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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Hassan, Payam Pourzadeh Marsland, Ian Smith, Roland Kerr, Ron Iun, Edwin Lambadaris, Ioannis Carleton University Department of Systems and Computer Engineering Ottawa Canada Ericsson Canada Ottawa Canada

ISBN: (纸本)9798350351255

This paper presents an advanced implementation of the single-snapshot 2D-MUSIC algorithm, enhanced by cross-linear antenna arrays, to improve uplink indoor positioning accuracy using OFDM-based 5G networks. The proposed method adeptly handles multipath interference and non-line-of-sight (NLoS) conditions, demonstrating significant advancements over traditional techniques. Our method not only accurately determines the position of user equipment in two dimensions but also extends to 3D positioning. A novel aspect of our research includes addressing the challenge of unknown time of departure, which often complicates the time of flight calculations necessary for precise localization. By using two strategically placed cross-linear antenna arrays, our system compensates for this uncertainty, providing reliable and precise location estimates even without perfect transmitter-receiver synchronization. Simulation results validate the robustness of our approach, showing exceptional localization accuracy and promising potential for complex IoT applications within industrial settings. Provided the separation between multipath components in either range or angle is sufficiently large, the algorithm is capable of detecting the transmitter with sub-centimeter accuracy. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Charging Station Planning for Electric Ride-Hailing Taxi Services 30

Charging Station Planning for Electric Ride-Hailing Taxi Ser...

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30th IEEE International Conference on Telecommunications, ICT 2024

作者： Forster, Phillippe Mathur, Anuj Ghazzai, Hakim Kadri, Abdullah Systems and Computer Engineering Carleton University Ottawa Canada CEMSE Division Thuwal Saudi Arabia

ISBN: (纸本)9798350356694

The development of the transportation sector towards green infrastructures plays a critical role towards mitigating climate change. Specifically, the trend of transitioning from Gas to Electric Vehicles (EVs) requires city planning for the optimal placements of EV Charging Stations (CS). With a future vision of converting all vehicles to electric, this paper examines the impact of CS locations placement on normal taxi operations. Optimized placement of stations have a significant impact on charger utilization and minimize charging queues leading to lower user wait times. A simulator was developed for modeling the taxi service in the Manhattan Borough of New York City. Factors such as taxi regions, DC stations, as well as real user data were used to closely mimic the system. The Particle Swarm Optimizer (PSO) was developed to optimize the CS locations showing the consequences of optimized CS placement on the taxi infrastructure in Manhattan. The results showed that the city of NYC would need approximately 80 DC fast CS, or 480 level 2 chargers placed mostly within midtown Manhattan. © 2024 IEEE.

关键词： Climate change

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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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Explainable Long-Term Forecasting of Air Pollution

Explainable Long-Term Forecasting of Air Pollution

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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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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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Patch-based Learning for Radar-based Fall Event Detection using Gramian Angular Field 1

Patch-based Learning for Radar-based Fall Event Detection us...

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1st International Conference on Computing, Internet of Things and Microwave systems, ICCIMS 2024

作者： Dey, Ankita Rajan, Sreeraman Xiao, Gaozhi Lu, Jianping Systems and Computer Engineering Carleton University Ottawa Canada Quantum and Nanotechnologies Research Centre Ottawa Canada

ISBN: (纸本)9798350351736

This paper proposes a Gramian Angular Field (GAF) for detection of fall events using radars. These GAF representations are directly obtained using range-time information and utilize temporal dependency among time samples for enhanced fall event detection. This work compares the performance of a patch-based learning model with attention (improved vision transformer (I-ViT)) and two patch-based learning models without attention (Multi-Layer Perceptron-Mixer (M-mixer) and Convolutional-Mixer (C-Mixer)) for fall detection with GAF plots as input with those obtained with range-time plots as input. Models are evaluated using a publicly available dataset with location-wise testing strategy. Patch-based learning model using GAF plots as input perform significantly better as compared to patch-based learning models using range-time plots at two out of three testing locations. © 2024 IEEE.

