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Task Offloading and Resource Management for IIoT with Satellite-Terrestrial Integrated Computing Power Network Based on D3QN

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IEEE Internet of Things Journal 2025年第12期12卷 21770-21783页

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex communication environments, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov Decision Process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2025 IEEE.

关键词： Energy utilization

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Developing an Instrumentation Package to Measure Noise and Vibration in Neonatal Patient Transport

Developing an Instrumentation Package to Measure Noise and V...

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

作者： Gibb, Keely Yovchev, Nikita Aubertin, Cheryl Greenwood, Kim Redpath, Stephanie Ibey, Andrew A. M. Chan, Adrian D. C. Green, James R. Langlois, Robert G. Carleton University Mechanical and Aerospace Engineering Ottawa Canada Carleton University Systems and Computer Engineering Ottawa Canada Children's Hospital of Eastern Ontario Ottawa Canada Carleton University Children's Hospital of Eastern OntarioSystems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665493840

Inter-facility transport is often necessary for patients requiring specialized medical care. In the case of neonatal patients, specialized transport systems are used to ensure continuous care during transport. Concerns relating to physical stressors to which the patients are exposed during transport has motivated our ongoing study on evaluating and mitigating vibration and acoustic noise in these systems. Regardless of the various testing configurations, whether they be full-scale vehicle tests or laboratory shaker tests, reliable and consistent data collection practices are desired. An instrumentation package, designed to measure noise and vibration of the transport system, is currently in development to ultimately improve the safety of patients during transport. This system is intended to promote standard data collection practices and ultimately the aggregation of findings from independent studies into a single database. Requirements for the system have been defined, and early stages of sensor selection and prototyping are underway. The current configuration of sensors can successfully collect acceleration, angular rate, and sound level data. Additional sensors can be integrated into the system, to allow for multiple locations of interest to be studied simultaneously. Future work will improve the efficiency of the data collection script, the automation of data processing, and the physical implementation of the system. © 2023 IEEE.

关键词： Acoustic noise

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A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

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Digital Communications and Networks 2023年第5期9卷 1113-1122页

作者： Tejasvi Alladi Varun Kohli Vinay Chamola F.Richard Yu Department of Computer Science and Information Systems BITS-PilaniPilani Campus333031India Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Systems and Computer Engineering Carleton UniversityOttawaONK1S 5B6Canada

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of

关键词： Vehicular Ad-hoc Networks(VANETs) Intelligent Transportation systems(ITS) Artificial Intelligence(AI) Deep Learning Internet of Things(IoT)

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Data Augmentation using Reverb and Noise in Deep Learning Implementation of Cough Classification

Data Augmentation using Reverb and Noise in Deep Learning Im...

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

作者： Huq, Saiful Xi, Pengcheng Goubran, Rafik Valdes, Julio J. Knoefel, Frank Green, James R Carleton University Systems and Computer Engineering Ottawa Canada Carleton University Natinoal Research Council Canada Ottawa Canada Carleton University Bruyère Research Institute Systems and Computer Engineering Ottawa Canada National Research Council Canada Digital Technologies Ottawa Canada Bruyère Research Institute Élisabeth Bruyère Hospital Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9781665493840

The interest in automated analysis and classification of cough sounds has increased in recent years, partly due to the worldwide COVID19 pandemic. To train such classification models, a large dataset of cough sounds is needed, however, it remains challenging to find such datasets of cough sounds that have expert-labelled diagnoses of cough types. Data augmentation techniques have been used to train machine learning models given such limited data. Furthermore, augmentation ensures that trained models are invariant to natural transformations of the data, measured from real environments/surroundings. This paper presents a method for classifying wet and dry coughs using a ResNet18 convolutional neural network model. Several forms of spectral data augmentation are investigated including many traditional audio data augmentation methods. A novel form of audio data augmentation is leveraged, where coughs are augmented with varying levels of reverberation and Gaussian noise, during model training. The study found that using a combination of reverb and noise augmentation provided greater improvement than either form of augmentation alone, or traditional augmentations as well, leading to an accuracy of 95%. Use of such a model that has been trained on both reverb- and noise-augmented data is recommended when classifying audio recordings, such as cough sounds, from natural environments outside of laboratory conditions. © 2023 IEEE.

关键词： Classification (of information)

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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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DEVS AS A METHOD TO MODEL AND SIMULATE COMBINATORIAL DOUBLE AUCTIONS FOR E-PROCUREMENT

DEVS AS A METHOD TO MODEL AND SIMULATE COMBINATORIAL DOUBLE ...

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2024 Winter Simulation Conference, WSC 2024

作者： De Antón, Juan Ruiz-Martin, Cristina Villafáñez, Félix Poza, David Department of Business Organization INSISOC University of Valladolid Valladolid Spain Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9798331534202

The surge in electronic procurement is fostering the proliferation of electronic marketplaces and advanced auctions as primary coordination mechanisms. Among these, combinatorial and double auctions are gaining traction in the procurement sector. However, prevalent implementations often assume participants to be perfectly rational, adhering to predefined behaviors within the auction model. These centralized models, while prevalent, fail to capture the intricate dynamics of real auction environments adequately. Consequently, there is a growing recognition of the necessity for decentralized models within an agent-based framework to simulate such auctions authentically. The contribution of this work is the application of the DEVS formalism to develop a decentralized model for a combinatorial iterative double auction to address the limitations of centralized implementations. The model is formally defined, and a case study is presented to verify it against its centralized version. This is the first step toward accommodating agents with varied behavioral patterns within auction simulations. © 2024 IEEE.

