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

共 30781 条记录，以下是1181-1190 订阅

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A Comprehensive Study on distributed Denial of Service Attack Detection using Deep Learning Technique 1

A Comprehensive Study on Distributed Denial of Service Attac...

引用

1st international conference on Software, systems and Information Technology, SSITCON 2024

作者： Mukku, Bhavana Mlr Institute of Technology Department of Data Science and Cyber Security Hyderabad India

ISBN: (纸本)9798350352931

distributed Denial of Service (DDoS) attacks are among the most dangerous threats in cyberspace impacting areas such as the Internet of Things (IoT), Cybersecurity, and Cloud computing, which leads to network interruptions. DDoS attacks target IoT environments by manipulating IoT networks, consuming resources and interrupting services, leading to privacy challenges and performance degradation. However, accurate detection of DDoS attacks is difficult, due to their various DDoS attack types, heterogeneity of network security and complexity of communication protocols. Advanced Deep Learning (DL) approaches have been employed for effective detection of DDoS attacks while reducing false-positive rates and improving high detection rates of the different network traffic patterns. The DL algorithms such as Convolutional Neural Networks (CNN), Bidirectional Long-Short Term Memory (Bi-LSTM), Recurrent Neural Networks (RNN), and Deep Neural Networks (DNN) are used for the detection of DDoS attacks. This survey focuses on analyzing the advantages and limitations of different DL algorithms used for DDoS attack detection. The performance of the DL models is evaluated based on performance metrics such as accuracy, precision, recall, and F1-score. © 2024 ieee.

关键词： Cyber attacks

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DiMOpt: a distributed Multi-robot Trajectory Optimization Algorithm

DiMOpt: a Distributed Multi-robot Trajectory Optimization Al...

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

作者： Salvado, Joao Mansouri, Masoumeh Pecora, Federico Orebro Univ AASS Res Ctr Orebro Sweden Univ Birmingham Sch Comp Sci Birmingham W Midlands England

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

This paper deals with Multi-robot Trajectory Planning, that is, the problem of computing trajectories for multiple robots navigating in a shared space while minimizing for control energy. Approaches based on trajectory optimization can solve this problem optimally. However, such methods are hampered by complex robot dynamics and collision constraints that couple robot's decision variables. We propose a distributed multirobot optimization algorithm (DiMOpt) that addresses these issues by exploiting (1) consensus optimization strategies to tackle coupling collision constraints, and (2) a single-robot sequential convex programming method for efficiently handling non-convexities introduced by dynamics. We compare DiMOpt with a baseline centralized multi-robot sequential convex programming algorithm (SCP). We empirically demonstrate that DiMOpt scales well for large fleets of robots while computing solutions faster and with lower costs. Finally, DiMOpt is an iterative algorithm that finds feasible trajectories before converging to a locally optimal solution, and results suggest the quality of such fast initial solutions is comparable to a converged solution computed via SCP.

关键词： Couplings Trajectory planning Navigation Heuristic algorithms Programming Dynamic programming Iterative methods

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Task Offloading and Parameter Optimization of MAR in Multi-access Edge computing 23

Task Offloading and Parameter Optimization of MAR in Multi-a...

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, Yumei Zhu, Xiumin Song, Shudian Ma, Shuyue Yang, Feng Zhai, Linbo Shandong Normal University Jinan China

ISBN: (纸本)9781665494571

With the popularization of mobile devices and the development of mobile augmented reality (MAR), people's demand for MAR applications is increasing. However, few MAR applications are applied to mobile devices since it is computationally intensive and requires computing power. MAR applications consume a lot of energy, and the battery capacity of mobile devices cannot support the long-term operation of applications. In this paper, we study the problem of dynamically configuring parameters of MAR system based on different user service characteristics under the system model of multi-edge server and multi-client. Considering the influence of client parameters such as CPU frequency, client task resolution and wireless bandwidth resource allocation, we design an objective function to evaluate service delay, energy consumption per frame and detection accuracy of target tasks according to different user service characteristics. Based on our objective function, we propose a server selection and parameter optimization (SSPO) algorithm to select the appropriate mobile edge server for client task offloading and parameter optimization. Simulation results show that our proposed algorithm can effectively reduce the energy consumption of MAR client while meeting the service characteristics of different users. © 2021 ieee.

关键词： Energy consumption Wireless sensor networks Image edge detection Computational modeling Linear programming Mobile handsets Delays

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RhythmEdge: Enabling Contactless Heart Rate Estimation on the Edge 8

RhythmEdge: Enabling Contactless Heart Rate Estimation on th...

