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IEEE Networking Letters 2025年第1期7卷 41-45页

作者： Ghosh, Aviroop Yousefi, Saleh Kunz, Thomas Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Urmia University Department of Electrical and Computer Engineering Urmia57561-51818 Iran

The IEEE 802.1Qbv (80.21Qbv) standard is designed for traffic requiring deterministic and bounded latencies through strict periodic time synchronization, as specified by IEEE 802.1AS standard. However, internal clock drift in devices causes timing misalignment, introducing further challenges to 802.1Qbv scheduling. Existing solutions, using either complex optimization approaches or non-trivial scheduling heuristics, address this by scheduling frame transmissions only once they are guaranteed to have been fully received, even in the presence of clock drifts. However, this approach introduces additional delays that can impact deadline requirements. This letter analytically derives tight end-to-end latency bounds, allowing us to determine if stream deadlines for a given network will be violated without the need to solve for any scheduling algorithms. It also proposes an approach that results in tighter bounds based on information collected from the synchronization process. The analytical results are compared with simulation results, confirming their validity. © 2019 IEEE.

关键词： Synchronization Clocks Delays Standards Schedules Logic gates Scheduling algorithms Payloads Jitter Data models

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Have the Factors Affecting Software New Product Development (S-NPD) Changed in the Age of Mobile Apps and Agile Methods?

Have the Factors Affecting Software New Product Development ...

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2023 Portland International Conference on Management of engineering and Technology, PICMET 2023

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

ISBN: (纸本)9781890843434

It has been long recognized that Time-to-Market, Cost-of-Delay, and Uncertainty are the three major factors that can impact New Product Development (NPD) process. Software New Product Development (S-NPD) process is even more notorious with delay and cost overrun. The goal of this position paper is to examine the three factors: Time-to-Market, Cost-of-Delay and Uncertainty through 'observation' vis-à-vis software Apps development process and the use of Agile methods in order to see if something has changed over the years. In addition, a controlled group interview is conducted to support the observation method. The questions this paper seeks to answer are: Are we getting to market on time? Are we delivering software product on budget? Do we have reduced uncertainty when it comes to Apps development and the use of Agile method? The results obtained show that the use of Agile approach in developing Apps can take the product to the market quicker compared to using the rigid linear process, in our case the waterfall V-model. Also, the 'product introduction delay' as part of product performance was measured on the assumption that there is no budget or cost overrun. In this case, the Agile method performs better than the linear approach. Lastly, in terms of technological uncertainty, the Agile development approach does not necessarily bring the expected commercial success, and in fact, for the situation where the development of Apps is involved, rapid development could result in failure if proper plan is not in place for the evolution of the product. © 2023 PICMET (Portland International Center for Management of engineering and Technology(.

关键词： Budget control

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Domain Adaptation applied to microRNA Target Prediction 23

Domain Adaptation applied to microRNA Target Prediction

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Ajila, Victoria Green, James R. Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9798350393118

MicroRNA target prediction attempts to determine which genes are regulated by which miRNA. When developing such predictors, we are often faced with data scarcity in the species of interest. In this paper we explore the use of domain adaptation as a means to intelligently increase the available training data by effective pooling of data from multiple training species. Through a variety of experiments, we discovered that, on average, domain adaptation improves the performance of miRNA target predictors. Significant performance gains can be found in inter-kingdom experiments where an animal training set is augmented with plant data to perform target prediction on a plant species, or vice versa. Additionally, the inclusion of position data in cases where the target species is animal significantly improved performance overall. Taken together, these advancements can lead to improved species-specific miRNA target prediction. © 2023 IEEE.

关键词： Animals

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Estimating the Performance of VNE Algorithms with a Novel Loss Network Model 36

Estimating the Performance of VNE Algorithms with a Novel Lo...

