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

共 30762 条记录，以下是581-590 订阅

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Security Issues and Mitigation Mechanisms in distributed systems 3

Security Issues and Mitigation Mechanisms in Distributed Sys...

引用

3rd international conference on Advanced Research in computing, ICARC 2023

作者： Mahaliyanaarachchi, Vishmila Uva Wellassa University Department of Computer Science and Informatics Badulla Sri Lanka

ISBN: (纸本)9798350347371

In this age of digital computing, security is very essential. When building and implementing systems, security in distributed systems presents special issues that must be taken into account. There are various security mechanisms that are in use to mitigate these attacks. Also, it has been proven that it is more effective using machine learning and artificial intelligence in mitigation mechanisms rather than using other mitigation methods, and also today the world is moving forward with machine learning and deep learning concepts. This paper provides a wide survey of various distributed system attacks, and the mitigation methods proposed against them, also this paper provides an in-depth analysis of using machine learning and deep learning techniques in mitigating the issues and attacks faced by distributed systems. © 2023 ieee.

关键词： Deep learning

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Uniform and Randomly distributed Fuzzy Logic Based Clustering in Wireless sensor Networks 12

Uniform and Randomly Distributed Fuzzy Logic Based Clusterin...

引用

12th ieee international conference on Advanced computing, ICoAC 2023

作者： Sree Nidhi, S. Kumar, Kishore Sridharan, D. Anna University Department of Electronics and Communication Engineering Chennai India IIT Madras Department of Electrical Engineering Chennai India

ISBN: (纸本)9798350318210

The important aspect to consider in WSN would be extending the network lifetime. Due to the limited resources provided by sensors, there are limitations when using the battery power of sensor nodes. The limitation can be overcome by the utilization of clustering schemes, which leverage the power of sensors and can lead to a reduction in energy consumption. This paper introduces a novel approach called Uniform and Randomly distributed Fuzzy Logic (URDFL) for clustering in WSN. The Cluster Heads selection (CH) is determined by considering aspects such as battery power, node centrality and distance to BS. This approach aims to enhance the network's lifespan and minimize energy consumption. Additionally, a unique cluster formation technique based on a fuzzy logic interference system is presented. Finally, the proposed work has been implemented and compared with the existing protocol, LEACH in WSN by MATLAB simulations. © 2023 ieee.

关键词： Fuzzy logic

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Lumped Drag Model Identification and Real-Time External Force Detection for Rotary-Wing Micro Aerial Vehicles

Lumped Drag Model Identification and Real-Time External Forc...

引用

ieee international conference on Robotics and Automation (ICRA)

作者： Walti, Lucas Martinoli, Alcherio Swiss Fed Inst Technol EPFL Sch Architecture Civil & Environm Engn ENAC Distributed Intelligent Syst & Algorithms Lab DIS CH-1015 Lausanne Switzerland

ISBN: (纸本)9798350384581;9798350384574

This work focuses on understanding and identifying the drag forces applied to a rotary-wing Micro Aerial Vehicle (MAV). We propose a lumped drag model that concisely describes the aerodynamical forces the MAV is subject to, with a minimal set of parameters. We only rely on commonly available sensor information onboard a MAV, such as accelerometer data, pose estimate, and throttle commands, which makes our method generally applicable. The identification uses an offline gradient-based method on flight data collected over specially designed trajectories. The identified model allows us to predict the aerodynamical forces experienced by the aircraft due to its own motion in real-time and, therefore, will be useful to distinguish them from external perturbations, such as wind or physical contact with the environment. The results show that we are able to identify the drag coefficients of a rotary-wing MAV through onboard flight data and observe the close correlation between the motion of the MAV, the measured external forces, and the predicted drag forces.

关键词： Micro air vehicle (MAV)

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A sensor Predictive Model for Power Consumption using Machine Learning 12

A Sensor Predictive Model for Power Consumption using Machin...

引用

12th international conference on Cloud Networking (CloudNet)

作者： Moocheet, Nalveer Jaumard, Brigitte Thibault, Pierre Eleftheriadis, Lackis Concordia Univ Comp Sci & Software Engn Montreal PQ Canada Ericsson GAIA Montreal PQ Canada Ericsson Res AI Machine Reasoning & Hybrid AI Stockholm Sweden

