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

作者： Lovett, Samuel Paquette, Tyler Deboon, Brayden Rajan, Sreeraman Rossa, Carlos Carleton University Department of Systems and Computer Engineering OttawaON Canada Hibou Systems Inc. SpringwaterON Canada

ISBN: (纸本)9798350337020

Two-dimensional (2D) simultaneous localization and mapping (SLAM) using a LIDAR is a method used to track the position and orientation of a moving platform. 2D-SLAM assumes that the platform translates in a 2D plane and can only rotate about an axis perpendicular to that plane. However, the assumption of no out-of-plane (OOP) motion does not hold true for platforms experiencing motion in six degrees-of-freedom (6-DOF), such as wearable technologies that have no 3D LIDAR. This paper proposes a new algorithm, called the Level Plane for SLAM (LPS) for removing OOP motion from 2D-LIDAR scans generated on platforms experiencing 6-DOF without requiring scan-matching in 3D. Like other existing methods, an IMU is combined with a 2D-LIDAR to determine the platform's orientation, capture OOP motion, and generate a scan in 3D. Unlike other methods, OOP motion is removed by projecting scans onto a globally stabilized coordinate frame in 2D where both scan matching and map alignment take place. The proposed algorithm is validated over a series of experiments with different levels of induced and observed OOP motion. Experimental results show that LPS is able to handle more OOP motion than other algorithms and run in real-time. © 2023 IEEE.

关键词： Hector SLAM LIDAR odometry Lidar-IMU fusion scan matching signal filtering SLAM

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Decision Tree Machine Learning to Determine Direction of Steering Force for Hospital Bed Push Handle System

Decision Tree Machine Learning to Determine Direction of Ste...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Chimehi, Bahareh Wallace, Bruce Carleton University SAM3 Innovation Hub Department of Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute SAM3 Innovation Hub Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665493840

This work demonstrates the use of Decision Tree and Random Forest machine learning to determine the direction of desired movement from the forces applied to two push-handles on a novel patient transfer system the Able Innovations ALTA™ patient transfer system provides a replacement transfer method for residents in care that cannot transfer on their own that have limited mobility. This new system is significantly heavier that a hospital gurney because of the weight of the mechatronics and healthcare professionals will need power assist to transport and position the system. In this work, two loadcell sensor-based prototypes have been used to measure the input forces and direction applied by a user to two handles. These two prototypes have been placed on a hospital bed to simulate the two handles on the right and left provided to hospital staff to push the system. Machine learning is used to analyze sensor measurements from each handle to predict the movement intent. The results are presented for test pushes in 6 directions that include forward and reverse in each of straight, left, and right turns. Two models of Machine Learning (decision tree classifier and random forest classifier) have been used for 8 and 14 features and are shown to be able to predict the direction of push with high accuracy. The accuracy for 8 features using decision tree and random forest has been measured 93.5% and 97.0% respectively. © 2023 IEEE.

关键词： Decision trees

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Intelligent and Autonomous Edge Slicing for IoT systems

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IEEE Internet of Things Journal 2025年第12期12卷 21210-21224页

作者： Hussein, Dana Haj Ibnkahla, Mohamed Carleton University Department of Systems and Computer Engineering OttawaON Canada

Edge intelligence is rapidly emerging as a pivotal platform for supporting future IoT networks. The integration of Artificial Intelligence and Machine Learning (AI/ML) with edge computing furnishes a new era in which edge systems can learn environment dynamics and optimize resource autoscaling policies. However, the heterogeneity of IoT networks, characterized by diverse applications and requirements, necessitates edge systems with advanced intelligence to tailor resource autoscaling policies to specific environments. Despite ongoing research in edge intelligence, most studies have focused on a single environment or service type, thereby limiting their applicability to real-world scenarios with varied IoT services. To address this limitation, we propose the Intelligent and Autonomous Edge Slicing (IAES) system, a novel approach designed to recognize diverse IoT environments and implement per-slice resource allocation policies. IAES leverages Deep Reinforcement Learning (DRL), namely, Dueling Double Deep Q-Networks (D3QN), to optimize resource autoscaling across distinct IoT environments, such as smart cities, eHealth, and smart factories. Additionally, IAES incorporates an intelligent environment classification component that utilizes joint traffic prediction and classification models. Several AI algorithms such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Multi-Layer Perceptron Networks (MLP), are evaluated for their efficacy in predicting IoT environments. Simulation experiments demonstrate that the IAES system achieves a 50-60% reduction in system costs compared to both rule-based commercial autoscaling employed in Kubernetes systems and an intelligent prediction-based autoscaling algorithm. © 2014 IEEE.

