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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

ISBN: (纸本)9783903176553

We study joint downlink-uplink beamforming design for wireless federated learning (FL) with a multi-antenna base station. Considering analog transmission over noisy channels and uplink over-the-air aggregation, we derive the global model update expression over communication rounds. We then obtain an upper bound on the expected global loss function, capturing the downlink and uplink beamforming and receiver noise effect. We propose a low-complexity joint beamforming algorithm to minimize this upper bound, which employs alternating optimization to breakdown the problem into three subproblems, each solved via closed-form gradient updates. Simulation under practical wireless system setup shows that our proposed joint beamforming design solution substantially outperforms the conventional separate-link design approach and nearly attains the performance of ideal FL with error-free communication links. © 2023 IFIP.

关键词： Beamforming

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A Novel Fast and High Performance Reconstruction Voltage Perdictive Controller for three Phase Multilevel Inverters

A Novel Fast and High Performance Reconstruction Voltage Per...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Jafarian, Yousefreza Salari, Omid Youssef, Mohamed Z. Bakhshai, Alireza Jain, Praveen Electrical and Computer Engineering Queen's University Kingston Canada Electrical and Computer Engineering Ontario Tech University Oshawa Canada

ISBN: (纸本)9798350364446

This paper introduces a novel high-performance reconstruction voltage predictive control (RVPC) scheme with broad applicability to three-phase multilevel inverters. The proposed modulation technique presents a significant reduction in computational time compared to the conventional finite control set model predictive control (FCS-MPC) methods, eliminating the need to search the entire state space for optimal switching state. Additionally, the proposed RVPC not only enhances the DC link utilization parameter but also improves inverter performance by reducing voltage THD compared to conventional MPC. The proposed current control technique is tested on a three-level T-type Neutral Point-Clamped (T-NPC) topology, with introducing a modified level-shifted SPWM for DC link voltage balancing approach to keep the voltage across DC link capacitors balanced. The design, simulation, and experimental results are given in the paper to prove the merits of the proposed control scheme. © 2024 IEEE.

关键词： Predictive control systems

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Intrusion Detection for Wireless Sensor Network Using Graph Neural Networks

Intrusion Detection for Wireless Sensor Network Using Graph ...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Gharavian, Vida Khosrowshahli, Rasa Mahmoud, Qusay H. Makrehchi, Masoud Rahnamayan, Shahryar Lab Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Engineering Department St. CatharinesON Canada

ISBN: (纸本)9781665430654

Wireless Sensor Networks (WSNs) are rapidly employed in many applications due to highly demanded autonomous systems. These networks are of immense importance due to their ability to collect data from remote and challenging environments, their impact on various sectors like healthcare, agriculture, industry, environment, and their role in enabling smart technologies for a sustainable, secure, and connected future. Nevertheless, these systems can be attacked by adversaries. Usually, the WSNs are designed with lightweight sensor nodes with limited computation and memory resources. Therefore, employing a firewall system on every sensor node is unacceptable. This paper tackled this problem with a very lightweight Graph Neural Network-based model. The conducted experiment performed in this work demonstrates promising attack-type detection by our proposed approach to the WSN-DS dataset. In this article, our proposed method is compared with other the-state-of-the-art works, and we could discover all Blackhole attacks, one of the most common Denial-of-Service attacks. © 2023 IEEE.

关键词： Denial of Service DoS Graph Neural Network GraphSAGE Intrusion Detection LEACH Wireless Sensor Network WSN

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A Machine Learning Based Approach to Detect Fault Injection Attacks in IoT software Systems

A Machine Learning Based Approach to Detect Fault Injection ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Gangolli, Aakash Mahmoud, Qusay H. Azim, Akramul Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