ISBN: (纸本)9798350313062;9798350313079

Reducing the power consumption of computing devices remains a challenge for the data center industry. In 2022, it represents approximately 2% of global electricity consumption and 1% of global greenhouse gas emissions. In addition, data centers must integrate the 5G and B5G challenges into their strategies, by increasing the computing resources available to face higher-quality service constraints. Indeed, 5G and B5G future networks are increasingly software-oriented and therefore, rely heavily on cloud computing to process large amounts of data from multiple sources in real-time. Several research works on energy management have been proposed to ensure a reduction of the energy consumed by the various components of a data center (e.g., software, computing devices, or cooling systems). However, to optimize the energy consumption of computing devices (e.g., virtual machines/container operations), it is essential to have an accurate model for predicting power consumption. Thus, we propose in this study a new sensor predictive model to predict the dynamic power consumption of cloud computing devices with high accuracy. Our proposal takes advantage of the various sensors that are now embedded in physical machines, or more generally in cloud server machines, as well as Performance Monitoring Counters to implement a Machine Learning power prediction model. The performance evaluation results confirm that our power consumption prediction models outperform previous literature models in terms of accuracy. Indeed, our best model achieves a R2 score of 93.6% which is higher than the compared baseline model by 21.1%.

关键词： Power Consumption Prediction sensor Machine Learning Performance Monitoring Counter

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IoT Based Smart Waste Monitoring System Envisioning a Sustainable Smart City

IoT Based Smart Waste Monitoring System Envisioning a Sustai...

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2024 ieee international conference on computing, Applications and systems, COMPAS 2024

作者： Aurko, Najmus Saquib Masum, S.M. Hossain, Md Sajid American International University Bangladesh Computer Science and Engineering Dhaka1229 Bangladesh American International University Bangladesh Electrical and Electronic Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798331529765

This paper introduces a smart approach to waste management through the integration of smart technologies. Our proposed system employs the ESP8266 Wi-Fi module to enable remote notification, ensuring efficient waste monitoring both indoors and outdoors. The core components include an Ultrasonic sensor HC-SR04 for real-time monitoring of the waste bin's fill level, an IR sensor to facilitate automated lid opening upon user proximity, and a servo motor to control the lid mechanism. Upon detection of fullness of the waste bin, the system triggers a buzzer, providing an audible alert for the user and encouraging the user timely waste disposal. The innovation aimed for a more cost effective and economically beneficial system. The innovation extends beyond conventional waste monitoring because we are not only implementing it indoors but also outdoors to monitor waste disposal which will maintain a clean environment overall. The paper outlines the implementation of a client-server model, emphasizing its potential to foster a pollution-free society, good health, and a cleaner environment. © 2024 ieee.

关键词： Ultrasonic sensors

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Comparison of Recent Energy Efficient Protocols Used in Smart Agriculture

Comparison of Recent Energy Efficient Protocols Used in Smar...

引用

2024 international conference on Integrated Circuits, Communication, and computing systems, ICIC3S 2024

作者： Rani, Bindu Sangwan, Anju Sangwan, Anupma GJUS&T Hisar Department of CSE India

ISBN: (纸本)9798350364088

The economic growth of each country depends on the agricultural production sector. Producers' production must be in good status to yield the intended revenue. Many scientists in farming systems have conducted the most successful research, primarily identifying different problems. Internet of Things (IoT)-based intelligent agriculture systems check the climate, soil fertility evaluation, insect detection, humidity evaluation, etc. There are several frameworks for smart agriculture through Wireless sensor Networks (WSNs). But still, Numerous issues plague the current frameworks, including excessive power consumption, high computational costs, packet losses, cluster head selection, and energy- efficiency. To overcome these problems, the right routing protocol selection is necessary. In this paper, routing protocols, energy-efficient protocols, and smart agricultural algorithms are discussed. A comparative analysis is made on different parameters which helps in selection of best protocol. © 2024 ieee.

关键词： Wireless sensor networks

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FedGSync: Jointly Optimized Weak Synchronization and Gradient Transmission for Fast distributed Machine Learning in Heterogeneous WAN

FedGSync: Jointly Optimized Weak Synchronization and Gradien...

引用

2023 ieee international conference on systems, Man, and Cybernetics, SMC 2023

作者： Zhou, Huaman He, Yihong Zhang, Zhihao Luo, Long Yu, Hongfang Sun, Gang University of Electronic Science and Technology of China China

ISBN: (纸本)9798350337020

Due to privacy and cost reasons, distributed machine learning in Wide-Area Networks(DML-WAN) is becoming an emerging and popular collaborative learning paradigm. However, heterogeneity in computing power and data distribution among workers in different locations has a dramatic impact on training performance, including convergence speed and learning accuracy. Most of the existing works on distributed training mechanisms either focus on computing heterogeneity or data heterogeneity, and none of them can handle both well. In this paper, we propose FedGSync, a novel distributed training mechanism to improve the training performance for DML-WAN, where computing heterogeneity and data heterogeneity usually coexist. To speed up training and improve model accuracy, FedGSync clusters workers into groups according to the similarity of their data distribution and introduce group-based weak synchronization to minimize the synchronization delays waiting for slow workers and the accuracy loss by balancing the contributions of all data distributions. To preserve data privacy and improve efficiency, FedGSync only groups workers based on principal components of gradients and design an approximate grouping mechanism based on Kmeans. To further reduce synchronization time, FedGSync prioritizes packets and uses differential transmission for gradient packets between groups. Evaluation results demonstrate that FedGSync improves convergence speed and learning accuracy under the coexistence of computing heterogeneity and data heterogeneity compared with state-of-the-art distributed training mechanisms. © 2023 ieee.