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

作者： Lu, Qiao Huang, Changcheng Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665477161

A large number of virtual network embedding (VNE) algorithms have been proposed in the literature. Their performances have always been evaluated through simulations due to the formidable difficulty of developing analytical models for the performance of VNE algorithms. In this paper, we propose a novel loss network model with Dynamic Routing And Random Topology (DRART) that allows us to estimate the blocking probability of mapping a virtual link (VL). Furthermore, by integrating our new model with other existing models, we can estimate the blocking probability of VNE algorithms quite accurately. Our analytical model can be used as a benchmark for comparing different VNE algorithms as well as a tool to evaluate an operating virtual network service where substrate nodes and links can fail or be put into maintenance randomly. © 2023 IEEE.

关键词： Blocking probability

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Learning a Policy for Pursuit-Evasion Games Using Spiking Neural Networks and the STDP Algorithm

Learning a Policy for Pursuit-Evasion Games Using Spiking Ne...

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

作者： Ramezanlou, Mohammad T. Schwartz, Howard Lambadaris, Ioannis Barbeau, Michel Naqvi, Syed Hassan Raza Carleton University Department of Systems and Computer Engineering Ottawa Canada School of Computer Science Carleton University Ottawa Canada

ISBN: (纸本)9798350337020

Pursuit-Evasion (PE) games are regarded as a major platform for game theory. In this kind of game, an agent called an evader tries to escape from another agent called a pursuer. Active Target Defense (ATD) is a derivative of PE games, attracting attention recently. In an ATD game, the evader, often called an invader, strives to capture a moving target. The pursuer, called a defender, tries to intercept the invader. This paper implements the Spike-Timing-Dependent Plasticity (STDP) algorithm to train two Spiking Neural Networks (SNNs) to find a suitable solution for the ATD problem in decentralized situations. One of the SNNs is used to control the invader, while the other controls the defender. The performance is compared with the analytical solution for the pedestrian model. The results showed that an SNN can learn the optimal capture point only using relative velocities and line of sight. © 2023 IEEE.

关键词： Active Target Defense Spike-Timing-Dependent Plasticity Spiking Neural Network

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Unstructured Transportation Safety Board Findings Categorization Using the Knowledge Graph Pipeline

Unstructured Transportation Safety Board Findings Categoriza...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Panday, Ritesh Lung, Chung-Horng Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350324457

In this study, the Transportation Safety Board's (TSB) Findings data was analyzed to assist Transport Canada Civil Aviation (TCCA) in better informing safety policy decision-making. As the TSB Findings data was unstructured, various methods to categorize and analyze unstructured data were explored in the existing literature. It was found that Knowledge Graphs (KGs), in combination with Deep Learning and Natural Language Processing (NLP) models, such as Neuralcoref and REBEL, were versatile and adaptable to different data needs, which could provide insights into the analysis of the TSB data. This paper first emulated and validated the KG pipeline using the BBC News dataset and then applied the KG pipeline technique to the unstructured TSB Findings Reports data consisting of 4,121 rows, each containing text for an incident or accident. The results showed that the model detected an average of 1.03 entities per row of the data and a total of 5,484 relationships or 1.33 relationships per row. Further, the top-four relationships in the graph database structure obtained from Neo4j accounted for 50% of all relations, though not all relations were found to be valuable. However, a few less-frequent relations were also found to be valuable due to their ability to capture critical components of aviation safety. The results of this data pipeline can be used for further analysis and categorization of TSB's Findings data to improve aviation safety. © 2023 IEEE.

关键词： Data Pipeline Deep Learning Graph Database Knowledge Graph Machine Learning Natural Language Processing

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Adaptive Computation Offloading for Heterogeneous IoT Applications: An Experimental Study 20

Adaptive Computation Offloading for Heterogeneous IoT Applic...

