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2023 IEEE International Conference on Big Data, BigData 2023

作者： Mutakabbir, Abdul Ajila, Samuel A. Carleton University Dept. of Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350324457

Long-term air pollution forecasting is essential for making public policies and issuing warnings. This will reduce the impact of pollution on the environment and human health. This research focuses on achieving long-term forecasting of air pollution attributes $(PM 2.5, PM_{10}, SO_{2}$, $NO_{2}, CO$, and $O_{3})$ by providing minimal historical data as input to the model. The best-performing models in this research produced between a 1% increase in RMSE for certain pollutants to a 50% decrease in RMSE for other air pollution attributes (except $CO)$ compared to the initial research on compositional learning, while being able to forecast the trend and seasonality accurately for more than one year into the future. The training time for each pollution attribute model dropped to 4 seconds compared to 1800 seconds in the case of the compositional learning method. This paper also delves into the challenges with long-term forecasting for the Beijing air quality dataset and discusses approaches to overcome these challenges. © 2023 IEEE.

关键词： Air Quality Index ARIMA Bagging Big Data Analytics Boosting Deep Learning Explainable AI Forecasting Machine Learning NeuralProphet Random Committee Random Forest Timeseries Forecasting

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Optimizing DDoS Detection with Time Series Transformers 34

Optimizing DDoS Detection with Time Series Transformers

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34th Annual International Conference on Collaborative Advances in Software and Computing, CASCON 2024

作者： Ejikeme, Chibuike Kahani, Nafiseh Ajila, Samuel A. Carleton University Dept. of Systems and Computer Eng. OttawaON Canada

ISBN: (纸本)9798331504830

Distributed Denial of Service (DDoS) attacks pose severe risks to large networks by disrupting services. Effective detection and response are crucial. Despite progress, current DDoS detection methods need improvement in adaptability and scalability to manage growing network traffic complexity. Existing models often fail to generalize across various network environments, protocols or attack types. This research introduces a hybrid model combining a Time Series Transformer (TST) with feature engineering to enhance DDoS detection and classification. Based on an experimental evaluation using the CICDDoS2019 and CICIoT2023 datasets, which encompass diverse DDoS attack types and network environments, the proposed model outperforms RNN, BiLSTM, and TransformerRNN models. The TST approach achieved an accuracy of 0.971 and an F-beta score of 0.701 on the CICDDoS2019 dataset, and an accuracy of 0.981 and an F-beta score of 0.952 on the CICIoT2023 dataset. We also compare the TST approach's performance across different network protocols for DDoS classification, analyzing the model's ability to detect attacks within various protocols, such as TCP, UDP, and ICMP. © 2024 IEEE.

关键词： Internet of things

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TESTING METHODOLOGY FOR DEVS MODELS IN CADMIUM

TESTING METHODOLOGY FOR DEVS MODELS IN CADMIUM

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2024 Winter Simulation Conference, WSC 2024

作者： Winstanley, Curtis Wainer, Gabriel Dept. of Systems and Computer Eng. Carleton University OttawaON Canada

ISBN: (纸本)9798331534202

The practice of testing in modeling and simulation software development can be a very lengthy and tedious process but is arguably one of the most important phases in the software development lifecycle. As the complexity of a simulation model increases, so does the amount of testing to thoroughly verify and validate and to achieve adequate quality assurance of the software. This paper introduces a testing framework that is used to assist in proving the validity of DEVS atomic models in the open-source simulation tool Cadmium. Furthermore, this framework utilizes the ChatGPT Application Programming Interface (API) to help lighten the workload involved in testing those DEVS atomic models. We show how to use the framework using the Cadmium simulator to show the effectiveness of the framework. © 2024 IEEE.

关键词： Software testing

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Optimal Detection in Barrage-Jammed Single-Input Double-Output systems

Optimal Detection in Barrage-Jammed Single-Input Double-Outp...

