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检索条件"任意字段=6th IEEE International Conference on Distributed Computing in Sensor Systems"

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High-Level Metrics for Service Level Objective-aware Autoscaling in Polaris: a Performance Evaluation 6

High-Level Metrics for Service Level Objective-aware Autosca...

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6th ieee international conference on Fog and Edge computing (ICFEC)

作者： Bartelucci, Nicolo Bellavista, Paolo Pusztai, thomas Morichetta, Andrea Dustdar, Schahram Univ Bologna Dept Comp Sci & Engn Bologna Italy Vienna Univ Technol Distributed Syst Grp Vienna Austria

ISBN: (纸本)9781665495240

With the increasing complexity, requirements, and variability of cloud services, it is not always easy to find the right static/dynamic thresholds for the optimal configuration of low-level metrics for autoscaling resource management decisions. A Service Level Objective (SLO) is a high-level commitment to maintaining a specific state of a service in a given period, within a Service Level Agreement (SLA): the goal is to respect a given metric, like uptime or response time within given time or accuracy constraints. In this paper, we show the advantages and present the progress of an original SLO-aware autoscaler for the Polaris framework. In addition, the paper contributes to the literature in the field by proposing novel experimental results comparing the Polaris autoscaling performance, based on high-level latency SLO, and the performance of a low-level average CPU-based SLO, implemented by the Kubernetes Horizontal Pod Autoscaler.

关键词： Cloud Edge computing Autoscaling Polaris Kubernetes Performance Evaluation Horizontal Pod Autoscaler Elasticity High-level SLO Horizontal Pod Autoscaler

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distributed data-driven engine framework facing CPS brain-inspired intelligent transportation 23

Distributed data-driven engine framework facing CPS brain-in...

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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： Yang, Zhengzhuang Xu, Mingyue Zhao, Kaidi Yang, Chao Zhi, Huiyu Zhang, Xihou Chen, Zhuoran Qiu, Song Han, Dingding School of Information Science and Technology Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665494571

Single data source, isolated data islands and low information utilization in intelligent transportation lead to the poor inversion of the cyber space to the physical space including Incomplete data integration, high interdependency of systems and poor anti-control ability. In this paper the data-driven method is adopted to optimize the W parameter in Webster method to reduce the waiting time of vehicles to realize the traffic congestion. In particular, a multi-source heterogeneous data processing method and a unified modeling of the multi-dimensional and multi-layer networks have been introduced. By data-driven engine, we have implemented both massive storage and fast processing system. We have also build a service system which can rapidly realize data integration and services delivery as well as be directly called by researchers from the database through network requests to reduce the data reprocessing. this paper also produces the data interaction process of the data engine framework in traffic management and control, and proposes a solution for the use of massive city data. © 2021 ieee.

关键词： Smart cities Soft sensors Transportation Data integration Process control distributed databases Data models

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distributed Photovoltaic Daily Power Generation Forecast Method Based on Model Selection and Statistical Scale-up 6

Distributed Photovoltaic Daily Power Generation Forecast Met...

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6th international conference on Electronics Technology, ICET 2023

作者： Wang, Biyuan Deng, Xingye Chen, Taowei Zheng, Shaolong Li, Danyun Huizhou Power Supply Bureau Guangdong Power Grid Co. Guangdong China China University of Geosciences School of Automation Wuhan China

ISBN: (纸本)9798350337693

there are certain difficulties in the data acquisition and management of distributed photovoltaic (PV) systems, resulting in a limited quantity and quality of data. Significant challenges were posed to the forecasting of distributed PV output by these issues. Given the shortage of historical data of distributed PV power stations, a distributed PV daily generation forecasting method based on model selection and statistical scale-up is proposed in this paper. Initially, the entire region is divided into smaller regions based on the geographical location information of distributed PV power stations. Subsequently, the PV power station with high-quality data in the region is selected as the reference power station, and other distributed PV power stations in the region are regarded as a virtual PV power station. the neural network model is then invoked by model selection algorithm to precisely predict the power generation of the reference power station, and the daily power generation of the virtual power station is calculated by the relationship between the power generation and installed capacity of the reference and virtual power stations. Finally, the predicted values of all regions are summed up to obtain the daily energy generation of the entire region. By taking the distributed PV power station in Huizhou, Guangdong as an example, the prediction accuracy of this area reaches 86.057%, thus verifying the effectiveness of the proposed method. © 2023 ieee.

