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Proximity-based service discovery for distributed digital twin systems

Discover Internet of Things 2025年第1期5卷 1-24页

作者： Rothermel, Kurt Herzog, Otthein Zhiqiang, Siegfried Wu Institute Of Parallel and Distributed Systems University of Stuttgart Universitaetsstr. 38 Stuttgart70569 Germany College of Architecture and Urban Planning Tongji University 1239 Siping Road Shanghai200092 China Department of Mathematics and Computer Science University of Bremen Bibliothekstrasse 5 Bremen23884 Germany

Over the past decade, there has been a significant increase in interest in digital twin (DT) technology in a variety of domains. While research on DTs of single assets was initially prevalent, there has been a notable shift towards distributed systems of DTs, which connect to each other to collaborate. Typically, collaboration is enabled by DTs providing services that can be consumed by other DTs. In service-oriented systems, a service is typically identified by type information. However, this is not sufficient in distributed DT systems, where DTs associated with different physical entities may provide the same type of service. Consequently, selecting the appropriate service depends not only on the service type, but also on the associated physical entity. However, requiring DTs to know the mapping of services to their physical environment is not feasible for large dynamic systems. This paper presents a novel proximity-based service discovery method that allows DTs to select services based on service type and their proximity to other objects. That is, service specifications are fully abstracted from the mapping of services to physical objects, relieving DTs from maintaining information about this mapping. Furthermore, service discovery is robust to changes in the physical environment and service population. The proposed service discovery method has been implemented on top of a spatial DBMS. We argue that this implementation is optimal in terms of network utilization and latency, and perform comprehensive evaluations to show the performance of discovery queries as a function of their complexity. © The Author(s) 2025.

关键词： Location based services

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models 16

VisioSLR: A Vision Data-Driven Framework for Sign Language V...

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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Ismail, Leila Shahin, Nada Tesfaye, Henok Hennebelle, Alain Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Challenges and Initial Measurements on Communication and Localization for Mountain Bike Safety Applications

Challenges and Initial Measurements on Communication and Loc...

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Annual Conference on Wireless On Demand Network systems and Services (WONS)

作者： Michele Zucchelli Marcus Marx Max Hörmann Frank Kargl Michele Segata Department of Information Engineering and Computer Science University of Trento Italy Institute of Distributed Systems Ulm University Germany

ISBN: (数字)9783903176713

ISBN: (纸本)9798331522025

As of today, research in vulnerable road users (VRUs) applications is mainly focused on safety in urban road scenarios. There is little to be found in the literature with respect to VRUs in mountain areas, where mountain biking and hiking also present risks of collision. Here, it is not yet clear whether existing localization and communication technologies would provide sufficient performance in such harsh environments. In this work, we start answering this question by presenting the results of a measurement campaign which took place in a mountain area in Northern Italy during Summer 2024. With respect to localization, we show that global navigation satellite system (GNSS)-based localization alone often provides unreliable results due to vegetation and terrain. Trilateration with Bluetooth Low Energy (BLE) and beacons mounted at fixed positions performs well in some circumstances and can be used to enhance GNSS, however, we also observed many unclear effects that require further investigations. Concerning communication, the results indicate that both direct short range communications (DSRC) and cellular V2X (C-V2X) works fairly well in most cases, but terrain characteristics might induce packet losses or low signal quality, whereas instabilities in GNSS fixes might also cause C-V2X outages.

关键词： Location awareness Wireless communication Global navigation satellite system Roads Vegetation mapping Packet loss Communications technology Safety Bluetooth Low Energy Network systems

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Towards New Simulation Models for DL-Based Video Streaming in Edge Networks 24

Towards New Simulation Models for DL-Based Video Streaming i...

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Proceedings of the Winter Simulation Conference

作者： Abdolreza Abhari Distributed Systems & Multimedia Processing Laboratory (DSMP Lab) Department of Computer Science Toronto Metropolitan University Toronto ON CANADA

ISBN: (纸本)9798331534202

To evaluate novel solutions for edge computing systems, suitable distribution models for simulation are essential. The extensive use of deep learning (DL) in video analytics has altered traffic patterns on edge and cloud servers, necessitating innovative models. Queuing models are used to simulate the performance and stability of edge-enabled systems, particularly video streaming applications. This paper demonstrates that traditional Markovian M/M/s and general distribution G/G/s queuing models must be revamped for accurate simulation. We examined these queuing models by characterizing the real data with discrete and continuous distributions for arrival rates to homogenous servers in AI-based video analytics edge systems. Based on achieved results, traditional methods for finding general distributions are inadequate, and an automation method for finding empirical distribution is needed. Therefore, we introduce a novel approach using a generative adversarial network (WGAN) to generate artificial data to automate the process of estimating empirical distribution for modeling these applications.

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Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction

arXiv

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arXiv 2025年

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Distributed Systems and Informatic Devices Silesian University of Technology Poland

In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm with heterogeneous transfers. We design dense and sparse feature tensors to deal with the sparsity of data sources. Heterogeneous federated learning is developed to share asynchronous parts of networks and select appropriate models for knowledge transfer. Experimental results show that the proposed HFL achieves the lowest prediction error among all benchmark systems on eight out of ten prediction tasks, with MSE reduction of 94.8%, 48.3%, and 52.1% compared to the benchmark systems. These results demonstrate the effectiveness of HFL in transferring knowledge from heterogeneous domains, especially in the smaller target domain. Ablation studies then demonstrate the effectiveness of the designed mechanisms for heterogeneous domain selection and switching in predicting healthcare time series with privacy, model security, and heterogeneous knowledge transfer. © 2025, CC BY-NC-SA.

