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

共 3170 条记录，以下是141-150 订阅

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(POSTER) Wireless Multi-sensor Hardware Platform to Enhance Smart Lighting Solutions

(POSTER) Wireless Multi-Sensor Hardware Platform to Enhance ...

引用

international conference on distributed computing in sensor systems (dcoss)

作者： João Gomes Diogo Correia Carlos Resende Waldir Moreira Jorge Filipe Carlos Silva Antonio Sousa Fraunhofer Portugal AICOS Porto Portugal Tridonic Portugal Vila Nova de Gaia Portugal

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Smart lighting solutions bring benefits in terms of energy usage and users’ well-being. However, their use is not widespread due to high installation and maintenance costs. The current manual commissioning procedure is one of the main human and time-consuming tasks, so its automation is expected to reduce the overall installation cost. Considering this, we propose the Lite4More device, a Bluetooth Low Energy (BLE) based sensor platform that supports the commissioning procedure and is backwards compatible with Digital Addressable Lighting Interface (DALI) devices. The Lite4More device produces sensor data that can feed algorithms to automate the commissioning of lighting infrastructures, namely luminaire localisation and groups and scene creation. The hardware platform was validated in both lab and real office environments. The preliminary results show that Lite4More device sensors are suitable for feeding luminaire localisation and group creation algorithms, and the considered communication technology is viable for smart lighting solutions.

关键词： Location awareness Wireless communication Wireless sensor networks Costs Soft sensors Hardware Communications technology

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(POSTER) Enhancing Structural Health Monitoring with On-device AI

(POSTER) Enhancing Structural Health Monitoring with On-devi...

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international conference on distributed computing in sensor systems (dcoss)

作者： Junhak Lee Chungwhan Hwang Jaehyun Park Tae Rim Park Dept. Infor. and Comm. Engr. Inha University Incheon Korea Dept. Elec. and Comp. Engr. Inha University Incheon Korea Ino-on Inc. Seoul Korea

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This paper presents a service model for structural health monitoring (SHM) by leveraging on-device AI techniques. Unlike conventional SHM systems relying on server-based diagnostics, our approach incorporates recent advancements in lightweight AI platforms to enable real-time diagnostics directly on the sensor nodes. This integration enhances both real-time performance and accuracy while significantly reducing network load by minimizing the need for data transfer to centralized servers. Through the experimentation using real data collected from a pedestrian bridge, we demonstrate the feasibility and efficacy of the on-device AI approach for SHM. By seamlessly integrating on-device AI into IoT infrastructure, our approach not only offers improved real-time performance and accuracy but also showcases the potential of AI in optimizing SHM systems.

关键词： Temperature sensors Accuracy Pedestrians Computational modeling distributed databases Machine learning Computer architecture

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2023 ieee international conference on Pervasive computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

2023 IEEE International Conference on Pervasive Computing an...

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2023 ieee international conference on Pervasive computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

ISBN: (纸本)9781665453813

The proceedings contain 139 papers. The topics discussed include: context-aware outlier detection for sensor data stream processing;content-aware dynamic resource allocation framework for video analytic applications at edge;bus ridership prediction with time section, weather, and ridership trend aware multiple LSTM;emotions studied by computer scientists and psychologists — a complementary perspective;supporting winter road maintenance procedures with the use of distributed measurements based on IoT, thermodynamic models and machine learning;a tutorial on dataset creation for sensor-based human activity recognition;episodes of change: emotion change in semantic trajectories of multimodal sensor data;a perspective of telemedicine videostreaming systems for emergency care;holistic life-cycle management of cloud-to-thing compute entities;on efficacy of meta-learning for domain generalization in speech emotion recognition;and selective data offloading in edge computing for two-tier classification with local domain partitions.

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False Data Injection Attacks Against High Voltage Transmission systems

False Data Injection Attacks Against High Voltage Transmissi...