关键词： computing Heterogeneity Data Heterogeneity distributed Machine Learning distributed Training Mechanism Wide-Area Network

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Research on distributed Database Synchronization Mechanism in Edge computing Environment

Research on Distributed Database Synchronization Mechanism i...

引用

2024 international conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Wang, Chunxin Jiang, Yuankun Liu, Yuan Zhang, Wei Wang, Zenghai Liu, Liguo Zhao, Zilin Cloud Data Technology Co. Ltd. China State Grid Information & Telecommunication Co. Ltd. Beijing Branch Beijing100031 China

ISBN: (纸本)9798331511296

Challenges like network latency, bandwidth limitations, and varied node resources are encountered by distributed databases in edge computing environments. This paper examines a distributed database synchronization mechanism for edge computing to enhance synchronization efficiency and maintain data consistency. It suggests an optimized design involving the weak consistency model and adaptive synchronization frequency adjustment. By building mathematical models and conducting performance tests, the performance of the synchronization mechanism under different network conditions was evaluated, and the advantages of the adaptive synchronization mechanism in key indicators such as synchronization delay, data consistency, and bandwidth utilization were verified. Experimental results show that this mechanism can significantly reduce synchronization delays, improve bandwidth utilization, and maintain low consistency errors under dynamic network conditions, and is especially suitable for edge computing nodes with limited resources. In addition, through practical application cases of intelligent traffic management systems, the effectiveness and application value of the optimization mechanism are further proved. The research in this article provides theoretical support and technical solutions for distributed database synchronization in edge computing scenarios, which helps to improve the data management efficiency and reliability of the system. © 2024 ieee.

关键词： Data consistency

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Efficient Centralised and Decentralised Gaussian Process Approaches for Online Tracking within Stone Soup 27

Efficient Centralised and Decentralised Gaussian Process App...

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27th international conference on Information Fusion (FUSION)

作者： Lyu, Chenyi Liu, Xingchi Wright, James Barra, Jordi Hunter, Alasdair Mihaylova, Lyudmila Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England Rutherford Appleton Lab Sci Comp Dept Sci & Technol Facil Council Didcot Oxon England Def Sci & Technol Lab Salisbury Wilts England

ISBN: (纸本)9798350371420;9781737749769

This paper explores the application of centralised and distributed Gaussian process algorithms to real-time target tracking and compares their performance. By embedding the algorithms into the Stone Soup, the focus is on the innovative implementation of Gaussian process methods with learning hyperparameters and implementation with a factorised variance of the Gaussian kernel. The performance of the methods with different kernels was evaluated, not only with the Gaussian kernel. Extensive experiments with various kernel configurations demonstrate their importance in enhancing prediction accuracy and efficiency, especially in real-time tracking. The case studies with manoeuvring targets show significant advancements in tracking capabilities, particularly in wireless sensor networks, using optimised Gaussian process methods. This work advances Stone Soup's capabilities and lays the groundwork for future investigations into adaptive Gaussian Process applications in tracking and sensor data analysis.

关键词： Learning Gaussian process methods distributed Gaussian process sensor networks covariance matrix tracking Stone Soup online tracking

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distributed Time Slot Allocation For Transmission of sensor Data in UAV Formations

Distributed Time Slot Allocation For Transmission of Sensor ...

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2023 ieee Symposium sensor Data Fusion and international conference on Multisensor Fusion and Integration, SDF-MFI 2023

作者： Samandari, Amelia Willig, Andreas University of Canterbury Department of Computer Science Christchurch New Zealand

ISBN: (纸本)9798350382587

Unmanned Aerial Vehicles (UAVs) in multi-UAV systems rely on sensor data to achieve a collective goal, and for command and control. This paper presents distributed Assignment and Resolution of Time slots (D-ART), a Time Division Multiple Access (TDMA)-based Medium Access Control (MAC) protocol that supports the dissemination of safety-critical sensor data between UAVs in a formation. D-ART allows for each UAV to self-organize their time slot allocation, and has provisions for UAV-driven changes to the superframe size as necessary. We determine the best settings and evaluate the performance of D-ART using time to convergence as a key performance measure, as time to convergence directly corresponds to the time taken to generate the smallest possible TDMA schedule for a formation of unknown size. © 2023 ieee.

关键词： Time division multiple access

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