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8th ieee international conference on Smart computing (SMARTCOMP)

作者： Hasan, Zahid Dey, Emon Ramamurthy, Sreenivasan Ramasamy Roy, Nirmalya Misra, Archan Univ Maryland Baltimore Cty Ctr Real Time Distributed Sensing & Auton Cards Dept Informat Syst Mobile Pervas & Sensor Comp Lab Baltimore MD 21250 USA Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore

ISBN: (纸本)9781665481526

The primary contribution of this paper is designing and prototyping a real-time edge computing system, RhythmEdge, that is capable of detecting changes in blood volume from facial videos (Remote Photoplethysmography;rPPG), enabling cardiovascular health assessment instantly. The benefits of RhythmEdge include non-invasive measurement of cardiovascular activity, real-time system operation, inexpensive sensing components, and computing. RhythmEdge captures a short video of the skin using a camera and extracts rPPG features to estimate the Photoplethysmography (PPG) signal using a multi-task learning framework while offloading the edge computation. In addition, we intelligently apply a transfer learning approach to the multi-task learning framework to mitigate sensor heterogeneities to scale the RhythmEdge prototype to work with a range of commercially available sensing and computing devices. Besides, to further adapt the software stack for resource-constrained devices, we postulate novel pruning and quantization techniques (Quantization: FP32, FP16;Pruned-Quantized: FP32, FP16) that efficiently optimize the deep feature learning while minimizing the runtime, latency, memory, and power usage. We benchmark RhythmEdge prototype for three different cameras and edge computing platforms while evaluating it on three publicly available datasets and an in-house dataset collected under challenging environmental circumstances. Our analysis indicates that RhythmEdge performs on par with the existing contactless heart rate monitoring systems while utilizing only half of its available resources. Furthermore, we perform an ablation study with and without pruning and quantization to report the model size (87%) vs. inference time (70%) reduction. We attested the efficacy of RhythmEdge prototype with a maximum power of 8W and a memory usage of 290MB, with a minimal latency of 0.0625 seconds and a runtime of 0.64 seconds per 30 frames.

关键词： rPPG Edge computing System Prototyping

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2024 6th international conference on Next Generation Data-Driven Networks, NGDN 2024

2024 6th International Conference on Next Generation Data-Dr...

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6th international conference on Next Generation Data-Driven Networks, NGDN 2024

ISBN: (纸本)9798350388374

The proceedings contain 93 papers. The topics discussed include: a data quality quantitative evaluation of marine policy based on PMC model;text guided road marking point cloud completion;EFSNet: a threat detection method based on system event feature sequence;multi-test knowledge tracing prediction model based on learning cycle;MAGIC: memory advanced general index compression for learned indexes;microservices crowdsourcing platform for industrial control systems;IoT-based smart farming system with security powered by solar energy;MMLT: a novel multimodal lightweight convolutional transformer for fault diagnosis with imbalanced data in IoT;GPMBC-FZ: a query optimization method in distributed database;and energy and effective computing contract toward heterogeneity in terminal-edge-cloud computing network.

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PhD Forum Abstract: Diffusion-based Task Scheduling for Efficient AI-Generated Content in Edge Networks 23

PhD Forum Abstract: Diffusion-based Task Scheduling for Effi...

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23rd ACM/ieee international conference on Information Processing in sensor Networks, IPSN 2024

作者： Xu, Changfu Hong Kong Baptist University Hong Kong and BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798350362015

The Artificial Intelligence-Generated Content (AIGC) technique has gained significant popularity in creating diverse content. However, the current deployment of AIGC services is a centralized framework, thus leading to high response times. To address this issue, we propose a diffusion-based task scheduling method that considers the integration of the diffusion model, Deep Reinforcement Learning (DRL), and Mobile Edge computing (MEC) technique to improve the AIGC efficiency. This challenges efficient server selection without prior information in dynamic MEC systems. We formulate our problem as an online integer linear programming problem aiming to minimize task offloading delay. Furthermore, we propose a novel AIGC Task Scheduling (DDRL-ATS) algorithm based on Diffusion DRL (DDRL) that effectively addresses this problem. The DDRL-ATS algorithm achieves efficient AIGC tailored for heterogeneous MEC environments. Additionally, an online Adaptive Multi-server Selection and Allocation (DDRL-AMSA) algorithm based on DDRL is proposed to further enhance the AIGC efficiency. Moreover, our DDRL-AMSA algorithm achieves near-optimal solutions within approximate linear time complexity bounds. Finally, experimental results validate the effectiveness of our method by showcasing at least a reduction of 13.54% in task offloading delay compared to state-of-the-art methods. © 2024 ieee.

