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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

共 30780 条记录，以下是1501-1510 订阅

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Compute- and Data-Intensive Networks: The Key to the Metaverse 1

Compute- and Data-Intensive Networks: The Key to the Metaver...

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1st international conference on 6G Networking (6GNet)

作者： Cai, Yang Llorca, Jaime Tulino, Antonia M. Molisch, Andreas F. Univ Southern Calif Los Angeles CA 90007 USA NYU New York NY USA

ISBN: (纸本)9781665467636

The worlds of computing, communication, and storage have for a long time been treated separately, and even the recent trends of cloud computing, distributed computing, and mobile edge computing have not fundamentally changed the role of networks, still designed to move data between end users and pre-determined computation nodes, without true optimization of the end-to-end compute-communication process. However, the emergence of Metaverse applications, where users consume multimedia experiences that result from the real-time combination of distributed live sources and stored digital assets, has changed the requirements for, and possibilities of, systems that provide distributed caching, computation, and communication. We argue that the real-time interactive nature and high demands on data storage, streaming rates, and processing power of Metaverse applications will accelerate the merging of the cloud into the network, leading to highly-distributed tightly-integrated compute- and data-intensive networks becoming universal compute platforms for next-generation digital experiences. In this paper, we first describe the requirements of Metaverse applications and associated supporting infrastructure, including relevant use cases. We then outline a comprehensive cloud network flow mathematical framework, designed for the end-to-end optimization and control of such systems, and show numerical results illustrating its promising role for the efficient operation of Metaverse-ready networks.

关键词： Metaverse virtual reality augmented reality immersive video edge computing caching distributed cloud decentralized control 5G networks

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Blockchain-based Zero Trust on the Edge

Blockchain-based Zero Trust on the Edge

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international conference on Computational Science and Computational Intelligence (CSCI)

作者： Bicer, Cem Murturi, Ilir Donta, Praveen Kumar Dustdar, Schahram TU Wien Distributed Syst Grp A-1040 Vienna Austria

ISBN: (纸本)9798350361513;9798350372304

Internet of Things (IoT) devices pose significant security challenges due to their heterogeneity (i.e., hardware and software) and vulnerability to extensive attack surfaces. Today's conventional perimeter-based systems use credential-based authentication (e.g., username/password, certificates, etc.) to decide whether an actor can access a network. However, the verification process occurs only at the system's perimeter because most loT devices lack robust security measures due to their limited hardware and software capabilities, making them highly vulnerable. Therefore, this paper proposes a novel approach based on Zero Trust Architecture (ZTA) extended with blockchain to further enhance security. The blockchain component serves as an immutable database for storing users' requests and is used to verify trustworthiness by analyzing and identifying potentially malicious user activities. We discuss the framework, processes of the approach, and the experiments carried out on a testbed to validate its feasibility and applicability in the smart city context. Lastly, the evaluation focuses on non-functional properties such as performance, scalability, and complexity.

关键词： Zero Trust Blockchain Edge computing Edge Cloud computing Security

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Characterizing CNN-Based Vessel Detection Algorithm Sensitivity to Optical sensor Artifacts 22

Characterizing CNN-Based Vessel Detection Algorithm Sensitiv...

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22nd ieee international conference on Machine Learning and Applications, ICMLA 2023

作者： Warner, John G. Davidson, Quinton Tietz, Michael Scharpf, William Keene, Charles Naval Center for Space Technology Us Naval Research Laboratory WashingtonDC United States

ISBN: (纸本)9798350345346

The number of space-based optical imaging systems have substantially grown in recent years. Many of these commercial based vendors are exploring concepts to move processing applications directly to the space segment or to incorporate advanced algorithms in their ground segment. Typically, Convolutional Neural Network (CNN)-based detection algorithms are considered due to their high reliability in laboratory settings. However, this class of algorithms routinely demonstrates degraded performance when unexpected phenomena are introduced to the data. As more near-real-time applications are considered, CNN-based algorithms will be subjected to sensor calibration artifacts. This paper seeks to characterize the response of CNN-based vessel detection algorithms to common electro-optical sensor calibration artifacts. Several artifacts are explored, including added sensor noise and failed detector artifacts. Each of the explored artifacts uniquely impact the algorithm detection performance. It is found that applying Poisson distributed random noise to the imagery substantially degrades model performance by increasing false alarm rate. Likewise, the application of a uniformly distributed random scale factor to the imagery degrades model performance by lowering positive detection rate. These results may inform plans to implement CNN-based algorithms directly in the space-segment by characterizing typical performance variations due to underlying sensor calibration issues. © 2023 ieee.

关键词： Automatic Target Recognition CNN maritime onboard processing spacecraft

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SpecTextor: End-to-End Attention-based Mechanism for Dense Text Generation in Sports Journalism 8

SpecTextor: End-to-End Attention-based Mechanism for Dense T...