关键词： Virtual Power Plants

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Efficiently Managing Large-Scale Keys in HDFS 23

Efficiently Managing Large-Scale Keys in HDFS

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作者： Jin, Wei Geng, Kui Yu, Mingjie Guo, Yunchuan Li, Fenghua Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Cyber Security University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781665494571

To transparently encrypt/decrypt data stored in Hadoop distributed File System (HDFS), the Key Management Server (KMS) has been designed to automatically assign encryption zone keys (EZK) to encryption zones. However, the current key-management scheme suffers from performance bottleneck. In this paper, dividing key files for storing key data into metadata file and material file, we propose a memory-disk coordinated key management architecture for Hadoop EZK to manage massive keys, where a tree structure is designed to index key metadata stored in memory, and the inverted index structure is used to record materials stored on hard disk. Further balance-tree-based management algorithms without Java serialization is designed to efficiently query keys (including metadata and materials). A series of theoretical analysis are conducted to show the upper and lower boundary of metadata query. Finally, we implement the above scheme in the Hadoop KMS without modifying the original key-management interfaces. A series of experiments are carried out on the simulated data. Experimental results show that, our scheme can efficiently manage ten billion keys and perform much better than the existing KMS in Hadoop. © 2021 ieee.

关键词： Java Smart cities File systems Memory management distributed databases Metadata Hard disks

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Improving Inertial sensor-based Human Activity Recognition using Ensemble Deep Learning 8

Improving Inertial Sensor-based Human Activity Recognition u...

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Joint 8th international conference on Digital Arts, Media and Technology and 6th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Rojanavasu, Pornthep Jantawong, Ponnipa Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350396546

sensor-based human activity recognition (S-HAR) is a famous study focusing on detecting human physiological actions by interpreting various sensors, especially one-dimensional time series information. Typically, S-HAR machine learning methods were developed using handcrafted characteristics. Unfortunately, this is a complicated process that involves feature engineering and a high level of domain knowledge. Due to the development of deep neural networks, classification techniques could efficiently handle relevant characteristics from raw sensor data, resulting in enhanced classification outcomes. In this study, we describe a unique method for S-HAR based on ensemble deep learning with sensor nodes connected to the waist, chest, leg, and arm. Implementing and training three deep learning networks is performed using a publically available dataset, including wearable sensors from eight human actions. the findings demonstrate that the proposed Ens-ResNeXt model provides the maximum accuracy and F1-score, which is superior to existing techniques. © 2023 ieee.

关键词： Learning systems

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Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded systems with GPUs 17

Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded System...

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7th ieee international conference on Robotic computing (IRC)

作者： Eisoldt, Marc Mock, Alexander Porrmann, Mario Wiemann, thomas Osnabruck Univ Comp Engn Grp Osnabruck Germany Fulda Univ Appl Sci Robot Comp Sci Fulda Germany DFKI Nieder sachsen Plan Based Robot Control Osnabruck Germany

ISBN: (纸本)9798350395747

Monte Carlo Localization is a widely used approach in the field of mobile robotics. While this problem has been well studied in the 2D case, global localization in 3D maps with six degrees of freedom has so far been too computationally demanding. Hence, no mobile robot system has yet been presented in the literature that is able to solve it in real-time. the computationally most intensive step is the evaluation of the sensor model, but it also offers high parallelization potential. this work investigates the massive parallelization of the evaluation of particles in truncated signed distance fields for three-dimensional laser scanners on embedded GPUs. the implementation on the GPU is 30 times as fast and more than 50 times more energy efficient compared to a CPU implementation.

关键词： Degrees of freedom (mechanics)

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6th international conference on Soft computing and Signal Processing, ICSCSP 2023

6th International Conference on Soft Computing and Signal Pr...

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6th international conference on Soft computing and Signal Processing, ICSCSP 2023

ISBN: (纸本)9789819986279

the proceedings contain 54 papers. the special focus in this conference is on Soft computing and Signal Processing. the topics include: Stock Price Prediction Using LSTM, CNN and ANN;IoT-Based Smart Wearable Devices Using Very Large Scale Integration (VLSI) Technology;plant Disease Detection and Classification Using Artificial Intelligence Approach;camera and Voice Control-Based Human–Computer Interaction Using Machine Learning;optimal Crop Recommendation by Soil Extraction and Classification Techniques Using Machine Learning;ioT-Based Smart Irrigation System in Aquaponics Using Ensemble Machine Learning;Performance Evaluation of MFSK Techniques Under Various Fading Environments in Wireless Communication;prediction of Breast Cancer Using Feature Extraction-Based Methods;review and Design of Integrated Dashboard Model for Performance Measurements;anomaly Detection in Classroom Using Convolutional Neural Networks;Efficient VLSI Architectures of Multimode 2D FIR Filter Bank using distributed Arithmetic Methodology;implementation of an Efficient Image Inpainting Algorithm using Optimization Techniques;A Systematic Study and Detailed Performance Assessment of SDN Controllers Across a Wide Range of Network Architectures;Classification and Localization of Objects Using Faster RCNN;an Image Processing Approach for Weed Detection Using Deep Convolutional Neural Network;detection of Leaf Black Sigatoka Disease in Enset Using Convolutional Neural Network;detecting Communities Using Network Embedding and Graph Clustering Approach;Deep Learning Approaches-Based Brain Tumor Detection Using MRI Images—A Comprehensive Review;Predicting Crop Yield with AI—A Comparative Study of DL and ML Approaches;An Empirical Analysis of Lung Cancer Detection and Classification Using CT Images;trust and Secured Routing in Mobile Ad Hoc Network Using Block Chain.

