge regression, gradient boosting tree, support vector regression, and convolutional neural network were used to develop a renal cancer recurrence prediction model. The predictive performance of these five algorithms were compared and discussed. Finally, the best renal cancer recurrence prediction model was established by combining a convolutional neural network with an Internet of Things medical framework. This design provided an intelligent early warning system to predict the recurrence time of renal cancer patients. In addition, the warning prompts provided in accordance with the model results can assist doctors in making preliminary judgments of the patient's condition and has a certain auxiliary effect on the clinical diagnosis and treatment of cancer and kidney cancers.

关键词： Cancer Medical diagnostic imaging Analytical models machine learning algorithms Licenses Data mining Psychology Kidney cancer recurrence time recurrence warning system convolutional neural network

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Maximal Correlation Regression

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IEEE ACCESS 2020年 8卷 26591-26601页

作者： Xu, Xiangxiang Huang, Shao-Lun Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Berkeley Shenzhen Inst DSIT Res Ctr Shenzhen 518055 Peoples R China

In this paper, we propose a novel regression analysis approach, called maximal correlation regression, by exploiting the ideas from the Hirschfeld-Gebelein-Renyi (HGR) maximal correlation. We show that in supervised learning problems, the optimal weights in maximal correlation regression can be expressed analytically with the relationships to the HGR maximal correlation functions, which reveals theoretical insights for our approach. In addition, we apply the maximal correlation regression to deep learning, in which efficient training algorithms are proposed for learning the weights in hidden layers. Furthermore, we illustrate that the maximal correlation regression is deeply connected to several existing approaches in information theory and machine learning, including the universal feature selection problem, linear discriminant analysis, and the softmax regression. Finally, experiments on real datasets demonstrate that our approach can obtain performance comparable to the widely used softmax regression based-method.

关键词： Artificial neural networks HGR maximal correlation linear discriminant analysis machine learning algorithms regression analysis softmax regression

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Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection

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IEEE ACCESS 2020年 8卷 174394-174409页

作者： Pawelczyk, Maciej L. Wojtyra, Marek Warsaw Univ Technol Inst Aeronaut & Appl Mech PL-00665 Warsaw Poland

Recent years have shown a noticeable rise in the number of incidents with drones, related to both civilian and military installations. While drone neutralization techniques have become increasingly effective, detection most often relies on professional equipment, which is too expensive to be used for all critical nodes and applications. Therefore, there is a need for drone detection systems that could work on low performance hardware. Its critical component consists of an object detection system. In this article, we introduce a new object detection dataset, built entirely to train computer vision based object detection machine learning algorithms for a task of binary object detection to enable automated, industrial camera based detection of multiple drone objects using camera feed. The dataset expands existing multiclass image classification and object detection datasets (ImageNet, MS-COCO, PASCAL VOC, anti-UAV) with a diversified dataset of drone images. In order to maximize the effectiveness of the model, real world footage was utilized, transformed into images and hand-labelled to create a custom set of 56821 images and 55539 bounding boxes. Additionally, semi-automated labelling was proposed, tested and proved to be very useful for object detection applications. The dataset was divided into train and test subsets for further processing and used to generate 603 easily deployable Haar Cascades as well as 819 high performing Deep Neural Networks based models. They were used to test different object detection methods to determine the long term feasibility of a large scale drone detection system utilizing machine learning algorithms. The study has shown that Haar Cascade can be used as the Minimum Viable Product model for mediocre performance but fails to scale up effectively for a larger dataset compared to the Deep Neural Network model.

关键词： Drones Object detection Task analysis Computational modeling machine learning machine learning algorithms Aerospace engineering aerospace safety artificial intelligence computer vision databases image processing unmanned aerial vehicles

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Comparative study of ML models for IIoT intrusion detection: impact of data preprocessing and balancing

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NEURAL COMPUTING & APPLICATIONS 2024年第13期36卷 6955-6972页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nasif, Ali Bou Injadat, Mohammadnoor Univ Sharjah Coll Comp & Informat Dept Comp Engn Sharjah U Arab Emirates Zarqa Univ Fac Informat Technol Dept Data Sci & Al Zarqa Jordan

This study investigates the effectiveness of six prominent machine learning models-random forest, decision trees, K-nearest neighbor, logistic regression, support vector machines, and Naive Bayes-for intrusion detection systems in industrial Internet of Things environments. The evaluation encompasses the effects of data preprocessing techniques, including feature engineering, data normalization, recoding, and missing data mitigation. Furthermore, the research delves into dataset balancing, examining the effects of six different techniques on model performance. The investigations are conducted using the domain-specific WUSTL-IIOT-2021 dataset, which captures the unique characteristics of IIoT data. The study also investigates multi-class attack identification utilizing an innovative SMOTE-based multi-class balancing approach to tackle dataset imbalances. The results indicate that data preprocessing and intelligent dataset balancing produce consistent enhancements in the classification performance of the selected models across binary and multi-classification tasks. Random forest emerges as the standout algorithm, delivering consistently high performance with computational efficiency.

关键词： Industrial Internet of Things Intrusion detection machine learning algorithms Data normalization Dataset balancing

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Sensing Coverage-Based Cooperative Spectrum Detection in Cognitive Radio Networks

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IEEE SENSORS JOURNAL 2019年第13期19卷 5325-5332页

作者： Kim, Junhee Choi, Jihwan P. Daegu Gyeongbuk Inst Sci & Technol Dept Informat & Commun Engn Daegu 42988 South Korea

With the advancement of the Internet of Things (IoT), conventional principles of spectrum allocation cannot mitigate spectrum depletion so that a cognitive radio technology is proposed as a solution. The hidden primary user (PU) problem, however, is a critical issue in cognitive radio networks, because spectrum sensing nodes (SNs) can misclassify spectrum occupancy. To cope with this, machine learning-based cooperative spectrum sensing schemes (CSSs) have been proposed. The CSSs without considering the node placement, however, are still faced with the hidden PU problem. In this paper, we present how to place SNs to guarantee the performance of machine learning-based CSSs. We verify that the hidden PU problem makes the overlap of data distribution, which deteriorates the spectrum sensing ability. Based on the Kullback-Leibler divergence, analytical expressions for the spectrum sensing coverage of a single SN are derived. Then, we propose a strategy on how to place a few SNs to cover the whole area of the PU and prove the feasibility of our proposal by the experiment results.

