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Quality of Service (QoS)-Driven Edge computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions 1

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NIELIT's International Conference on Communication, Electronics and Digital Technology, NICE-DT 2023

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer and Information Sciences The University of Hyderabad Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

ISBN: (数字)9789819916993

ISBN: (纸本)9789819916986

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in health care and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses Edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage, and monitor the hospital systems in real time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Metaverse Services: The Way of Services Towards the Future

Metaverse Services: The Way of Services Towards the Future

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Xu, Xiaofei Sheng, Quan Z. Benatallah, Boualem Chen, Zhong Gazda, Robert Saddik, Abdulmotableb Ei Singh, Munindar P. Faculty of Computing Harbin Institute of Technology China Macquarie University School of Computing NSW2109 Australia Dublin City University School of Computing Ireland Peking University School of Computer Science China Wireless Networking Lab Interdigital United States University of Ottawa School of Electrical Engineering and Computer Science Canada North Carolina State University Department of Computer Science United States

ISBN: (纸本)9798350304855

With the emergence of new generation of digital technologies, e.g., artificial intelligence, blockchain, cloud computing, big data, edge computing, 5G/6G, VR/AR/MR, and the Internet of Things, an exciting era of metaverse is coming. Interacted and linked with the physical world, metaverse offers a platform of a new social ecosystem, dealing with digital twins and empowering virtual-reality symbiosis. In metaverse, social activities and business processes are performed based on the sequences of workflow or service processes. Bridging both the virtual space and the real world, such metaverse services are more complicated and present many new challenges and research topics. In this paper, the concept and characteristics of metaverse services are presented, the key technologies and typical use cases are reviewed, and the future challenges and opportunities of metaverse services are also discussed. © 2023 IEEE.

关键词： Internet of things

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Investigating the Use of Ray Tracing for Signal-Level Radar Simulation in Space Surveillance 5

Investigating the Use of Ray Tracing for Signal-Level Radar ...

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5th International Conference on Information Communication and Signal Processing, ICICSP 2022

作者： Martin, Mogamat Yaaseen Winberg, Simon Gaffar, Yunus Abdul MacLeod, David University of Cape Town Department of Electrical Engineering Cape Town South Africa Advanced Computer Engineering Lab Centre for High Performance Computing Cape Town South Africa

ISBN: (纸本)9781665485890

The growing number of objects in the Low Earth Orbit is becoming increasingly concerning to astronomers and space missions - particularly due to the limitations in tracking accuracy due to the availability and placements of surveillance sensors. This paper connects to this issue, presenting the development of an open-source, ray-traced, signal-level pulsed radar simulator for use in exploring and planning space monitoring solutions without using real-time data feeds. The simulator was also developed around the use of the NVIDIA® OptiX™ engine to accommodate its ray-tracing features and accelerate performance. This was designed to aid astronomers and researchers in space situational awareness applications through the simulation of radar designs for orbital surveillance experiments. The developed tool was also compared against a more streamlined application that used point-model approximations for quick-look simulations, and the trade-offs between both simulators were evaluated. While the use of ray tracing resulted in significant speed costs, it was found that the algorithm also introduced more realistic results relative to point-model simulations - providing various advantages in scenarios involving shadowing and multiscatter. © 2022 IEEE.

关键词： Ray tracing

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An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

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Digital Communications and Networks 2022年第6期8卷 900-910页

作者： M.S.Mekala Gautam Srivastava Ju H.Park Ho-Youl Jung Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38544South Korea RLRC LAb for Autonomous Vehicle Parts and Materials Innovation Yeungnam UniversityGyeongsan38544South Korea Department of Math and Computer Science Brandon UniversityBrandonCanada Department of Electrical Engineering Yeungnam UniversityGyeongsanSouth Korea Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and *** services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and *** issues lead to computation and communication overhead of servers and computation *** address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and ***,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication ***,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge *** simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.

