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Precision-Adaptive Task Offloading and Resource Allocation for Efficient Positioning and Sensing in Near-Field IoV systems

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IEEE Internet of Things Journal 2025年

作者： Dai, Cheng Bao, Song Chen, Songlin Garg, Sahil Kaddoum, Georges Sichuan University College of Computer Science Sichuan Chengdu China École de technologie supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India École de technologie supérieure MontréalQCH3C1K3 Canada King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

With the rapid advancement of sixth-generation (6G) network communication technology, improvements in data transmission rates, latency, and reliability have driven substantial growth in Internet of Vehicles (IoV) applications. Among these, the integration of 6G-enabled extremely large-scale antenna arrays (ELAAs) has extended the range of near-field (NF) communication, enabling their application in IoV to facilitate efficient and accurate environmental sensing. Through NF communication, vehicles can achieve high-accuracy localization and perception by analyzing the signal phase, channel state information, and beamforming calculations. However, positioning and sensing tasks place substantial computational and energy demands on edge devices, often exceeding traditional capacity limits. To address this challenge, task offloading has emerged as a solution, with mobile edge computing (MEC) offering a lower-latency alternative to centralized cloud computing by processing tasks at the network edge. Despite these advantages, MEC’s limited resources present challenges as the number of connected vehicles increases. Existing approaches to resource allocation often overlook the varied accuracy requirements of IoV tasks, where high-accuracy tasks like indoor navigation require stringent performance standards, while lower-accuracy tasks may tolerate reduced precision to save resources. Motivated by this, we propose an accuracy-based classification scheme for IoV positioning and sensing tasks, which dynamically adjusts accuracy requirements to reduce delay and energy consumption. Our approach maps total energy, accuracy loss, and delay to an overall quality of service (QoS) metric, and employs an optimization algorithm that leverages gradient descent and greedy strategies to balance resource allocation and accuracy selection. Extensive simulations demonstrate the effectiveness of the proposed scheme in reducing delay and energy consumption while maintaining high accuracy, signific

关键词： Resource allocation

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Towards a Theoretical Framework of Autonomous systems Underpinned by Intelligence and systems sciences

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 52-63页

作者： Yingxu Wang Ming Hou Konstantinos NPlataniotis Sam Kwong Henry Leung Edward Tunstel Imre JRudas Ljiljana Trajkovic International Institute of Cognitive Informatics and Cognitive Computing Department of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of CalgaryCalgary T2N 1N4Canada Toronto Research Centre Defence Research&Development CanadaToronto M3K 2C9Canada Department of Electrical&Computer Engineering University of TorontoToronto M5S3G4Canada Department of Computer Science City University of Hong KongHong KongChina Department of Electrical and Computer Engineering Schulich School of EngineeringUniversity of CalgaryCalgary T2N 1N4Canada Autonomous&Intelligent Systems Department United Technologies Research CenterCT 06108 USA University Research&Innovation Center(EKIK) Óbuda UniversityBudapest H-1034Hungary School of Engineering Science Simon Fraser UniversityBurnaby V5A 1S6Canada

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems *** paper explores the intelligent and mathematical foundations of autonomous *** focuses on structural and behavioral properties that constitute the intelligent power of autonomous *** explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive *** properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems *** paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are *** in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

关键词： Autonomous systems(AS) cognitive systems computational intelligence engineering paradigms intelligence science intelligent mathematics

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Assessing Driving Risk Indicators from Large Driving Data Sets

Assessing Driving Risk Indicators from Large Driving Data Se...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Bruce Wallace Philippe Masson Jonathan Ojangole Kathleen Van Benthem Chris M. Herdman Jocelyn Keillor Rafik Goubran Frank Knoefel Shawn Marshall Bruyère Research Institute SAM 3 Innovation Hub Systems and Computer Engineering Carleton University Ottawa Canada SAM Innovation Hub Computer Science Carleton University Department of Cognitive Science Carleton University Ottawa Canada Aerospace Research Centre National Research Council Canada Flight Research Laboratory Ottawa Canada Systems and Computer Engineering Carleton University Bruyère Research Institute SAM Innovation Hub Ottawa Canada Elisabeth Bruyère Hospital Systems and Computer Engineering Carleton University SAM Innovation Hub Ottawa Hospital Carleton University Bruyère Research Institute Elisabeth-Bruyère Hospital