关键词： Deep reinforcement learning

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AirMobilize: IoT-Powered Air Quality Monitoring on the Go

AirMobilize: IoT-Powered Air Quality Monitoring on the Go

引用

2023 ieee international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Rajitha Jasmine, R. Preethi, S. Swetha, P. Priyanka, K. Valliammai, V. Information Technology Dept RMK Engineering College Chennai India

ISBN: (纸本)9798350305708

This project investigates the use of Internet of Things (IoT) technologies for the present monitoring of air pollution. We construct and use sensor nodes to collect data on various air contaminants, allowing for remote access and analysis. The system provides a cost-effective and efficient substitute for monitoring air quality, assisting in combating pollution and public health management. © 2023 ieee.

关键词： Air pollution monitoring system Cloud distributed systems GPS sensors

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A Fault-Tolerant distributed Air-to-Ground Communication Architecture for Urban Air Mobility 19

A Fault-Tolerant Distributed Air-to-Ground Communication Arc...

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19th Annual international conference on distributed computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Wanniarachchi, Shashini Thamarasie Turau, Volker Hamburg Germany

ISBN: (纸本)9798350346497

With the increasing demand for urban passenger transportation, the concept urban air mobility (UAM) has gained a lot of research interest. One idea is to employ fully autonomous air crafts. That is, using unmanned and not remotely piloted aerial vehicles as a means of mass transit. Clearly, fully autonomous air taxis constitute a safety critical, time sensitive application. Efficient information management is a prominent requirement and the infrastructure for sensing and fast, reliable communication is the key to mitigate any harm in the aforesaid approach. Nevertheless, failures in such a system are inevitable and need to be addressed. This work investigates the significant aspect of building a fault-tolerant UAM communication network. We introduce a concept of distributed ground station architecture and a protocol for role delegation to improve system availability. This assures an uninterrupted service continuation fulfilling the high standards of safety requirements in an UAM system. © 2023 ieee.

关键词： Fault tolerance

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distributed Photovoltaic Short-Term Power Prediction Based on Multi-Point Correction of Autoformer and POPtree 5

Distributed Photovoltaic Short-Term Power Prediction Based o...

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5th ieee international conference on Power, Intelligent computing and systems, ICPICS 2023

作者： Li, Yonghui Hu, Yasha Wang, Xuejin He, Jin Yunnan Power Grid Co. Ltd. Kunming China

ISBN: (纸本)9798350333442

With the continuous expansion of photovoltaic grid-connected capacity, precise prediction of photovoltaic power has become essential for ensuring the secure and stable operation of new energy power systems. This paper presents a distributed photovoltaic short-term power forecasting method, utilizing Autoformer and POPtree multi-point correction to overcome challenges associated with insufficient or inaccurate weather forecast data, thus enabling accurate predictions. The methodology begins with the MIC algorithm, which calculates feature correlation coefficients, helping to identify highly correlated features that are then fed into the Autoformer model for prediction, yielding preliminary long sequence prediction results. Next, a point-by-point prediction approach based on the tree model trains multiple lightGBM models, producing multi-point power predictions. Finally, by setting a weighted threshold α, the corresponding prediction results from different models are combined using a hybrid weighting method to derive the ultimate photovoltaic short-term power prediction outcomes. Through a case analysis of photovoltaic output data in a specific station area, the results demonstrate that the proposed model achieves high-quality short-term photovoltaic output predictions even without relying on weather forecasting data. This method proves effective in meeting the demands of accurate photovoltaic power forecasting in practical scenarios. © 2023 ieee.

关键词： Weather forecasting

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Innovative Methodology Utilizing Vibration sensor Data for Early Detection and Prediction in Wind Turbine systems

Innovative Methodology Utilizing Vibration Sensor Data for E...

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2024 international conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Singla, Muskan Gill, Kanwarpartap Singh Malhotra, Sonal Devliyal, Swati Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350375237

This research work presents an innovative approach for forecasting the state of high-speed bearings in wind turbines. The method employs raw data obtained from sensors that quantify vibrations. The procedure involves computing a Kurtosis Spectrogram for each of the 50 samples and employing a 1D-CNN to classify bearings into two categories: Medium-life Expectancy (over 15 days) or Short- life Expectancy (under 15 days). This study emphasizes the significance of early detection in predicting the Remaining- Useful Life (RUL) of machine components, which is crucial for preventing failures and optimizing maintenance decisions. The 1D-CNN, implemented using the Keras framework, exhibits impressive accuracy, reaching an overall accuracy of about 90% as measured by recall. This study highlights the potential use of this technology in alert systems, providing crucial support for technical decisions about component maintenance, ensuring the reliability and efficiency of wind turbine systems. © 2024 ieee.

关键词： Neural network models

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