关键词： Edge computing Adaptive trust weight(ATW)model Quotient user-centric coeval-learning(QUCL)mechanism Deep learning Service reliability

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Discovering High Utility Episodes in Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 473-486页

作者： Gan, Wensheng Lin, Jerry Chun-Wei Chao, Han-Chieh Yu, Philip S. Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China Western Norway University of Applied Sciences Department of Computing Mathematics and Physics Bergen5063 Norway National Dong Hwa University Department of Electrical Engineering Hualien974 Taiwan University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

Sequence data are more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this article, we propose an efficient utility mining approach, namely, UMEpi: utility mining of high-utility episodes from complex event sequences. We propose the concept of remaining utility of episodes and achieve a tighter upper bound, namely, episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategies can achieve better improvements in mining efficiency. The search space of UMEpi w.r.t. a prefix-based lexicographic sequence tree is spanned and determined recursively for mining high-utility episodes, by prefix-spanning in a depth-first way. Finally, extensive experiments on four real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequences. Furthermore, the improved variants of UMEpi significantly outperform the baseline in terms of execution time, memory consumption, and scalability. © 2020 IEEE.

关键词： Artificial intelligence

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Shared Bodily Fusion: Leveraging Inter-Body electrical Muscle Stimulation for Social Play

Shared Bodily Fusion: Leveraging Inter-Body Electrical Muscl...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Patibanda, Rakesh Overdevest, Nathalie Nisal, Shreyas Saini, Aryan Elvitigala, Don Samitha Knibbe, Jarrod van den Hoven, Elise Mueller, Florian Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia School of Electrical Engineering and Computer Science Faculty of Engineering The University of Queensland St LuciaQLD Australia Materialising Memories & Visualisation Institute University of Technology Sydney Sydney Australia Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9798400705830

Traditional games like "Tag" rely on shared control via inter-body interactions (IBIs) – touching, pushing, and pulling – that foster emotional and social connection. Digital games largely limit IBIs, with players using their bodies as input to control virtual avatars instead. Our "Shared Bodily Fusion" approach addresses this by fusing players’ bodies through a mediating computer, creating a shared input and output system. We demonstrate this approach with "Hidden Touch", a game where a novel social electrical muscle stimulation system transforms touch (input) into muscle actuations (output), facilitating IBIs. Through a study (n=27), we identifed three player experience themes. Informed by these fndings and our design process, we mapped their trajectories across our three experiential spaces – threshold, tolerance, and precision – which collectively form our design framework. This framework facilitates the creation of future digital games where IBIs are intrinsic, ultimately promoting the many benefts of social play. © 2024 Copyright held by the owner/author(s).

关键词： Muscle

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Enhanced Multidimensional Harmonic Retrieval in MIMO Wireless Channel Sounding

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IEEE Internet of Things Journal 2025年第11期12卷 16243-16255页

作者： Zhang, Yanming Xu, Wenchao Jin, A-Long Tang, Tianquan Li, Min Ma, Peifeng Jiang, Lijun Gao, Steven The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Computing Hong Kong Xi’an Jiaotong-Liverpool University Department of Communications and Networking Suzhou215123 China HKU Zhejiang Institute of Research and Innovation Lab for Aerodynamics and Acoustics Hangzhou311305 China Tianjin University School of Microelectronics Tianjin300000 China Institute of Space and Earth Information Science The Chinese University of Hong Kong Department of Geography and Resource Management Hong Kong Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO65409 United States

This article introduces a recursive parallel dynamic mode decomposition (RPDMD) scheme tailored for multidimensional harmonic retrieval (MHR), specifically applied to MIMO wireless channel sounding. The RPDMD algorithm is devised to address the complexities inherent in multidimensional scenarios, leveraging the dynamic mode decomposition (DMD) framework within a recursive parallel structure. Initially, the observed tensorial multidimensional harmonic data is transformed into a 2-D matrix format along the rth dimension. Subsequently, DMD dissects this matrix data into eigenvalues and their associated modes. The real and imaginary components of the DMD eigenvalues yield damping factors and frequencies in the rth dimension, respectively. Furthermore, recursive DMD is employed to scrutinize each mode independently for parameter retrieval across the remaining dimensions, enabling parallel analysis. Ultimately, this high-dimensional correlated decomposition scheme delivers paired damping factors and frequencies for all tones. Notably, the proposed approach can ascertain the number of tones in undamped sinusoidal signals, making it particularly suitable for MHR even without prior knowledge of the source count. Numerical experiments demonstrate the accuracy and robustness of the RPDMD scheme, with comparative analysis indicating that RPDMD outperforms similar methods, achieving optimal results with minimal mean square error in high signal-to-noise ratio scenarios. This work presents an effective data-driven solution for the MHR problem in MIMO wireless channel sounding. © 2014 IEEE.

关键词： Eigenvalues and eigenfunctions

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Advancing Proactive Producer Mobility Management in Named Data Networking: A Conceptual Model 7

Advancing Proactive Producer Mobility Management in Named Da...