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international conference on distributed computing in sensor systems (dcoss)

作者： Panagiotis Radoglou-Grammatikis Magda Zafeiropoulou Maria Atanasova Pencho Zlatev Sofia Giannakidou Thomas Lagkas Vasileios Argyriou Evangelos K. Markakis Ioannis Moscholios Panagiotis Sarigiannidis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Innovative Energy and Information Technologies LTD (IEIT BIC-IZOT Sofia Bulgaria K3Y Ltd Sofia Bulgaria Department of Computer Science International Hellenic University Kavala Campus Greece Department of Networks and Digital Media Kingston University London Surrey UK Department of Electrical and Computer Engineering Hellenic Mediterranean University Crete Greece Department of Informatics & Telecommunications University of Peloponnese Tripolis Greece

The digitisation of the smart electrical grid provides several advantages and valuable services, such as self-monitoring, pervasive control and smart healing. However, despite the benefits of this progression, critical cybersecurity and privacy issues are raised due to the vulnerabilities of legacy Electrical Power and Energy systems (EPES) and the evolution of stealthy cyberthreats and malware. In this paper, we give emphasis to False Data Injection Attacks (FDIAs) that can affect the EPES State Estimation (SE). In particular, we investigate two FDIA categories, namely: (a) Global Positioning System (GPS) Spoofing Attacks and (b) ieee C37.118 FDIAs against an actual testbed emulating a high-voltage ieee 9-Bus transmission grid. Finally, we provide a relevant Intrusion Detection System (IDS) capable of detecting the aforementioned FDIAs. The evaluation analysis demonstrates the impact of the above FDIAs and the efficiency of the proposed IDS.

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Energy-Constrained Task Scheduling in Heterogeneous distributed systems 26

Energy-Constrained Task Scheduling in Heterogeneous Distribu...

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26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Chen, Chen Zhu, Jie Huang, Haiping Gao, Yingmeng Nanjing University of Posts & Telecommunications Nanjing210003 China Institute of Computing Technology Academy of Sciences State Key Laboratory Chinese of Computer Architecture Beijing100864 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (纸本)9798350331684

The resource-constrained task scheduling problem has been one of the popular research topics in cloud computing systems. By employing the dynamic voltage and frequency scaling (DVFS) techniques, the task scheduling can be further constrained by energy consumption. The paper investigates the DAG task scheduling considering both the resource and energy constraints in heterogeneous distributed systems. The objective is to minimize the scheduling length. An energy-constrained task scheduling framework is employed, where tasks are initially scheduled according to their upward rank values. Then two heuristics are proposed to improve the initial solution, namely, the simulated annealing local search method and the frequency adjustment method. Experiments are conducted by testing a large number of instances with multiple parameter settings, and the results show that the proposed algorithms are effective and efficient. © 2023 ieee.

关键词： Energy utilization

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Privacy-Preserving Multi-Party Keyword-Based Classification of Unstructured Text Data

Privacy-Preserving Multi-Party Keyword-Based Classification ...

引用

international conference on distributed computing in sensor systems (dcoss)

作者： Ian Pépin Furkan Alaca Farhana Zulkernine School of Computing Queen’s University Kingston Canada

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Collaborative data analytics and text classification are required in numerous application domains including health data analytics, which has strict privacy constraints. However, sharing data with other parties widens the attack surface and thus increases the risk of the data being compromised. Research on applications of secure multi-party computation to health data analytics have mostly focused on structured text or numeric data. In this work, we formulate a privacy-preserving multiparty scheme for performing keyword-based classification of unstructured text data, with a case study on medical text data. Our scheme uses arithmetic secret sharing and we implement it using the CrypTen framework. We also devise techniques that significantly reduce computation time without impacting accuracy, enhancing the practical feasibility of our approach.

关键词： Data privacy Data analysis Accuracy Text categorization Information sharing distributed databases Collaboration

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Fast distributed Stochastic Gradient Descent for Big Data Classification 23

Fast Distributed Stochastic Gradient Descent for Big Data Cl...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Xiao, Mingfei Wang, Huihui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665494571

Large-scale classification is an important task of machine learning, especially in the smart city field, which is a big data environment. In recent years, single-threaded optimization algorithms can no longer meet the needs of big data applications. When a single machine is trained, it will face the problem of insufficient memory and limited computing capacity. Therefore, distributed algorithms have been proposed to solve big data classification, and have received much attention. Particularly, distributed gradient descent (GD) is a widely-used algorithm for solving big data problem. However, distributed algorithms based on GD always converge slowly and suffer from expensive time in the training process. To alleviate this problem, we propose a novel fast distributed stochastic gradient descent algorithm for big data classification in this paper. By formulating the original optimization problem as a series of subproblems for classification in each machine. In particular, we exploit a fast stochastic method combined with the Nesterov acceleration method for sub-problem optimization in parallel to improve the convergence speed, and thus reduce the training time. The experimental results on some public and benchmark data sets show that our proposed distributed algorithm can converge faster with the competitive accuracy performance compared with distributed GD-based methods, which is suitable to big data classification. © 2021 ieee.