With the rapid growth of Internet of Things (IoT) applications, the security of these systems has become critical. Fault injection attacks are a type of physical attack on the hardware components of an IoT system. These attacks cause the IoT system software to behave abnormally, which the adversaries exploit. Typically, these attacks have been detected through the use of a separate hardware detection mechanism, which is expensive and itself vulnerable to attack. The purpose of this paper is to propose a machine learning based approach to detect the attacks by monitoring specific run-time software parameters in the live environment of an IoT system. The proposed approach generates a labelled dataset by injecting instruction-level faults into the software executable, which is then used to train a machine learning model that can predict whether the IoT software system is currently being affected by a fault injection attack. Using a software fault injection tool to create a labelled dataset enables the use of supervised machine learning techniques, which produce more accurate prediction results than unsupervised techniques. The machine learning model can be used in the live environment of an IoT software system to monitor specific run-time software properties in order to detect the effects of a fault injection attack on the software. Additionally, the model classifies the type of fault introduced into the software as a result of the attack, which can be used to determine the necessary corrective action. © 2022 IEEE.

关键词： Internet of things

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Wide-ranging parameter extraction of Lithium-ion Batteries to Estimate State of Health using Electrochemical Impedance Spectroscopy 1

Wide-ranging parameter extraction of Lithium-ion Batteries t...

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1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Anekal, Latha Samanta, Akash Williamson, Sheldon Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350398069

To ensure effective and safer utilization of Lithium-Ion Batteries (LIB), accurate State Estimation particularly State of Health (SOH) is extremely essential, especially in automotive applications. Accurate information about SOH not only plays a crucial role in health-conscious battery management while at the same time helps to prognose battery fault and replacement. Battery SOH cannot be measured directly. Therefore, typically equivalent models and data-driven estimation techniques are used to obtain SOH through external measurements. Feature vectors required for SOH estimation can be very effectively extracted by Electrochemical Impedance Spectroscopy (EIS). Still, existing EIS-based methods have several limitations, issues, and challenges. Thus, a comprehensive review of state-of-the-art EIS-based SOH estimation techniques is presented in this paper to expedite the research effort toward developing a more advanced and practical EIS method for accurate and real-time SOH estimation. © 2022 IEEE.

关键词： Lithium-ion batteries

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Feature Selection-driven Bias Deduction in Histopathology Images: Tackling Site-Specific Influences 13

Feature Selection-driven Bias Deduction in Histopathology Im...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kheiri, Farnaz Bidgoli, Azam Asilian Makrehchi, Masoud Rahnamayan, Shahryar Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada Wilfrid Laurier University Dept. of Computer Science Waterloo Canada Brock University Dept. of Engineering St. Catharines Canada

ISBN: (纸本)9798350308365

The emergence of bias in deep neural models represents a significant reliability concern, which may lead to overoptimistic results on seen data while compromising the model's ability to generalize effectively on unseen datasets. Recent studies conducted on The Cancer Genome Atlas (TCGA), which is a publicly available repository of histopathology images, reveal that the TCGA cancerous features extracted by deep neural networks surprisingly are able to discriminate slides based on their origin sites. This finding undoubtedly indicates the existence of site-specific patterns embedded in the extracted features learned by deep networks rather than focusing on histomorphologic patterns. Consequently, this biased behavior raises concerns about the reliability of these networks. This observation motivates us to conduct a series of experiments in which we present two distinct evolutionary feature selection strategies, each differentiated by its objective function evaluation. The primary goal is to select the features with a minimized foot-print of data source signatures, thereby ensuring a more accurate and site-independent cancer classification. We have conducted nine comprehensive independent experiments across nine cancer types, employing each evolutionary strategy. The comparison between results obtained through evolutionary strategies and the original feature sets highlights the substantial impact of feature selection methods on bias reduction while maintaining accuracy in cancer-type discrimination. Furthermore, the comparison of the two strategies with each other demonstrates the intricate nature of bias and its integration with cancerous features during the training process. © 2024 IEEE.