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8th ieee international conference on Smart computing (SMARTCOMP)

作者： Ghosh, Indrajeet Ivler, Matthew Ramamurthy, Sreenivasan Ramasamy Roy, Nirmalya Univ Maryland Baltimore Cty Mobile Pervas & Sensor Comp Lab Baltimore MD 21228 USA Univ Maryland Baltimore Cty Ctr Real Time Distributed Sensing & Auton Cards Baltimore MD 21228 USA Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD 21228 USA Pomona Coll Dept Comp Sci Claremont CA 91711 USA

ISBN: (纸本)9781665481526

Language-guided smart systems can help to design next-generation human-machine interactive applications. The dense text description is one of the research areas where systems learn the semantic knowledge and visual features of each video frame and map them to describe the video's most relevant subjects and events. In this paper, we consider untrimmed sports videos as our case study. Generating dense descriptions in the sports domain to supplement journalistic works without relying on commentators and experts requires more investigation. Motivated by this, we propose an end-to-end automated text-generator, SpecTextor, that learns the semantic features from untrimmed videos of sports games and generates associated descriptive texts. The proposed approach considers the video as a sequence of frames and sequentially generates words. After splitting videos into frames, we use a pre-trained VGG-16 model for feature extraction and encoding the video frames. With these encoded frames, we posit a Long Short-Term Memory (LSTM) based attention-decoder pipeline that leverages soft-attention mechanism to map the semantic features with relevant textual descriptions to generate the explanation of the game. Because developing a comprehensive description of the game warrants training on a set of dense time-stamped captions, we leverage two available public datasets: ActivityNet Captions and Microsoft Video Description. In addition, we utilized two different decoding algorithms: beam search and greedy search and computed two evaluation metrics: BLEU and METEOR scores.

关键词： Sports Journalism Semantic Knowledge LSTM Soft-Attention Mechanism Beam Search Greedy Search Human-Machine Interactive Applications

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distributed Effect Evaluation Algorithm of Computer English Online Platform based on Hibernate Task-based Data Architecture

Distributed Effect Evaluation Algorithm of Computer English ...

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6th international conference on Intelligent computing and Control systems, ICICCS 2022

作者： Wang, Taofeng Guangdong Heyuan Vocational Technology Institute Guangdong 517000 China

ISBN: (纸本)9781665410359

distributed Effect Evaluation Algorithm of the computer English online platform based on Hibernate Task-based Data Architecture is studied in this paper. The image file generated by the CIAB method does not store the state of the system before shutdown. Using this image file to restore, the system will cause inconsistencies between the fast startup and the normal startup system. Hence, to deal with this challenge, the Hibernate framework is combined. Using a specific pattern in a specific application, scenario can obtain a code structure with a clear structure, easy to extend and understand, and is applied to the effect evaluation algorithm into the platform construction. Through the testing under different scenarios, the performance is validated. © 2022 ieee.

关键词： Computer architecture

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Studying the Impact of Smart Meter Placement on Low-Voltage Grid State Estimation 5

Studying the Impact of Smart Meter Placement on Low-Voltage ...

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5th international conference on Smart Energy systems and Technologies (SEST)

作者： Zhang, Haoyang Zufferey, Thierry Swiss Fed Inst Technol Power Syst Lab Zurich Switzerland Eindhoven Univ Technol Elect Energy Syst Eindhoven Netherlands

ISBN: (数字)9781665405577

ISBN: (纸本)9781665405577

This paper comprehensively investigates the influence of smart meter allocation methods, considering sensors at grid buses and branches, on the performance of distribution system state estimation algorithms. Three algorithms are used, namely the Weighted Least Squares, the Extended Kalman Filter, and the Schweppe-type GM-estimator with the Huber psi-function. These are tested on a real low-voltage distribution grid with radial structure for multiple scenarios characterized by different penetration levels and types of measurements. Based on Monte Carlo simulations, different locations of sensors at buses and branches are considered for each scenario. An empirical study is carried out to assess the correlation of the placement of meters with the state estimation error. The results suggest that bus meters are most profitable at customers with the highest energy consumption. In addition, well distributed sensors at the grid branches based on a newly proposed 'path search' method appear to be the most effective.

关键词： Meters Low voltage Monte Carlo methods sensor phenomena and characterization Smart meters sensor systems Resource management

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HIERARCHICAL TRAINING FOR distributed DEEP LEARNING BASED ON MULTIMEDIA DATA OVER BAND-LIMITED NETWORKS 29

HIERARCHICAL TRAINING FOR DISTRIBUTED DEEP LEARNING BASED ON...

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ieee international conference on Image Processing (ICIP)

作者： Qi, Siyu Chamain, Lahiru D. Ding, Zhi Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

distributed deep learning (DL) plays a critical role in many wireless Internet of Things (IoT) applications including remote camera deployment. This work addresses three practical challenges in cyber-deployment of distributed DL over band-limited channels. Specifically, many IoT systems consist of sensor nodes for raw data collection and encoding, and servers for learning and inference tasks. Adaptation of DL over band-limited network data links has only been scantly addressed. A second challenge is the need for pre-deployed encoders being compatible with flexible decoders that can be upgraded or retrained. The third challenge is the robustness against erroneous training labels. Addressing these three challenges, we develop a hierarchical learning strategy to improve image classification accuracy over band-limited links between sensor nodes and servers. Experimental results show that our hierarchically-trained models can improve link spectrum efficiency without performance loss, reduce storage and computational complexity, and achieve robustness against training label corruption.