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

Understanding the driving behaviours that lead to increased risk of collision for older drivers is a serious public safety challenge. Driving is important for older drivers to travel to activities that maintain an active independent lifestyle. Research has shown that varying levels of night driving, velocity and driving exposure may be risk factors. However, there is limited knowledge of what levels are indicative of higher risk for older drivers, especially as compared to a group of similar age peers. Drivers with behaviours significantly above or below the typical norm age group may indicate higher risk. For example, while lower levels of night driving could indicate lowered night-time crash risk due to self-regulation associated with reduced confidence or visual issues, greater amounts of nighttime driving may show increased risk related to over confidence or a need to drive at night. The paper analyzes a large dataset of naturalistic driving data that includes 1 year of driving data for 19,800 drivers and proposes normative models for night driving, driving speed and driving exposure for 5-year age groups from the youngest drivers (age 16) through to drivers over the age of 95. The models provide measures of the average and variance for drivers in each age group. This addresses a gap in the literature due to previous limitations of the available data or study population. The proposed measures allow for driver risk to be identified that is associated with atypical behaviour that is higher or lower than the age group norm. Models are proposed for night driving, velocity and driving exposure that allow the driving by other drivers to now be analyzed to identify with increased risk such as those that are outliers (+/- >2 SD from mean) in comparison to their age group peers.

关键词： Analytical models Visualization Sociology Public security Data models Statistics Accidents

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An LMI adaptive-barrier function global sliding mode control of uncertain nonlinear systems with input saturation

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Ain Shams engineering Journal 2024年第2期15卷

作者： Rahimpour, Rahim Sepestanaki, Mohammadreza Askari Mobayen, Saleh Mokhtare, Zahra Fekih, Afef Assawinchaichote, Wudhichai Zhilenkov, Anton Electrical Engineering Department University of Zanjan Zanjan Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan Department of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette United States Department of Electronic and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok10140 Thailand Department of Cyber-Physical Systems St. Petersburg State Marine Technical University Saint-Petersburg190121 Russia

This paper proposes a barrier function-based adaptive control strategy with a nonlinear sliding surface for a class of uncertain dynamic systems with input saturation. Its main objectives are to ensure robustness to parametric and non-parametric uncertainties whilst eliminating the reaching phase. A global nonlinear switching surface is defined and an adaptive barrier function sliding mode control approach is derived. The barrier strategy is adopted to ensure the convergence of the states to the desired values without overestimating the control gains. System stability is analyzed using the Lyapunov stability theory. The control parameters are obtained using the linear matrix inequality (LMI) approach. The control law is designed to remove the reaching phase. The effectiveness of the proposed approach was validated using a simulation study. The proposed control approach can easily be implemented to various nonlinear uncertain systems. © 2023 THE AUTHORS

关键词： Linear matrix inequalities

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Effect of Surface Roughness of Polymer Dielectric Materials on Resolution of Fine Line Features

Effect of Surface Roughness of Polymer Dielectric Materials ...

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Electronic Components and Technology Conference (ECTC)

作者： Pragna Bhaskar Mohan Kathaperumal Christopher Blancher Mark Losego Madhavan Swaminathan 3D Systems Packaging Research Center School of Material Science and Engineering Georgia Institute of Technology Atlanta USA 3D Systems Packaging Research Center School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA 3D Systems Packaging Research Center Georgia Institute of Technology Atlanta USA Department of Electrical Engineering Pennsylvania State University University Park USA

High density interconnects is a key factor for achieving high bandwidth which is required for AI and high performance compute applications. The high density interconnects can be achieved by, 1) fine lines and spaces in a metal layer, or 2) a stack-up consisting of multiple redistribution layers with not so fine lines and spaces. Fabrication of fine lines and spaces is conventionally achieved by advanced lithography and photoresists. However, in order to attain fine lines and spaces formation, the resists and the following processes should lead to a smooth surface which will yield fine lines and spaces. Demonstration of lines and spaces (L/S) has been a method to evaluate new dielectrics, photoresists and lithography processes. Adhesion between copper and polymer is an important factor determining the stability of these copper lines on the polymer as the substrate goes through different processes such as photoresist stripping and seed layer etching. Several methods have been employed to improve the adhesion between copper and polymer, the most important among them being plasma treatment. This method relies on mechanical roughening of the surface which results in interlocking between the metal and polymer to improve adhesion. However, there have also been reports indicating that excessive roughness resulting in the detachment of copper traces from the substrate. Therefore, it is important to study how the roughness of the dielectric influences the resolution of fine line features. This paper presents results on the effect of surface roughness of the laminated dielectrics on the resolution of lines and spaces. Ultra-low-k (ULK) dielectric materials having dielectric constants ≤ 3 are selected for this study in addition to an industry standard dielectric material for comparison. These ULK materials were considered owing to their significance as build-up layers in 2.5D interposers with multiple redistribution layers (RDL). However, with their decreasing dielectric consta