关键词： Cognitive radio networks spectrum sensing coverage opportunistic spectrum access machine learning algorithms

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Indoor Positioning System Using Synthetic Training and Data Fusion

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IEEE ACCESS 2021年 9卷 115687-115699页

作者： Assayag, Yuri Oliveira, Horacio Souto, Eduardo Barreto, Raimundo Pazzi, Richard Univ Fed Amazonas Inst Comp BR-69067005 Manaus Amazonas Brazil Ontario Tech Univ UOIT Fac Business & Informat Technol Oshawa ON L1H 7K4 Canada

Indoor Positioning Systems (IPSs) are used to estimate the position of mobile devices in indoor environments. Fingerprinting is the most used technique because of its higher accuracy. However, this technique requires a labor-intensive training phase that measures the Received Signal Strength Indicator (RSSI) at all Reference Points (RPs) locations. On the other hand, model-based IPSs use signal propagation models to estimate distances from RSSI. Thus, they do not require expensive training but result in higher positioning errors. In this work, we propose SynTra-IPS (Synthetic Training Indoor Positioning System), a hybrid approach between a fingerprint and a model-based IPS that uses synthetic, simulated datasets combined with data fusion techniques to eliminate the fingerprint collection cost. In our solution, we use the map of the scenario, with known anchor nodes' positions and the log-distance signal propagation model, to generate several synthetic, model-based, fingerprint training datasets. In the online phase of our solution, the positions estimated by the several synthetic datasets using K-Nearest Neighbors (KNN) are combined using data fusion techniques into a single, more accurate position. We evaluated the performance of our SynTra solution in a real-world, large-scale environment using mobile devices with Bluetooth Low Energy (BLE) technology, and we compared our solution to classic approaches from the literature. Our results show that SynTra can locate mobile devices with an average error of only 2.36 m while requiring no real-world environment training.

关键词： Training Mobile handsets IP networks Data integration Location awareness machine learning algorithms Indoor environment Indoor localization fingerprint path-loss propagation model synthetic dataset

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Byzantine Resilient Non-Convex SCSG With Distributed Batch Gradient Computations

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2021年 7卷 754-766页

作者： Bulusu, Saikiran Khanduri, Prashant Kafle, Swatantra Sharma, Pranay Varshney, Pramod K. Syracuse Univ Dept EECS Syracuse NY 13244 USA Univ Minnesota Dept ECE Minneapolis MN 55455 USA

Distributed learning is an important paradigm in the current machine learning algorithms with large datasets. In this paper, distributed stochastic optimization problem of minimizing a nonconvex function in an adversarial setting is considered. A robust variant of the stochastic variance-reduced algorithm is proposed. In the distributed setup, we assume that a fraction of worker nodes (WNs) can be Byzantines. We assume that the batch gradients are computed at the WNs and the stochastic gradients are computed at the central node (CN). We provide the convergence rate of the proposed algorithm which employs the design of a novel filtering rule that is independent of the problem dimension. Furthermore, we capture the effect of Byzantines present in the network on the convergence performance of the algorithm. We evaluate the performance of the proposed algorithm and present the simulation results using real world datasets, in addition to providing the theoretical guarantees.

关键词： Convergence Linear programming Optimization machine learning algorithms Distance learning Computer aided instruction Stochastic processes Nonconvex optimization distributed optimization variance reduction Stochastic gradient descent

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Recognition of Trip-Based Aggressive Driving: A System Integrated With Gaussian Mixture Model Structured of Factor-Analysis, and Hierarchical Clustering

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 20442-20451页

作者： Wang, Junhua Xu, Wenxiang Fu, Ting Jiang, Rui Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China China Shandong Int Econ & Tech Cooperat Grp Ltd Investment & Dev Dept Jinan 250098 Shandong Peoples R China

Recognition of aggressive driving behavior helps future research and practices in Intelligent Transport Systems. This paper tries to briefly explain the concepts related to aggressive driving behavior and introduces a system integrated with a novel machine learning algorithm for the recognition of trip-based aggressive driving behavior. The algorithm is a Gaussian Mixture Model (GMM) structured with Factor Analysis (FA), and Hierarchical Clustering (HC): common factors were extracted using FA, which is further applied to HC and GMM in the recognition of trip-based aggressive driving. The system is applied in a case study using data from the Shanghai Naturalistic Driving Study, for simulating data collection using the Advanced Driving Assistance System (ADAS) system in a real-traffic situation. Three behavior types (cautious, regular, and aggressive driving) were successfully clustered. For validity, the real aggressive driving behavior records were extracted based on the video, and the proposed system was compared with existing recognition methods. Results indicate that the accuracy of aggressive driving recognition of the system is higher than others (accuracy = 87%). This paper provides a reference in defining and determining aggressive driving, and a robust system for aggressive driving behavior recognition along with the trained algorithm, which can be used in real-world applications for improving driving safety with the applications in ADAS systems, auto-insurance industry.

关键词： Behavioral sciences Vehicles Data models Clustering algorithms Computational modeling machine learning algorithms Dimensionality reduction Aggressive driving driving behavior mixture model hierarchical clustering Gaussian mixture model

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