关键词： Reinforcement learning

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Privacy-Preserving Intelligent Intent-Based Network Slicing for IoT systems

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IEEE Internet of Things Journal 2025年

作者： Hussein, Dana Haj Ibnkahla, Mohamed Carleton University Department of Systems and Computer Engineering OttawaON Canada

The proliferation of Internet of Things (IoT) services across diverse sectors such as healthcare, industrial IoT, and smart cities has introduced unprecedented complexity in network Management and Orchestration (MO). Contemporary MO systems are challenged to support the coexistence of IoT services with varying Quality of Service (QoS) requirements while ensuring end-to-end (E2E) performance across heterogeneous technologies and multi-administrator domain networks. To address these requirements, emerging technologies such as Intelligent Intent-Based Network Slicing (I-IBNS) systems are increasingly employed, leveraging advancements in network automation, Artificial Intelligence (AI), and Network Slicing (NS). This paper focuses on three critical challenges in developing I-IBNS systems for IoT: E2E resource allocation, privacy preservation in multi-administrator systems, and E2E QoS assurance across heterogeneous networks and time-varying traffic. We propose a privacy-preserving I-IBNS framework, named Harmony Slice Master (H-SliceMaster), which integrates a knowledge management framework for privacy-aware data aggregation, an intent propagation mechanism for translating high-level intents into network configurations, and a novel Promise and Price Network Operation (PPNO) principle for optimizing E2E resource allocation while maintaining intra-domain privacy. A proof-of-concept design of the H-SliceMaster is presented, utilizing Deep Q-Networks (DQN) for intra-domain resource allocation and a centralized algorithm for optimizing E2E network slice deployment. Simulation results demonstrate that the H-SliceMaster efficiently satisfies various IoT applications and delay requirements. Moreover, the proposed system achieved an 20% and 50% reduction in system costs compared to a Branching Dueling Q-Network (BDQ) and greedy algorithm, respectively. © 2014 IEEE.

关键词： Resource allocation

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Robust Data Exchange Across Wind Turbines 5

Robust Data Exchange Across Wind Turbines

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5th International Conference on computer Communication and the Internet, ICCCI 2023

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)9798350326956

Wind farms play an important role in the smart grid initiative. Most wind turbines are deployed in open areas and their wireless communication layer needs to be robust. In this paper, the concept of information-theoretic secrecy is introduced to establish some benchmarks of robust communication. Compatible with the common topology of wind turbine deployment, the secrecy analysis is conducted in the framework of cooperative communication with a generic relay configuration. The analysis takes several issues into account, such as fading, line-of-sight, and multi-scattering. We derive several benchmark metrics for this system. These metrics are explicitly expressed in the closed-form. The availability of these solutions helps directly gain the deep insights, while reducing the computational load of tedious simulations. © 2023 IEEE.

关键词： Wind turbines

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Emission Mitigation Dispatch in Wind Power Generation with Expedited Machine Learning 10

Emission Mitigation Dispatch in Wind Power Generation with E...

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10th International Conference on Power and Energy systems engineering, CPESE 2023

ISBN: (纸本)9798350327625

In this paper: a machine learning (ML) module is presented for emission mitigation dispatch (EMD) in wind power (WP) generation. It is developed for the mobile edge computing (MEC) platforms. Therefore, the module is based on a lightweight ML scheme: radial basis neural network (RBN). It has been well known that RBN is compact compared with a more popular ML scheme: feedforward neural network. However, this is the first study to combine EMD with RBN, strongly motivated by the MEC implementations. MEC is becoming an important platform in the evolving smart grid program. As a trial, a case study with 36 generators and 50 wind turbines is presented. Two standard ML stages, training and testing, are conducted in the simulation. It is shown that, by using the trained RBN, the time of solving EMD is significantly reduced. It is expected to be a desirable model for MEC. © 2023 IEEE.

关键词： Wind power

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MPC Based Linear Equivalence with Control Barrier Functions for VTOL-UAVs

MPC Based Linear Equivalence with Control Barrier Functions ...

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2024 American Control Conference, ACC 2024

作者： Ali, Ali Mohamed Hashim, Hashim A. Shen, Chao Carleton University Department of Mechanical and Aerospace Engineering OttawaONK1S-5B6 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S-SB6 Canada

ISBN: (纸本)9798350382655

In this work, we propose a cascaded scheme of linear Model prediction Control (MPC) based on Control Barrier Functions (CBF) with Dynamic Feedback Linearization (DFL) for Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). CBF is a tool that allows enforcement of forward invariance of a set using Lyapunov-like functions to ensure safety. The First control synthesis that employed CBF was based on Quadratic Program (QP) that modifies the existing controller to satisfy the safety requirements. However, the CBF-QP-based controllers leading to longer detours and undesirable transient performance. Recent contributions utilize the framework of MPC benefiting from the prediction capabilities and constraints imposed on the state and control inputs. Due to the intrinsic nonlinearities of the dynamics of robotics systems, all the existing MPC-CBF solutions rely on nonlinear MPC formulations or operate on less accurate linear models. In contrast, our novel solution unlocks the benefits of linear MPC-CBF while considering the full underactuated dynamics without any linear approximations. The cascaded scheme converts the problem of safe VTOL-UAV navigation to a Quadratic Constraint Quadratic Programming (QCQP) problem solved efficiently by off-the-shelf solvers. The closed-loop stability and recursive feasibility is proved along with numerical simulations showing the effective and robust solutions. © 2024 AACC.