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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Aidaros, Othman Al Bouida, Zied Ibnkahla, Mohamed Carleton University Systems and Computer Engineering Department OttawaON Canada

ISBN: (纸本)9798350332568

Internet of Things (IoT) applications are heterogeneous in terms of the deployed hardware, developed protocols, and requirements of each of these applications. The decision whether to process such applications at the sensor nodes or at a higher layer (i.e., Edge, Fog, or Cloud) heavily depends on those heterogeneity features. In this paper, we propose a number of adaptive schemes to address the local processing vs. offloading of IoT applications based on their energy consumption and execution time. To this end, we deploy six IoT applications on real sensor nodes, measure their energy consumption and execution time, and then decide where to process the application based on the measurements. We study the performance of the proposed schemes in terms of energy efficiency and execution time. We also compare their performances to selected non-adaptive schemes. © 2023 IEEE.

关键词： Energy efficiency

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A Two-Stage Deep Learning Framework for Enhanced Waste Detection and Classification 22

A Two-Stage Deep Learning Framework for Enhanced Waste Detec...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Pang, Hongbo Huang, Changcheng Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350345346

With the rapid modernization progress over the past decades, waste classification has become increasingly important as cities worldwide seek to implement more sustainable waste management practices. Traditional manual sorting methods are labor-intensive, prone to inaccuracies, and hard to scale, driving the need for automated, efficient solutions. Although deep learning techniques, recognized for their ability to process complex hierarchical data, have emerged as potential aids, their successful implementation is often hampered by the challenging task of gathering diverse, large-scale, high-quality waste image datasets, leading to possible overfitting and model bias. This study proposes an innovative, two-stage waste detection framework that first identifies the bounding box of waste items, then classifies them into one of six primary categories, effectively addressing the inherent issues with previous methodologies by optimally utilizing available data and reducing overfitting and bias. Trained and evaluated on the comprehensive TACO and WaRP waste datasets, our model has demonstrated superior performance relative to existing methods, underscoring its promise as a scalable, accurate, and efficient solution for waste classification, thus offering exciting prospects for further research and practical applications in sustainable waste management. © 2023 IEEE.

关键词： Deep learning garbage classification waste management

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DRL-based Trajectory Planning and Sensor Task Scheduling for Edge Robotics 10

DRL-based Trajectory Planning and Sensor Task Scheduling for...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Bouhoula, Sirine Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Department of Software and IT Engineering Montréal Canada Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350373011

Mobile Edge Computing (MEC) and Edge Robotics have recently emerged as transformative technologies, revolutionizing industries by enabling real-time processing, decision-making, and automation at the network edge. However, the dynamicity induced by the system's conditions and specifically the mobility poses a challenge for optimally deciding where to execute a given computational task. As a response, we develop an intelligent algorithm for dynamic sensor task offloading tailored to the unique requirements of MEC-enabled robotic environments. Specifically, we first introduce the environmental dynamics including a sensor task's end-to-end delay and the robots' mobility and energy consumption and provide mathematical formulations to model these dynamics. Then, we mathematically formulate the optimization problem and its MDP counterpart and we propose a Deep Reinforcement Learning (DRL)-based computational offloading strategy to jointly optimize Quality of Service (QoS) and energy consumption through robot trajectory planning and fine-grained task allocation. Through hand-picked representative simulation scenarios, we demonstrate the superiority of our proposed mechanism in enhancing the overall system performance, specifically in optimizing task execution, reducing energy consumption, and mitigating transmission delays, compared to various baseline approaches. © 2024 IEEE.

关键词： Computation offloading

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Machine Learning for Polar Codes in Small IoT Devices 7

Machine Learning for Polar Codes in Small IoT Devices

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Liu, Xian Huang, Changcheng University of Arkansas at Little Rock Department of Systems Engineering Little Rock United States Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350352009

Error-correction in the communication module of IoT systems is a mission-critical task. The Polar code is one of the modern 5G wireless technologies. Most conventional decoding methods are iterative. It is interesting to investigate the feasibility of AI methodology, such as machine learning, for small Polar codes. In this paper, we design a small artificial neural network for this purpose. It is shown that this neural network can successfully decode four small Polar codes: both non-systematic and systematic, with codeword length 8 and 16, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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