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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1541-1550 订阅

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Enhancement of Labour's Health by Solar and AI based Sander Machine

Enhancement of Labour's Health by Solar and AI based Sander ...

引用

2023 international conference on Sustainable computing and Smart systems, ICSCSS 2023

作者： Elango, M.K. Girishankaran, D. Hariprasath, V. Sanjay, M. K.S. Rangasamy College of Technology Department of Electrical and Electronics Engineering Namakkal India

ISBN: (纸本)9798350333602

The use of sanding machines in construction and painting processes has become increasingly prevalent. These machines use abrasive belts, discs, or sheets to smooth the surface of wood, plastic, or composites. The sander machine is designed with wheels for easy movement and can be powered by solar energy generated from a solar panel, reducing electricity usage, especially during winter and overcast weather. However, the use of sanding machines also leads to the release of dust, which can impact the environment, climate control, aviation, and human health. To mitigate these effects, it is essential to monitor dust levels and trigger an automatic switch to prevent accidents and malfunctions. To further reduce the amount of dust produced, the sander machine also includes a vacuum that helps to clean and remove dust particles from the surface. © 2023 ieee.

关键词： sensor networks

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distributed Learning for Application Placement at the Edge Minimizing Active Nodes 2

Distributed Learning for Application Placement at the Edge M...

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2nd international conference on 6G Networking, 6GNet 2023

作者： Torres-Pérez, Claudia Coronado, Estefanía Cervelló-Pastor, Cristina Camargo, Juan Sebastian Siddiqui, Muhammad Shuaib i2CAT Foundation Barcelona Spain Universidad de Castilla-La Mancha Albacete Spain Universitat Politécnica de Catalunya Barcelona Castelldefels Spain

ISBN: (纸本)9798350306736

The main goal of application placement in Multi-Access Edge computing (MEC) is to map their requirements to the infrastructure for desired Service Level Agreement (SLA). In highly distributed infrastructures in beyond 5G and 6G networks, meeting this need and minimizing energy use are crucial. Fo-cusing solely on meeting SLA requirements can lead to resource fragmentation and reduced energy efficiency, as nodes utilize only a small portion of their resources. Furthermore, when multiple orchestrators govern MEC nodes, achieving optimal efficiency becomes a more complex challenge. This paper addresses the application placement problem by employing distributed deep reinforcement learning to efficiently minimize the overall cost of active MEC nodes in a distributed scenario involving multiple MEC systems. Our technique reduces the number of active nodes maintaining an average accuracy of up to 98%, meets SLA requirements, and is scalable for hosting several MEC nodes. © 2023 ieee.

关键词： Edge computing

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distributed Game-Theoretical Task Offloading for Mobile Edge computing 18

Distributed Game-Theoretical Task Offloading for Mobile Edge...

引用

18th ieee international conference on Mobile Ad hoc and Smart systems (ieee MASS)

作者： Wang, En Dong, Pengmin Xu, Yuanbo Li, Dawei Wang, Liang Yang, Yongjian Jilin Univ Changchun Peoples R China Montclair State Univ Montclair NJ USA Northwestern Polytech Univ Xian Peoples R China

ISBN: (纸本)9781665449359

Mobile Edge computing (MEC) has been envisioned as a promising distributed computing paradigm, where mobile users offload their tasks to edge nodes to decrease the cost of energy and computation. However, most existing works only consider the congestion of wireless channels as the crucial factor influencing the strategy-making process, and ignore the impact of the offloading among edge nodes. In addition, centralized task offloading strategies result in heavy computation complexity in center nodes. Along this line, we take both the congestion of wireless channels and the offloading among multiple edge nodes into consideration to enrich users' offloading strategies. To this end, we first formulate the offloading problem as a multi-user potential game, and then propose a distributed task offloading algorithm to reach an equilibrium state which can also protect individual privacy. Specifically, in the above task offloading algorithm, we propose two subalgorithms to select users for updating strategies: Parallel User Selection Algorithm (PUS) and Single User Selection Algorithm (SUS) in order to substantially accelerate the convergence. Extensive experiments on three real-world data sets validate that the proposed algorithm achieves a Nash equilibrium and effectively decreases the total user cost which is acceptable compared to the optimal solution.

关键词： Mobile Edge computing computation offloading potential game Nash equilibrium

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An Efficient Edge Based Resource Scaling Method in Next-Generation Networks in Inntegration With distributed NN 1

An Efficient Edge Based Resource Scaling Method in Next-Gene...