关键词： Hierarchical training image compression and classification auto-encoders information theory

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Development of under Bed Mounted Obrid-sensor for Bed-Exit Detection 8

Development of under Bed Mounted Obrid-Sensor for Bed-Exit D...

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8th ieee/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2023

作者： Tanaka, Shunki Nishifuji, Seiji Hayata, Tomohiro Kotoku, Kazuko Eguchi, Eri Kitazono, Yuhki Serikawa, Seiichi Nakashima, Shota Yamaguchi University Graduate School of Sciences and Technology for Innovation Ube Japan Fukuoka University Faculty of Medicine Fukuoka Japan Ube Frontier University Faculty of Nursing Department of Nursing Yamaguchi Japan National Institute of Technology Kitakyushu College Department of Creative Engineering Kitakyushu Japan Kyushu Institute of Technology Graduate School of Engineering Kitakyushu Japan

ISBN: (纸本)9798350373615

In an aging society, accidents involving the elderly constitute a significant social issue. Constant supervision by caregivers, regardless of day or night, is essential for accident prevention, but this leads to an increased burden on caregivers, posing a serious challenge. Against this backdrop, there is a demand for the development of bed-exit detection systems as a monitoring solution for the elderly. Previous research has proposed a system capable of detecting a person's bed-exit while preserving privacy using a sensor called Obrid-sensor. However, it presented practical challenges. Thus, this study proposes a bed-exit detection method that can adapt to actual environmental changes. The proposed method installs the sensor with infrared LEDs and IR filters under the caregiving bed. Moreover, we extracted the features of the target by applying the inter-frame difference methods to the brightness waveforms obtained. Accordingly, it becomes feasible to robustly detect bed-exit events while minimizing the influence of natural and artificial lighting. To evaluate the effectiveness of the proposed method, verification experiments were conducted against diverse background changes in actual environments. The results revealed that the bed-exit detection rate reached 100%, confirming the practicality of the proposed method, regardless of changes in the real environment or nighttime conditions. Additionally, the false-positive rate was restrained to 10.7% or less against factors potentially leading to misidentification, ensuring a reduction in erroneous detections. These findings suggest that the proposed method can be effectively utilized as a monitoring system for the elderly in actual environments. © 2023 ieee.

关键词： Accidents

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distributed Adaptive Norm Estimation for Blind System Identification in Wireless sensor Networks 48

Distributed Adaptive Norm Estimation for Blind System Identi...

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48th ieee international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Blochberger, M. Elvander, F. Ali, R. Østergaard, J. Jensen, J. Moonen, M. Waterschoot, T.Van Leuven3001 Belgium Aalto University Dept. of Information and Communications Engineering Espoo02150 Finland Aalborg University Dept. of Electronic Systems Aalborg9220 Denmark

ISBN: (纸本)9781728163277

distributed signal-processing algorithms in (wireless) sensor networks often aim to decentralize processing tasks to reduce communication cost and computational complexity or avoid reliance on a single device (i.e., fusion center) for processing. In this contribution, we extend a distributed adaptive algorithm for blind system identification that relies on the estimation of a stacked network-wide consensus vector at each node, the computation of which requires either broadcasting or relaying of node-specific values (i.e., local vector norms) to all other nodes. The extended algorithm employs a distributed-averaging-based scheme to estimate the network-wide consensus norm value by only using the local vector norm provided by neighboring sensor nodes. We introduce an adaptive mixing factor between instantaneous and recursive estimates of these norms for adaptivity in a time-varying system. Simulation results show that the extension provides estimation results close to the optimal fully-connected-network or broadcasting case while reducing inter-node transmission significantly. © 2023 ieee.

关键词： Adaptive algorithms

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Game-Theoretic Deep Reinforcement Learning for Task Offloading in Heterogeneous MEC systems 16

Game-Theoretic Deep Reinforcement Learning for Task Offloadi...

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16th international conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Yu, Han Dou, Haie Xu, Xinmian Cui, Jingwu Wang, Lei Liu, Chen Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China

ISBN: (纸本)9798350390643

The emergence of new applications has increased the demand of mobile users for edge computing. Therefore, the optimization of base station load balancing and task offloading is particularly important. In this paper, a two-tier heterogeneous mobile edge computing (MEC) system based on Stackelberg Game (SG) is studied. The introduction of SG can improve user association flexibility while reducing base station load. The introduction of network slicing can classify users according to different types, which increases the universality of the proposed framework. To solve the above problem, we map an optimization problem of efficiency, computation delay and computation energy consumption. Due to the non-convexity of the optimization problem, the Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm based on SG is proposed in this paper. The gradient descent strategy can converge faster and obtain the optimal solution of user association policy and unload policy. Through simulation, it is verified that the proposed scheme can obtain lower computation delay and computation energy consumption on the basis of solving the problem of base station overload. © 2024 ieee.

关键词： Deep reinforcement learning

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