关键词： Lung cancer

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Enhancing Diversity in Multi-Objective Feature Selection 13

Enhancing Diversity in Multi-Objective Feature Selection

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Ebrahimi, Sevda Makrehchi, Masoud Lab Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada

ISBN: (纸本)9798350308365

Feature selection plays a pivotal role in the data preprocessing and model-building pipeline, significantly en-hancing model performance, interpretability, and resource efficiency across diverse domains. In population-based optimization methods, the generation of diverse individuals holds utmost importance for adequately exploring the problem landscape, particularly in highly multi-modal multi-objective optimization problems. Our study reveals that, in line with findings from sev-eral prior research papers, commonly employed crossover and mutation operations lack the capability to generate high-quality diverse individuals and tend to become confined to limited areas around various local optima. This paper introduces an augmen-tation to the diversity of the population in the well-established multi-objective scheme of the genetic algorithm, NSGA-II. This enhancement is achieved through two key components: the genuine initialization method and the substitution of the worst individuals with new randomly generated individuals as a re-initialization approach in each generation. The proposed multi-objective feature selection method undergoes testing on twelve real-world classification problems, with the number of features ranging from 2,400 to nearly 50,000. The results demonstrate that replacing the last front of the population with an equivalent number of new random individuals generated using the genuine initialization method and featuring a limited number of features substantially improves the population's quality and, consequently, enhances the performance of the multi-objective algorithm. © 2024 IEEE.

关键词： Genetic algorithms

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Ranking Center-based NSGA-II

Ranking Center-based NSGA-II

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Ibrahim, Amin Makrehchi, Masoud Lab Canada Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Engineering Department St. CatharinesON Canada Ontario Tech University Faculty of Business and Information Technology OshawaON Canada

ISBN: (纸本)9798350337020

Multi-objective optimization is a branch of computation to solve mathematical optimization problems when conflicting multiple objective functions must be simultaneously optimized. Many population-based algorithms, such as NSGA-II are used to find an optimal Pareto set of solutions in a short time. However, previous research works showed the performance of classic NSGA-II is degraded when solving problems with many objectives (M ≥ 5). In this work, we aim to take advantage of center-based sampling scheme to increase the exploration and exploitation capability of NSGA-II algorithm. This sampling strategy has demonstrated promising results on several single-objective evolutionary algorithms such as GA, DE, and PSO. Recently, a novel clustering center-based strategy has been proposed, which motivated us to utilize center-based sampling scheme in NSGA-II to solve multi-objective optimization problems. The outcomes confirm that the proposed clustering center-based NSGA-II is able to effectively solve CEC-2017 multi-objective benchmark problems with 2, 3, 5, 10, and 15 objectives. © 2023 IEEE.

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Real-time Point-to-Point Parameter Tracking for Fault Prognosis of Lithium-ion Batteries Using Electrochemical Impedance Spectroscopy 8

Real-time Point-to-Point Parameter Tracking for Fault Progno...

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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： Anekal, Latha Samanta, Akash Deshpande, Uday Williamson, Sheldon Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada D&v Electronics NC United States

ISBN: (纸本)9798350321128

With the increasing concerns of lithium-ion battery (LIB) failure, which can lead to fire and catastrophic failure in electric vehicles (EVs), this paper proposes an online parameter tracking-based fault prognosis strategy focusing on small frequency windows within the EIS spectrum. Early detection of faults remains an unsolved challenge in the practical application of LIBs, especially in high-power applications such as EVs. The proposed concept leverages the possibility of real-time tracking of internal battery parameters by identifying frequency windows. In most cases, faults and failures originate from a single cell and then propagate to the entire LIB pack. Therefore, an experimental setup consisting of one LIB cell, a battery cycler, and electrochemical impedance spectroscopy (EIS) is used to demonstrate the effectiveness of the proposed concept through battery aging and during faults. Furthermore, the results provide guidelines on the most sensitive battery parameters for predicting failures in practical applications. This information will help in reducing EIS measurement time, data storage, and computational costs without compromising prediction accuracy. © 2023 IEEE.

关键词： battery management system catastrophic failure electric vehicle safety transportation electrification

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