关键词： Quadratic programming

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Performance Measurement of IoT Traffic Through SRv6 Network Programming

Performance Measurement of IoT Traffic Through SRv6 Network ...

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2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

作者： Kuri, Sajib Kumar Eldosouky, Abdelrahman Ibnkahla, Mohamed Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (数字)9781665426718

ISBN: (纸本)9781665426718

Segment Routing over IPv6 (SRv6) is a modern networking technology for source routing that is envisioned to achieve high reliability, availability, and scalability of current IoT networks. Based on the IoT use-case scenario, traffic requires processing at both the edge and the cloud, and, thus it must be forwarded through the service provider's core networks. Using SRv6, IoT traffic can be forwarded over IPv6 without additional protocol overhead. However, performance measures are still needed to evaluate SRv6 behaviors or functions. To this end, the goal of this paper is to develop a realistic IoT traffic profile and use it to measure the performance of SRv6 behaviors. In particular, an SRv6 programming model is proposed that consists of three modules: Orchestrator, System Under Test (SUT), and Traffic Generator (TG). The proposed model considers IoT traffic forwarding through core and ensures reliability and quality of service (QoS). Then, different benchmarking methodologies, i.e., no-drop rate (NDR), partial drop rate (PDR), and maximum receive rate (MRR) are implemented to measure the performance of SRv6 policy headend and endpoint behaviors. Finally, a novel finder algorithm for MRR is proposed to automate the MRR benchmarking in the Linux kernel deployment. Implementation results are used to provide insights on the use of SRv6 behaviors to forward different IoT applications traffic based on the functional service requirements. © 2022 IEEE.

关键词： Benchmarking

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A Reliable Federated Learning Server Rotation Algorithm in IoV

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IEEE Internet of Things Journal 2025年第11期12卷 15501-15513页

作者： Cai, Xuelian Yang, Junyi Chang, Tianyu Fu, Yuchuan Richard Yu, F. Cheng, Nan Li, Changle Hui, Yilong State Key Laboratory of Integrated Services Networks Research Institute of Smart Transportation Xidian University Shaanxi Xi’an710071 China Carleton University Department of Systems and Computer Engineering ON OttawaK1S 5B6 Canada

Federated learning (FL) enables the collaborative training of models by users distributed across various locations, transforming traditional data sharing into model sharing. This paradigm holds the promise of facilitating the development of safe, reliable, and accurate driving models within Internet of Vehicles (IoV), with its performance contingent upon the stability of the training process. However, traditional FL relies on a central server for aggregation, which is susceptible to malicious attacks. Moreover, limited communication resources prevent the inclusion of all users in the training process. To resolve issues related to reliability and resource utilization, this article proposes a reliable rotating server FL (RSFL) algorithm to enhance the security and efficiency of FL. Specifically, we first consider the vehicular topology and participation in FL during their transition, and introduce a server rotation algorithm that incorporates a weighted sum of multiple factors, including model training activity, vehicle credibility, speed stability, and distance to augment system security. Additionally, addressing the limitation of server channel resources that can impede FL efficiency, this article proposes a method to select high-quality users for channel resource allocation by comprehensively considering participation latency, contribution, energy, and channel state during the FL process. This optimizes resource usage at the FL server side and constructs an efficiency-maximization problem for FL to improve the convergence rate. Simulation results confirm that the proposed RSFL algorithm can significantly enhance the security and system efficiency of FL. © 2014 IEEE.

关键词： Federated learning

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Using CNN-LSTM Model for Weather Forecasting

Using CNN-LSTM Model for Weather Forecasting

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Fan, Michael Imran, Omar Singh, Arka Ajila, Samuel A. Carleton University Ottawa Department of Systems and Computer Engineering Ontario Canada

ISBN: (纸本)9781665480451

An efficient and cost-effective weather forecasting approach can be used to protect humans and benefit economic growth as a result of secure forest, agriculture, and tourism industry sectors. This paper is based on the IEEE Big Data IARAI's Weather4cast 2021 challenge dataset. The goal of this paper is to consider computational cost of predicting future weather forecast by using a CNN-LSTM based neural network model. The network utilizes an encoder-decoder architecture to predict future weather images. All the four variables are predicted using the same model providing generalization in the solution. The model is trained and tested on the Nile Region (R1) data and a significant improvement is observed for the loss against cloud mask and rainfall feature prediction in comparison with CNNGRU deep learning model. Two models - shallow and deep models are compared and the results in terms of MSE values for the shallow model (which is computationally cost effective) is not too far from the deep model. © 2022 IEEE.

关键词： Neural network models

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