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State Management Against Two-message Attacks in Hash-Based Post Quantum Signatures for Large IoT sensor Networks Using Blockchain 6

State Management Against Two-message Attacks in Hash-Based P...

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6th international conference on Applied Computational Intelligence in Information systems, ACIIS 2023

作者： Kok Seng, Vincent Lew thien Wan, Au Shah Newaz, S.H. Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri Begawan Brunei

ISBN: (纸本)9798350317428

With the rise of quantum computers, quantum-safe digital signatures have been invented to ensure the security and verifiability of communication. Hash-based one-time signatures are one of the candidates to ensure unforgeability against the improved processing power. this signature algorithm requires tracking of secret keys to ensure one-time usage, called key state. In an Internet of things(IoT) environment, failure to maintain key state due to network failures, hardware/software failures, implementation flaws, and improper access is possible. Consequences of failure to manage key state results in the degradation of the system's security, opening up opportunities for attackers to forge signatures. Solutions to this however are limited due to the scale and resource constraints of IoT devices. Hence, this paper proposes a solution named, Blockchain-based Key State Management to ensure the key state is maintained without affecting resources and scalability heavily. the solution utilizes blockchain and smart contracts to maintain key states by leveraging blockchain protocols to ensure key states remain updated and un-tampered. the paper also contains a qualitative survey comparing other alternative solutions for preventing key reuse to highlight the importance of the proposed solution. © 2023 ieee.

关键词： Blockchain

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Design of a 45 nm Complementary Metal Oxide Semiconductor Low Noise Amplifier for a 30 GHz Millimeter-Wave Wireless Transceiver in Radar sensor Applications 6

Design of a 45 nm Complementary Metal Oxide Semiconductor Lo...

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6th international conference on Artificial Intelligence, Big Data, computing and Data Communication systems, icABCD 2023

作者： Chakoma, Shingirirai M. Ogudo, Kingsley A. University of Johannesburg Faculty of Engineering and The built Environment Department of Electrical & Electronics Engineering Johannesburg South Africa

ISBN: (纸本)9798350314809

the millimeter-wave (mmWave) frequency band is rapidly becoming utilized in wireless technologies due to its large bandwidth and high data throughput. Wireless technology is increasingly becoming the backbone of the Internet of things (IoT). this has resulted in increased applications of the radio frequency (RF) spectrum and congestion of the microwave band. this can be solved by utilizing more bandwidth at higher frequency bands. One notable application of IoT pertains to radar sensing, which has experienced increased popularity across various domains such as autonomous vehicles, gesture recognition, drones, and health monitoring. Radar sensors have been employed in these applications to perform tasks including proximity sensing, direction detection, speed measurement, target localization, and capturing physiological indicators such as heartbeat and breathing. Several factors have an impact on the performance of radar sensors, encompassing the maximum range for target detection, measurement precision, capability to differentiate between multiple targets, and ability to operate effectively in environments with high levels of noise. this paper presents the design of a 45 nm complementary metal-oxide-semiconductor (CMOS) low noise amplifier (LNA) for a mmWave Ka-band wireless transceiver for radar sensors. the LNA was designed to operate at 0.6V and 700 μA for low power consumption. the LNA consists of an inductive degenerated common source (CS) and a common gate (CG) diode-connected load. the LNA achieves a power gain of 31.19 dB and a noise figure (NF) of 0.133 dB at 30 GHz consuming 0.42 mW of power. © 2023 ieee.

关键词： Millimeter waves

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A Survey on Byzantine Attack using Secure Cooperative Spectrum Sensing in Cognitive Radio sensor Network 6

A Survey on Byzantine Attack using Secure Cooperative Spectr...

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6th international conference on computing Methodologies and Communication, ICCMC 2022

作者： Patil, Siddarama R Rajashree, Rajashree Agarkhed, Jayashree P.D.A College of Engineering Department of Electronics and Communication Engineering Karnataka Kalaburagi585102 India P.D.A College of Engineering Department of Computer Science and Engineering Karnataka Kalaburagi585102 India

ISBN: (纸本)9781665410281

the strategy of permanently allocating a frequency band in a wireless communication network to one application has led to exceptionally low utilization of the vacant spectrum. By utilizing the unused licensed spectrum along with the unlicensed spectrum, Cognitive Radio sensor Network (CRSNs) ensures the efficiency of spectrum management. To utilize the spectrum dynamically it is important to safeguard the spectrum sensing. Cooperative Spectrum Sensing (CSS) is recommended for this task. CSS aims to provide reliable spectrum sensing. However, there are various vulnerabilities experienced in CSS which can influence the performance of the network. In this work, the focus is on the Byzantine attack in CSS and current security solutions available to avoid the Byzantines in CRSN. © 2022 ieee.

关键词： Cognitive radio

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