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2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Almahorg, Khalid A. Gohary, Ramy H. Carleton University Systems and Computer Eng. Dept. Ottawa Canada

ISBN: (纸本)9798350362312

We derive the likelihood functions and the maximum likelihood (ML) detectors for single-input double-output (SIDO) communication systems. The received signals are contaminated by a Gaussian noise and a non-Gaussian signal induced by the Gaussian transmissions of a proactive continuous single-antenna jammer over an unknown complex 2× 1 Gaussian vector channel. We consider cases in which either full channel distribution information (CDI), or partial CDI about the transmitter channel and the jammer channel is available at the receiver. The vector channels considered herein interweave the components of the received signal vector, rendering the derivation of the likelihood function a daunting task for more than two receive antennas. The interweaving of the received signal components prevents the optimal ML detector for unit-norm constellations from reducing to the corresponding Gaussian approximation based detector. This contrasts with the scalar channel case, wherein the two detectors are equivalent for unit-norm constellations. Simulation results show that the difference between the two detectors can be significant, especially when the transmitter and jammer channels have substantial line-of-sight components. © 2024 IEEE.

关键词： Transmitters

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HANDLING ASYNCHRONOUS INPUTS IN DEVS BASED REAL-TIME KERNELS

HANDLING ASYNCHRONOUS INPUTS IN DEVS BASED REAL-TIME KERNELS

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2024 Winter Simulation Conference, WSC 2024

作者： Govind, Sasisekhar Wainer, Gabriel Dept. of Systems and Computer Eng. Carleton University OttawaON Canada

ISBN: (纸本)9798331534202

Real-time systems are complex to design and implement. Various modelling and simulation techniques are employed to make this task more structured and efficient. However, there is often a disconnect between modeling for simulation and development for deployment. In this paper we discuss a technique to bridge this gap between simulation and deployment, specifically dealing with a framework to handle asynchronous inputs into a system developed using the Discrete Event System Specification. Further, this paper presents a case study that demonstrates the effectiveness of the framework, and the congruence between simulation and deployment of a real-time system is determined. © 2024 IEEE.

关键词： Real time systems

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Downlink Covariance Estimation in URA FDD Massive MIMO systems 48

Downlink Covariance Estimation in URA FDD Massive MIMO Syste...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Bameri, Salime Almahrog, Khalid Gohary, Ramy H. El-Keyi, Amr Ahmed, Yahia Carleton University Dept. of Systems and Computer Engineering Canada Ericsson Canada Canada

ISBN: (纸本)9781728163277

We propose a low-complexity downlink channel covariance matrix estimation for massive multiple-input multiple-output systems in which the base station (BS) is equipped with a uniform rectangular antenna array (URA). This scheme can be expressed in the form of an affine transformation which depends only on the uplink and downlink carrier frequencies, and the BS array configurations. An upper bound on the estimation error is derived, which shows that the accuracy of the proposed scheme increases with the number of URA antennas and the compactness and differentiability class of the periodic extension of a non-linearly transformed version of the angular power spread. The performance superiority of the proposed scheme over its existing counterparts is confirmed through simulations. © 2023 IEEE.

关键词： angular power spectrum channel reciprocity estimation error Two-dimensional Fourier series

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Techniques for Moment Retrievals and Filtering from Large Volumes of Multimodal Data 10

Techniques for Moment Retrievals and Filtering from Large Vo...

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10th International Conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Syed, Azhar Talha Majumdar, Shikharesh Carleton University Dept. of Systems and Computer Eng Ottawa Canada

ISBN: (纸本)9798350316353

This paper focuses on applications of parallel processing and machine learning techniques for the retrieval and filtering of selected 'moments' from textual and video-based multimodal data. Given a set of textual and video criteria provided by the user, the system extracts moments (sequences of frames) of a fixed duration from a large video file and stores them. An approach that supports the parallelization of this filtration process is presented. Experiments with Apache Spark and TensorFlow-based prototypes developed to demonstrate the efficacy of the proposed approach were conducted for various combinations of system and workload parameters. Insights into system performance including the reduction in processing time that accrues from applying the proposed parallel processing techniques for data filtering are reported in the paper. © 2023 IEEE.