关键词： Federated learning

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia computer science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Heterogeneous Federated Learning systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism

arXiv

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arXiv 2025年

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Srivastava, Gautam Yun, Unil Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Distributed Systems and Informatic Devices Silesian University of Technology Poland Department of Math and Computer Science Brandon University Brandon Canada Department of Computer Science and Engineering Sejong University Korea Republic of

Time-series prediction is increasingly popular in a variety of applications, such as smart factories and smart transportation. Researchers have used various techniques to predict power consumption, but existing models lack discussion of collaborative learning and privacy issues among multiple clients. To address these issues, we propose Multi-Head Heterogeneous Federated Learning (MHHFL) systems that consist of multiple head networks, which independently act as carriers for federated learning. In the federated period, each head network is embedded into 2-dimensional vectors and shared with the centralized source pool. MHHFL then selects appropriate source networks and blends the head networks as knowledge transfer in federated learning. The experimental results show that the proposed MHHFL systems significantly outperform the benchmark and state-of-the-art systems and reduce the prediction error by 24.9% to 94.1%. The ablation studies demonstrate the effectiveness of the proposed mechanisms in the MHHFL (head network embedding and selection mechanisms), which significantly outperforms traditional federated average and random transfer. © 2025, CC BY-NC-SA.

关键词： Federated learning

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Correction to: A comprehensive performance evaluation of container migration strategies

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Computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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Optimizing Emergency department Staff Configurations Through Simulation and Montecarlo Clustering Search Algorithm (Mcsa)

SSRN

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SSRN 2025年

作者： Harita, Maria Wong, Alvaro Rexachs, Dolores Bruballa, Eva Epelde, Francisco Luque, Emilio Department of Computer Architecture and Operating Systems Carrer de les Sitges Cerdanyola del Valles Barcelona08193 Spain Computer Science School Avinguda de la Generalitat 202 Sant Cugat del Valles Barcelona08174 Spain Department of Medicine Hospital Universitari Parc Taulí Parc Tauli 1 Sabadell Barcelona08208 Spain

The Hospital Emergency department (ED) provides critical care for acute and urgent conditions, making it one of the most complex areas in healthcare. Optimizing staff configurations to reduce patient waiting times and improve efficiency is a significant challenge. This study introduces a methodology that integrates an agent-based simulation model with the Montecarlo Clustering Search Algorithm (MCSA). Designed to handle multidimensional and combinatorial challenges, the approach identifies optimal staff configurations to manage large patient volumes with varying acuity levels within a ED. Key Performance Indicators (KPIs), such as Length of Stay (LoS), guide the optimization process by aligning medical staff allocation—doctors, nurses, and technicians—with patient arrival patterns. The proposed framework ensures practical, real-world solutions without reallocating personnel from other hospital areas, maintaining operational balance while scaling resources to meet ED demands. A key advantage of MCSA is its ability to reduce computational time compared to exhaustive search methods, enabling efficient exploration of complex search spaces. Results validate this methodology, demonstrating significant reductions in LoS compared to a baseline configuration. By identifying effective staff arrangements, this approach provides decision- makers with a robust and scalable tool to optimize ED operations. It enhances system performance and improves KPIs while avoiding the impracticality of exhaustive computational methods, ensuring applicability in healthcare environments. © 2025, The Authors. All rights reserved.

关键词： Decision making

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SatCooper: Enhancing Cooperative Inference Analytics for Satellite Service via Multi-exit DNNs

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Qiyang Wang, Shangguang Guan, Jinglong Donta, Praveen Kumar Ma, Xiao Prasad, RangaRao Venkatesha Dustdar, Schahram Liu, Xuanzhe Peking University School of Computer Science Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Stockholm University Department of Computer and Systems Sciences Kista16455 Sweden Beiyou Shenzhen Institute Shenzhen China Netherlands TU Wien Distributed System Group Wien1040 Austria UPF Barcelona Spain

As a key technology of intelligent satellite-enabled services in B5G or 6G networks, deploying Deep Neural Networks (DNN) models on satellites has been a notable trend, catering to the daily demand for extensive computing-intensive and latency-sensitive tasks. The computing resources are strategically deployed on satellites where sensor data is generated or collected, facilitating the fine-grained computational inference of DNN-based tasks. However, no prior study has comprehensively explored the crucial inference challenges-e.g., the trade-off between the number of tasks completed and accuracy and partitioning models in multi-exit models-in the resource-constrained space environment. Effective scheduling frameworks cater to various streams of inference tasks are scarce because inference performance may deviate from the ideal situation due to changes in task system status, such as task profiles and network state. To this end, we first formulate a gain-aware in-orbit computing inference problem to strike a proper trade-off between inference latency and the number of tasks completed by dynamically selecting optimal early exit points and model partitioning points. We propose an offline dynamic programming-based algorithm that provides an effective solution when comprehensive system details are to be predicted. We have developed an online learning-based method to schedule inference tasks with uncertain and dynamic system statuses in real-world situations. Our evaluation shows that, compared to baseline methods, the online learning-based algorithm can improve task gain by an average of 87.3% across various tasks. © 2002-2012 IEEE.

关键词： Dynamic programming

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