关键词： Decentralized systems

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Survey of Commercial Wi-Fi Devices' Energy Detection Mechanism

Survey of Commercial Wi-Fi Devices' Energy Detection Mechani...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Bailey, Jeffrey Liu, Stone Lambadaris, Ioannis Max, Sebastian Sugirtharaj, David Carleton University Department of Systems and Computer Engineering Ottawa Canada Radio Network Research Ericsson Germany Ericsson Sweden

ISBN: (纸本)9798350324136

This paper presents an experimental survey of the capabilities of commercial IEEE 802.11 (Wi-Fi) devices to detect the state of the wireless medium. Wi-Fi devices use a Clear Channel Assessment (CCA) mechanism to determine if the wireless medium is occupied. By subjecting Wi-Fi devices to Gaussian interference signals transmitted through a Universal Software Radio Peripheral (USRP), we assess their CCA behavior. Moreover, we perform a deeper investigation of the Ericsson AP6321 Wi-Fi Access Point prompted by privileged access to its internal operations. Our findings highlight variations between different Wi-Fi devices' CCA mechanisms and reveal floating CCA thresholds. Since CCA forms the fundamental basis for Wi-Fi operation, this study provides insights into coexistence of Wi-Fi with itself and dissimilar technologies. Because future Narrow Band Frequency Hopping (NB FH) systems are a potential technology that commercial Wi-Fi devices need to coexist with, we provide an extended survey of the capability of Wi-Fi devices to detect NB FH signals in unlicensed bands. This survey demonstrates that rapidly changing, on-off signalling does not have a disproportionate impact on Wi-Fi performance. © 2024 IEEE.

关键词： Adaptive Noise Floor Clear Channel Assessment (CCA) Coexistence Dynamic On-Off (DOF) Interference Energy Detection Threshold (EDT) Narrow Band Frequency Hopping (NB FH)

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Level Plane SLAM: Out-of-Plane Motion Compensation in a Globally Stabilized Coordinate Frame for 2D SLAM

Level Plane SLAM: Out-of-Plane Motion Compensation in a Glob...

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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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Assessing Driver Engagement Through Machine Learning Classification of Physiological Measures

Assessing Driver Engagement Through Machine Learning Classif...

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

作者： Lochbihler, Aidan Wallace, Bruce Benthem, Kathleen Van Herdman, Chris Sloan, Will Brightman, Kirsten Goheen, Josh Knoefel, Frank Marshall, Shawn Carleton University Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute Sam 3 Innovation Hub Systems and Computer Engineering Ottawa Canada Carleton University Department of Cognitive Science Ottawa Canada Carleton University Bruyère Research Institute Elizabeth Bruyère Hospital Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute Elizabeth Bruyère Hospital Ottawa Hospital Ottawa Canada

ISBN: (纸本)9781665493840

The assessment to determine if drivers are engaged and supervising autonomous and semi-autonomous vehicles (AV) is becoming an increasingly important task. AVs are becoming more prevalent on the road and adequate driver engagement is a must for safe operation. The full adoption of level 5 vehicles will likely take many years as they do not currently exist for consumer purchase and before this happens the roads will see a mix of level 0-4 vehicles. During this time humans will still be responsible for taking control of the vehicle if a hazardous scenario occurs and the AV does not know how to maneuver around. To have a safe handover from AV to human during these situations the driver must maintain a level of engagement even while the AV is driving. To do this physiological sensors can be used to measure signals such as heart rate and respiration rate, which are known indicators of a driver's engagement. This paper exposes drivers to non-surprise and surprise driving scenarios to assess if attentive drivers can be identified from physiological changes for manual and AV driving. Machine learning (ML) is used to understand the patterns of physiological signals and classify when a driver is engaged during a surprise scenario. Finally, the ML models show a successful ability to classify engaged versus non-engaged drivers with a 73.3% accuracy in manual driving scenarios, 86.7% accuracy in AV, and 70.0% accuracy when the data from both driving scenarios are combined showing the model's ability to generalize. © 2023 IEEE.

关键词： Autonomous vehicles

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Postural Sway Classification using Modified Vision Transformer

Postural Sway Classification using Modified Vision Transform...

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2023 IEEE Biomedical Circuits and systems Conference, BioCAS 2023

作者： Nehary, Ebrahim A. Rajan, Sreeraman Ando, Bruno Carleton University Systems and Computer Engineering Ottawa Canada University of Catania Electric Electronic and Information Engineering Catania Italy

ISBN: (纸本)9798350300260

Continuous monitoring of postural sway in elderly individuals and patients with neurodegenerative diseases is crucial for fall prevention, thereby reducing mortality and morbidity rates. This monitoring can be accomplished through the use of accelerometers. A modified vision transformer (MViT) with patch partition (PP), local self-attention (LSA), and residual connection (RC) is proposed. In this study, the signal magnitude vector (smv) derived from triaxial accelerometer is employed as input to the proposed modified vision transform (MViT) for classifying postural sways into one of the following classes: standing, anteroposterior (AP), medio-lateral (ML), and unstable. PP is carried out using a rectangle window of length (N) and an overlap ratio (α). The role played by the window length and the overlap ratio on the classification performance are studied. Additionally, various augmentation methods are implemented and used in conjunction with PP to improve the classification performance. MViT that has PP implemented using smaller window and larger overlap when trained with augmented data yields the best classification. These results demonstrate that MViT and augmentation improve classification performance and augmentation without PP is inferior to the improvements achieved using a MViT with PP without augmentation. © 2023 IEEE.

关键词： Accelerometers Classification Deep learning Postural sway Signal magnitude value Vision transformer

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