关键词： Large datasets

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THE DIGITAL engineering FACTORY: CONSIDERATIONS, CURRENT STATUS, AND LESSONS LEARNED

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INCOSE International Symposium 2024年第1期34卷 927-943页

作者： Gregory, Joe Salado, Alejandro O'Neal, Sharon Larez, Richardo Reda, C.J. Martell, Niko Martin, Evan Colson, Matthew Masterson, John Armenta, David Dept. of Systems and Industrial Engineering University of Arizona United States Electrical and Computer Engineering University of Arizona United States

In industry, the advancement of digital engineering and the digital thread aims to reduce the impact of knowledge ‘siloes’ by providing a way to integrate data across the entire system lifecycle and across multiple domains. In a typical engineering curriculum, however, courses are still treated as ‘siloes’, and students often do not have the opportunity to experience this industrially relevant approach to engineering. The Digital engineering Factory (DEF) is a digital engineering environment under development at the university of Arizona to support engineering students. The DEF supports students by providing access to multiple engineering tools and is structured using a ‘hub-and-spoke’ approach to consolidate data from these tools. Through this connected architecture, students can transfer data generated in a particular course to tools for use in other courses. Connecting course activities in this way enables students to experience a complete end-to-end system lifecycle. At its ‘hub’, the DEF uses Violet to integrate data from multiple sources, create a digital thread, and generate a graph representation of the dataset. This knowledge graph, written in the Ontological Modeling Language (OML), can be viewed in OML Rosetta and is structured according to the university of Arizona Ontology Stack (UAOS). The use of the UAOS and OML Rosetta allows instructors to leverage semantic web technologies to support teaching activities such as grading. In this paper, the authors review the objectives of the DEF, discuss the status of the project, and highlight current limitations and lessons learned with regards to its deployment. These may be useful to inform similar developments in industrial settings. Copyright © 2024 by Author Name. Permission granted to INCOSE to publish and use.

关键词： Life cycle

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A Reinforcement-Learning Self-Healing Approach for Virtual Network Function Placement 36

A Reinforcement-Learning Self-Healing Approach for Virtual N...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Carleton University Dept. of Systems and Computer Eng. Ottawa Canada Dept. of Software Engineering and It Montréal Canada

ISBN: (纸本)9781665477161

Modern networking paradigms like Service Function Chaining (SFC) allow for services to be broken down to a series of ordered and interconnected Virtualized Network Functions (VNFs) that can be hosted in generic servers in EdgeCloud datacenters. Nonetheless, a critical issue arises, when a hardware or software failure occurs and the VNFs of an SFC need to be repositioned, allowing to autonomously bring the system back to its normal operation, a process called self-healing. In this paper, a distributed methodology is proposed that aims to address this challenge, considering the requirements of all involved actors. Specifically, a Reinforcement Learning (RL) based algorithm is proposed that allows to iteratively optimize and determine an SFC healing solution upon a datacenter failure. As a second stage, a revenue-driven resource allocation mechanism is integrated, to resolve the contention for resources in an already functional datacenter that potentially occurs due to the repositioning. Various simulation scenarios prove the efficiency of our proposed resilient healing mechanism. © 2023 IEEE.

关键词： Resource allocation

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Techniques for Achieving High Performance in Deep Learning Based systems for Selective Filtering of Live Video Streams

Techniques for Achieving High Performance in Deep Learning B...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Syed, Azhar Talha Majumdar, Shikharesh Carleton University Dept. of Systems and Computer Eng. Ottawa Canada

ISBN: (纸本)9798350327595

The research focuses on applications of parallel and distributed processing techniques and deep learning for filtering frames from live video streaming data based on a set of criteria specified by the user. With various video streaming sources around the world, storing the entire video streams and searching the stored data is computationally expensive. A video stream filtering system keeps the frames of interest and discards the rest for fast search and retrieval as well as reduction of storage space. This research concerns the devising of filtering techniques for streaming video data. Parallel and distributed processing techniques including frameworks like Apache Kafka integrated with TensorFlow are used to devise the proposed scalable real-time filtering prototypes. Initial research results that include experiments with live video streams from webcams were conducted on Amazon EC2 cloud to gain insights into system performance including the reduction in processing time that accrues from applying the proposed techniques. © 2023 IEEE.

关键词： Deep learning-based filtering Performance of video filtering on clouds Scalable systems Video streaming

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