关键词： Training Machine learning algorithms Smart cities Big Data Benchmark testing Classification algorithms distributed algorithms

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Multi-Behavior Multi-Agent Reinforcement Learning for Informed Search via Offline Training

Multi-Behavior Multi-Agent Reinforcement Learning for Inform...

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international conference on distributed computing in sensor systems (dcoss)

作者： Songjun Huang Chuanneng Sun Ruo-Qian Wang Dario Pompili Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ USA Department of Civil and Environmental Engineering Rutgers University New Brunswick NJ USA

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In modern informed search missions, Multi-Robot systems (MRSs) are playing more and more important roles due to their flexibility in exploring environments. Reinforcement learning (RL) is now widely used as a decision-making method for MRS. However, existing RL-based and conventional model-based frameworks cannot deal with some challenges posed by the realworld environment. To address these challenges, a Multi-Behavior Multi-Agent Reinforcement Learning (MBMARL) framework via offline reinforcement learning method was developed. In this framework, each agent is deployed with multiple behavior policies to let the agent have choices on behaviors given a state. The proposed framework is compared with traditional reinforcement learning frameworks, including Multi-Agent Actor Critic (MAAC) and REINFORCE. The result shows that MBMARL outperforms others in both aspects of total reward and convergence time.

关键词： Training Decision making Reinforcement learning Multi-robot systems Internet of Things distributed computing Convergence

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Energy-efficient Data Aggregation Schemes for Low-power distributed Wireless sensor Networks using Compressed Sensing 5

Energy-efficient Data Aggregation Schemes for Low-power Dist...

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5th international conference on Control, Communication and computing, ICCC 2023

作者： Manuel, Ebin M. Pankajakshan, Vinod Indian Institute of Technology Department of Electronics and Communication Uttarakhand Roorkee247664 India College of Engineering Dept. of Electronics and Communication Kerala Trivandrum695016 India

ISBN: (纸本)9798350334128

The advancements in distributed computing and technologies such as Industry 4.0 have rapidly deployed wireless sensor networks. In many scenarios, battery-connected wireless sensors gather the surrounding information that is to be aggregated at a base station. The volume of data generated from large-scale wireless networks has significant impacts on storage, network latency, and bandwidth. This demands efficient data aggregation systems for new-generation smart networks. Compressed sensing techniques are proven to deliver efficient compression for sparse signals. This paper evaluates the extension of such compression techniques to distributed low-power wireless sensor networks for efficient data aggregation. © 2023 ieee.

关键词： Compressed sensing

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Design and Security Analysis of a distributed OS with Adaptive IP Management

Design and Security Analysis of a Distributed OS with Adapti...

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2024 international conference on Artificial Intelligence and Quantum Computation-Based sensor Applications, ICAIQSA 2024

作者： Prajapat, Pramod Chadha, Raman Sharma, Aryan Awasthi, Kartikay Chandigarh University Computer Science and Engineering Punjab India

ISBN: (纸本)9798331517953

As we can see, in the era of high-speed internet and the increasing use of technologies, the proper and safe management of IP Addresses is of paramount importance. De- centralized networks' older methods of distributing IP addresses can reveal weaknesses and produce inefficiencies. In the following paper, a new approach towards dynamic IP management which is developed to overcome these difficulties is presented. Our APK Dynamic IP Manager is equipped with a dedicated tier that controls a range of IP addresses and distributes them to nodes focusing on traffic rates as well as security precautions. This makes it more secure and ensure the network is well managed despite the increase in the size of the network. The experimental analysis and theoretical assessment of the model has been performed and the results show that the proposed model presents significant improvement over existing systems. It offers more protection against computer criminal activities and also enhances resource utilizationa. This work offers a potential implementation to Network Management and its applicability spans from complex enterprise networks to cloud computing as well as IoT devices. © 2024 ieee.

关键词： Efficiency

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