关键词： Mixers (machinery)

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Few-shot transfer learning for wearable IMU-based human activity recognition

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Neural Computing and Applications 2024年第18期36卷 10811-10823页

作者： Ganesha, H.S. Gupta, Rinki Gupta, Sindhu Hak Rajan, Sreeraman The Department of Electronics and Communication Engineering Amity University UP Noida201313 India The Amity Centre for Artificial Intelligence Amity University UP Noida201313 India The Department of Systems and Computer Engineering Carleton University Ottawa Canada

Deep learning has proven to be highly effective for human activity recognition (HAR) when large amount of labelled data is available for the target task. However, training a deep learning model to generalize well on a new task with just-few observations of labelled data is an active area of research. In this paper, a novel few-shot transfer learning (FSTL) approach is proposed for classification of human activities using just few instances (shots) of the data obtained from a wearable system assembled to collect inertial sensor data for different human activities, performed by two users. First, a deep learning model is trained on a large publicly available HAR dataset. The model parameters of such a model are then fine-tuned using the Reptile algorithm to determine the optimal initial parameter set using which, the model will classify activities with just few-shots of data from the target task. The proposed FSTL approach yields an average classification accuracy of 74.86 ± 0.71% and 79.20 ± 1.05% for 3-way, 5-shot classification of new activities performed by a single user and same set of activities performed by a new user, respectively. When the pre-trained weights are used as the initial weights in the Reptile algorithm, the generalization ability of the model improves by about 10% for 3-way, 5-shot classification as compared to using few-shot learning without parameter transfer. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Inertial navigation systems

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Importance of Realistic Considerations of Time Synchronization for TAS in TSN Networks

Importance of Realistic Considerations of Time Synchronizati...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Ghosh, Aviroop Kunz, Thomas Yousefi, Saleh Carleton University Department of Systems and Computer Engineering Ottawa Canada Urmia University Department of Computer Engineering Urmia Iran

ISBN: (纸本)9798350335590

Industry 4.0 applications require communication networks with deterministic and bounded latency. Ethernet, augmented with the IEEE Time-Sensitive Networking (TSN) standards, can meet these requirements. One of the TSN standards, Time Aware Shaper (TAS) is ideal for periodic time-sensitive traffic in wired TSN networks. However, it relies on network-wide synchronization to a common reference time. Existing solutions for developing TAS schedules assume perfect synchronization or consider a worst-case error. This study highlights the need for using network-derived time synchronization parameters and errors to optimize TAS schedules. It raises the importance of accurate time synchronization information and proposes a novel methodology for efficient band-width utilization while ensuring deadline requirements are met for time-sensitive traffic in realistic networks. The effectiveness of the proposed framework is validated by simulating a TSN network in OMNeT++ and comparing the devised TAS schedules with schedules which use existing assumptions. © 2023 IEEE.

关键词： Ethernet

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On Enhancing the Performance of Inter-Cloud Data Transfers 10

On Enhancing the Performance of Inter-Cloud Data Transfers

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

作者： Contreras, Juan Pablo Majumdar, Shikharesh El-Haraki, Ali Carleton University Systems and Computer Engineering Ottawa Canada Telus Ottawa Canada

ISBN: (纸本)9798350316353

The research presented in this paper concerns the transfer of data in the form of requests from a compute cloud to a storage cloud using a buffer at the compute system. Different buffering techniques from literature have been shown to improve performance either when the system is subjected to a high load or a low load, but not both. This paper discusses the performance of a threshold based and a machine learning based adaptive data transfer methods that dynamically change the buffering technique at runtime to offer an improvement in performance at all loads. The performance results of a proof-of-concept prototype are discussed for both the threshold based and machine learning based adaptive methods. © 2023 IEEE.

关键词： Data transfer

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Event-Triggered Fixed-Time Consensus of Second-Order Nonlinear Multi-Agent systems with Delay and Switching Topologies

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Journal of Shanghai Jiaotong university(Science) 2024年第4期29卷 625-639页

作者： XING Youjing GAO Jinfeng LIU Xiaoping WU Ping School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China Department of Systems and Computer Engineering Carleton UniversityOttawaK1S 5B6Canada

To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is ***,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system ***,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each *** is available to save communication resources,and the Zeno behaviors are ***,the delay and switching topologies of the system are also ***,the system stabilization is analyzed by Lyapunov stability ***,simulation results demonstrate the validity of the presented method.

关键词： event-triggered mechanism fixed-time consensus multi-agent systems switching topologies

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