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1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart systems, ISTEMS 2024

作者： Malathi, H. Malviya, Ashwini Umrao, Sandhya Department of Biotechnology and Genetics Karnataka Bangalore India Atlas Skill Tech University Department of Ugdx Maharashtra Mumbai India Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350384246

Among the key concepts for 5G+ networks is 'network slicing,' in which operators partition specific areas of physical resources from the connectivity edge to data centers. The operators are accountable for the complex management of resource distribution among clients, who are subject to predetermined service-level agreements (SLAs). Deep neural networks, also called DNN s, are recommended in recent research to monitor tenant demand and effectively scale resources. But there is a major problem with edge resources, like Radio Access Networks (RAN), which makes it difficult for operators to scale those assets quickly-often in milliseconds-and makes it impractical to send large amounts of data to the cloud for DNN-based computations. We suggest a distributed-DNN architecture designed for a certain class of issues in order to overcome these difficulties. A portion of the DNN tiers at the network edge serve as a quick, independent resource allocator in this design. In addition, a Bayesian method that intelligently assigns a portion of the more complicated judgements to other DNN layers running remotely over the cloud complements this. Our method seeks to achieve a compromise between the computing demands of handling massive amounts of data in a cloud-based setting and the need for quick decision-making at the edge. We investigate the combined training and deployment of this distributed-DNN using the publically accessible Milano dataset, showing its effectiveness in handling a maximum of sixty percent of allocation choices locally. Crucially, the overall allocations cost is either little affected or not affected at all by this local decision-making. © 2024 ieee.

关键词： 5G mobile communication systems

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sensor Automation for Industrial Applications and Prediction of Energy Level for Home Appliances Using Machine Learning Algorithm 9

Sensor Automation for Industrial Applications and Prediction...

引用

9th international conference on Advanced computing and Communication systems, ICACCS 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Selvakumari Jeya, I Jasmine Niranjani, V. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

In recent years wireless sensor networks have helped with automation in many industry domains. Wireless technology cuts cable costs, deployment time and information to transfer and process from source to destination. To make good intelligent and highly automated systems, sensors are crucial. They make it possible to detect, analyze, measure, and process a variety of alterations that occur in industrial equipment's location, length, height, appearance, displacement, etc. This paper described the industrial standardization of different WSN protocols like Zig-Bee, Wireless HART, UWB, IETF 6LowPAN, ISA100, Bluetooth and Wi-Fi. The work represented a few applications of WSNs in industry and its implementation challenges like safety, security, and availability. The work collected the energy consumption data of different home appliances. We applied the LightGBM machine learning algorithm to find a better energy consumption and prediction result. © 2023 ieee.

关键词： Energy utilization

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distributed Monitoring and Control System of Gas Concentrations in Different Environment 24

Distributed Monitoring and Control System of Gas Concentrati...

引用

24th international conference on Control systems and Computer Science, CSCS 2023

作者： Furdu, Iulian Pruteanu, Eusebiu Nitu, Costica University Vasile Alecsandri of Bacau Department of Mathematics and Informatics Bacau Romania University Vasile Alecsandri of Bacau Department of Energy and Computer Science Bacau Romania Polytechnic University Department of Intelligent Robotics Bucharest Romania

ISBN: (纸本)9798350313390

Wireless sensor systems (WSN) is a broad, exciting area with new perspectives and growing growth over the past decades, where more research is being done. WSNs contain many (hundreds of thousands) of micro-sized, cheap chips, powered by low-cost wireless interconnected batteries. These chips are called nodes, which could be of several types, including acoustic, radar, low-frequency magnetic, thermal, and visual sampling frequencies. Many applications are based nowadays on WSN, such as environmental control, smart cities, wildlife monitoring, vehicles and infrastructure, natural disasters, home security, underwater investigations, military, airplane surveillance and drone body sensors. One of the aims of the paper is to develop a smart architecture based on intelligent microprocessor sensors, WSN802GCA/GPA and the software needed to operate with a TINI control unit. TINI allows this system to be connected to Ethernet via TCP/IP © 2023 ieee.

关键词： Wireless sensor networks

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A Dual Adaptive Factorization Network for CTR Prediction 23

A Dual Adaptive Factorization Network for CTR Prediction

引用

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： Li, Xianzhuang Wang, Zhen Wu, Xuesong Yuan, Bo Wang, Xueqian Shenzhen International Graduate School Tsinghua University Shenzhen China School of Computer Science The University of Sydney Sydney Australia Xiaomi Internet Business Department Beijing China

ISBN: (纸本)9781665494571

Factorization-based methods, which can model cross features automatically and generalize to unseen features, have been the benchmark models for the click-through rate (CTR) prediction. In general, they enumerate all cross features with a pre-determined order and then filter out useless interactions through model training. However, two major challenges remain. First, they need to make a trade-off between the computational cost and the expression ability of high-order interactions;Second, enumerating all feature interactions may introduce harmful noise. Inspired by the success of Adaptive Factorization Network (AFN), which aims to learn arbitrary-order feature interactions adaptively, we propose a novel model called Dual Adaptive Factorization Network (DAFN) with the following benefits. First of all, DAFN can learn arbitrary-order cross features at the element-wise and vector-wise levels simultaneously. More importantly, it leverages the information about the original embedding vectors thoroughly. Furthermore, it strategically integrates multiple components, including Talking-heads Attention, Logarithmic Transformation Layer and Residual Networks, into a unified end-to-end model. Extensive experiments on four real-world datasets demonstrate that our DAFN model outperforms several start-of-the-art methods. © 2021 ieee.