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Zukri, Nurul Hidayah Ahmad Arif, Ahmad Suki Che Mohamed Alsamman, Mohammed Abrar, Ahmad Fazea, Yousef Universiti Teknologi MARA Cawangan Perlis Kampus Arau Perlis Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia InterNetWorks Research Lab Universiti Utara Malaysia Kedah Malaysia Department of Computer Science and Electrical Engineering Marshall University One John Marshall Dr. HuntingtonWV25755 United States

ISBN: (纸本)9798331519513

In recent years, the Named Data Networking (NDN) paradigm has emerged as a promising candidate for future Internet architecture. Underpinned by the notion of a data-centric forwarding philosophy, NDN is purported to offer seamless network communication for mobile users. However, while it provides inherent support for consumer mobility, viable solutions for producer mobility remain insufficient. Therefore, this study aims to highlight the mobility issues in NDN, particularly concerning high latency, Interest packet loss, and Interest retransmission, with a focus on producer mobility. In this paper, we critically review previous works based on proactive and reactive approaches. Furthermore, we propose a mobility management model that adopts a proactive approach, estimating the producer's handoff time from the connected access point. The proposed model leverages this information to notify consumers about the moving producer, aiming to prevent consumers from sending Interest to the mobile producer during handoff and thereby overcome the associated challenges. © 2024 IEEE.

关键词： Named Data Network Proactive Producer Mobility

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Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition 60

Self-supervised Semantic-driven Phoneme Discovery for Zero-r...

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60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Wang, Liming Feng, Siyuan Hasegawa-Johnson, Mark Yoo, Chang D. Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States Multimedia Computing Group Delft University of Technology Netherlands Artificial Intelligence and Machine Learning Lab KAIST Korea Republic of

ISBN: (纸本)9781955917216

Phonemes are defined by their relationship to words: changing a phoneme changes the word. Learning a phoneme inventory with little supervision has been a longstanding challenge with important applications to under-resourced speech technology. In this paper, we bridge the gap between the linguistic and statistical definition of phonemes and propose a novel neural discrete representation learning model for self-supervised learning of phoneme inventory with raw speech and word labels. Given the availability of phoneme segmentation and some mild conditions, we prove that the phoneme inventory learned by our approach converges to the true one with an exponentially low error rate. Moreover, in experiments on TIMIT and Mboshi benchmarks, our approach consistently learns a better phoneme-level representation and achieves a lower error rate in a zero-resource phoneme recognition task than previous state-of-the-art self-supervised representation learning algorithms. © 2022 Association for Computational Linguistics.

关键词： Speech recognition

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Beyond MobileNet: An Improved MobileNet for Retinal Diseases 1

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Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th International Conference on Medical Image computing and computer Assisted Intervention, MICCAI 2023

作者： Zhu, Wenhui Qiu, Peijie Chen, Xiwen Li, Huayu Wang, Hao Lepore, Natasha Dumitrascu, Oana M. Wang, Yalin School of Computing and Augmented Intelligence Arizona State University TempeAZ United States McKeley School of Engineering Washington University in St. Louis St. LouisMO United States School of Computing Clemson University ClemsonSC United States Department of Electrical and Computer Engineering The University of Arizona TucsonAZ United States Department of Neurology Mayo Clinic PhoenixAZ United States CIBORG Lab Department of Radiology Children’s Hospital Los Angeles Los AngelesCA United States

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopic Maculopathy (MM) is the leading cause of severe vision loss or blindness. Deep learning-based automated tools are indispensable in assisting clinicians in diagnosing and monitoring RD in modern medicine. Recently, an increasing number of works in this field have taken advantage of Vision Transformer to achieve state-of-the-art performance with more parameters and higher model complexity compared to Convolutional Neural Networks (CNNs). Such sophisticated model designs, however, are prone to be overfitting and hinder their advantages in specific tasks in medical image analysis. In this work, we argue that a well-calibrated CNN model may mitigate these problems. To this end, we empirically investigated the macro and micro designs of a CNN and its training strategies by starting with a standard MobileNet. Based on the investigation, we proposed a lightweight MobileNet training framework equipped with a series of optimal parameters and modules based on retinal images. As a result of performance, our model secured third place in the MICCAI MMAC 2023 Challenge - Classification of Myopic Maculopathy. Our software package is available at https://***/Retinal-Research/NN-MOBILENET. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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