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Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Alagha, Ahmed Bentahar, Jamal Otrok, Hadi Singh, Shakti Mizouni, Rabeb Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Khalifa University Abu Dhabi United Arab Emirates

Multi-Agent Deep Reinforcement Learning (MDRL) is a promising research area in which agents learn complex behaviors in cooperative or competitive environments. However, MDRL comes with several challenges that hinder its usability, including sample efficiency, curse of dimensionality, and environment exploration. Recent works proposing Federated Reinforcement Learning (FRL) to tackle these issues suffer from problems related to model restrictions and maliciousness. Other proposals using reward shaping require considerable engineering and could lead to local optima. In this paper, we propose a novel Blockchain-assisted Multi-Expert Demonstration Cloning (MEDC) framework for MDRL. The proposed method utilizes expert demonstrations in guiding the learning of new MDRL agents, by suggesting exploration actions in the environment. A model sharing framework on Blockchain is designed to allow users to share their trained models, which can be allocated as expert models to requesting users to aid in training MDRL systems. A Consortium Blockchain is adopted to enable traceable and autonomous execution without the need for a single trusted entity. Smart Contracts are designed to manage users and models allocation, which are shared using IPFS. The proposed framework is tested on several applications, and is benchmarked against existing methods in FRL, Reward Shaping, and Imitation Learning-assisted RL. The results show the outperformance of the proposed framework in terms of learning speed and resiliency to faulty and malicious models. © 2025, CC BY-NC-SA.

关键词： Deep reinforcement learning

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Distributed Controller Design for Discrete-Time systems Via the Integration of Extended LMI and Clique-Wise Decomposition

arXiv

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arXiv 2024年

作者： Fushimi, Sotaro Watanabe, Yuto Sakurama, Kazunori Department of Systems Science Graduate School of Informatics Kyoto University Yoshida-Honmachi Sakyo-ku Kyoto606-8501 Japan Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093 United States Department of System Innovation Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan

This study addresses a distributed controller design problem for discrete-time systems using linear matrix inequalities (LMIs). Sparsity constraints on control gains of distributed controllers result in conservatism via the convexification of the existing methods such as the extended LMI method. In order to mitigate the conservatism, we introduce a novel LMI formulation for this problem, utilizing the clique-wise decomposition method from our previous work on continuous-time systems. By reformulating the sparsity constraint on the gain matrix within cliques, this method achieves a broader solution set. Also, the analytical superiority of our method is confirmed through numerical examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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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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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

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

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.

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Big data in healthcare:Conceptual network structure,key challenges and opportunities

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Digital Communications and Networks 2023年第4期9卷 856-868页

作者： Leonardo B.Furstenaua Pedro Leivas Michele Kremer Sott Michael S.Dohan Jose Ricardo Lopez-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Post-Graduate Program in Industrial Engineering Federal University of Rio Grande Do SulPorto Alegre90035-190Brazil University of Santa Cruz Do Sul Av.Independencia 2293Santa Cruz Do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Faculty of Business Administration Lakehead UniversityThunder BayOntarioCanada Autonomous University of Zacatecas Av.Ramon Lopez Velarde Col.CentroZacatecas98000Mexico Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain York University ON M3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.

关键词： Big data Healthcare digitalization Bibliometric Strategic intelligence Co-word analysis SciMAT

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Metrics and models for developer collaboration analysis in microservice-based systems. A systematic mapping study 32

Metrics and models for developer collaboration analysis in m...

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Joint of the 32nd International Workshop on Software Measurement and the 17th International Conference on Software Process and Product Measurement, IWSM-MENSURA 2023

作者： Li, Xiaozhou Abdelfattah, Amr S. Su, Ruoyu Lee, Josep Aponte, Ernesto Koerner, Rachel Cerny, Tomas Taibi, Davide M3S University of Oulu Oulu Finland CloudSEA.AI Tampere University Tampere Finland Department of Computer Science Baylor University WacoTX United States University of Richmond RichmondVA United States Universidad Del Sagrado Corazón San Juan Puerto Rico Systems and Industrial Engineering University of Arizona TucsonAZ United States

Microservices enable different teams to develop and deploy services independently. Practitioners are frequently mentioning the need for independence between teams and developers, and the need for metrics to measure developer collaboration. To shed light on the existing metrics and models, we conducted a Systematic Mapping Study to identify models for measuring the development activities, the metrics adopted by these methods, and the output produced by the methods themselves. We identified 10 different models proposed in 14 research papers. Results show that a large amount of the existing models adopt qualitative metrics, questionnaires, and surveys to collect the information required while others use information from issue tracking and version systems. The results will enable practitioners and researchers to further validate and extend the research on metrics for evaluating the collaboration and independence among developers. © 2021 Copyright for this paper by its authors.

关键词： Mapping

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