关键词： Training Deep learning Adaptation models Adaptive systems Smart cities Computational modeling Discrete Fourier transforms

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Robustifying cooperative awareness in autonomous vehicles through local information diffusion 20

Robustifying cooperative awareness in autonomous vehicles th...

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20th ieee international conference on Industrial Informatics (INDIN)

作者： Piperigkos, Nikos Lalos, Aris S. Berberidis, Kostas Univ Patras Comp Engn & Informat Dept Patras Greece Athena Res Ctr Ind Syst Inst Patras Sci Pk Patras Greece

ISBN: (数字)9781728175683

ISBN: (纸本)9781728175683

Cooperative Intelligent Transportation systems envision the integration of cooperative intelligence as a key operational part of autonomous driving. In this way, a fleet or swarm of Connected and Automated Vehicles collectively coordinates its driving actions in order to maximize its performance. To realize this ambition, vehicles need to be fully location-aware of their surrounding environment, through distributed AI intelligence. Motivated by this requirement, we develop in this paper a distributed cooperative awareness scheme which performs multimodal fusion of heterogeneous sensor sources along with V2V communication information, using graph Laplacian matrix and Least-Mean-Squares algorithm. The intuition behind our approach is that neighboring vehicles are interested in estimating common positions of other vehicles. We build upon our previous work on global awareness though local information diffusion, and prove that the proposed distributed framework is able to address highly efficient the case of lacking any information about other networked vehicles. More specifically, our approach achieves high enough convergence speed as well as location accuracy. The evaluation study has been performed in CARLA autonomous driving simulator and verifies the proposed method's benefits over other related solutions.

关键词： Localization Cooperative Awareness Least-Mean-Squares Information diffusion sensor fusion

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SMA-NBO: A Sequential Multi-Agent Planning with Nominal Belief-State Optimization in Target Tracking

SMA-NBO: A Sequential Multi-Agent Planning with Nominal Beli...

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ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Li, Tianqi Krakow, Lucas W. Gopalswamy, Swaminathan Texas A&M Univ Dept Mech Engn College Stn TX 77840 USA Texas A&M Univ BCDC Bryan TX 77807 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

In target tracking with mobile multi-sensor systems, sensor deployment impacts the observation capabilities and the resulting state estimation quality. Based on a partially observable Markov decision process (POMDP) formulation comprised of the observable sensor dynamics, unobservable target states, and accompanying observation laws, we present a distributed information-driven solution approach to the multi-agent target tracking problem, namely, sequential multi-agent nominal belief-state optimization (SMA-NBO). SMA-NBO seeks to minimize the expected tracking error via receding horizon control including a heuristic expected cost-to-go (HECTG). SMA-NBO incorporates a computationally efficient approximation of the target belief-state over the horizon. The agent-byagent decision-making is capable of leveraging on-board (edge) compute for selecting (sub-optimal) target-tracking maneuvers exhibiting non-myopic cooperative fleet behavior. The optimization problem explicitly incorporates semantic information defining target occlusions from a world model. To illustrate the efficacy of our approach, a random occlusion forest environment is simulated. SMA-NBO is compared to other baseline approaches. The simulation results show SMA-NBO 1) maintains tracking performance and reduces the computational cost by replacing the calculation of the expected target trajectory with a single sample trajectory based on maximum a posteriori estimation;2) generates cooperative fleet decision by sequentially optimizing single-agent policy with efficient usage of other agents' policy of intent;3) aptly incorporates the multiple weighted trace penalty (MWTP) HECTG, which improves tracking performance with a computationally efficient heuristic.

关键词： Reactive and sensor-based planning cooperating robots policy of intent random occlusion forest heterogeneous fleet

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Machine Learning Technique to Monitor Heartbeat using Amalgamated Data of Multi-sensor Stream 3

Machine Learning Technique to Monitor Heartbeat using Amalga...

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3rd international conference on Communication, computing and Digital systems, C-CODE 2023

作者： Hayat, Umar Iqbal, Zahid Mumtaz, Anam Abbas, Sara Bahria University Islamabad Pakistan Air University Islamabad Pakistan

ISBN: (纸本)9798350332391

The use of wearable devices in healthcare has become increasingly prevalent, with a focus on monitoring chronic diseases such as heart rate. Researchers have employed various methods, including machine learning, to analyze and predict personal activities. This study aims to predict abnormal behavior patterns within a FitBit dataset to anticipate potential diseases. The methodology involves using classification models such as Long Short-Term Memory (LSTM), Deep Neural network (DNN), and Support Vector Memory (SVM), as well as feature selection techniques such as Recursive Feature Elimination (RFE) and Random Forest (RF). The proposed system achieved a high accuracy rate of 85% using Random Forest. This research can aid in predicting and preventing future diseases, as well as providing behavior advice to help individuals achieve their fitness goals. © 2023 ieee.

关键词： Support vector machines

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