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共 2165 条记录，以下是21-30 订阅

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Dual Quadrature Phase Reflection Modulation for RIS-Assisted Integrated Sensing and Communication 14

Dual Quadrature Phase Reflection Modulation for RIS-Assisted...

引用

14th IEEE International conference on Wireless Communications and Signal processing (WCSP)

作者： Gao, Mei Cheng, Wenchi Zhang, Wei Xie, Wei Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Natl Univ Def Technol Sch Informat & Commun Wuhan Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia

ISBN: (纸本)9781665450850

The development of digital society generates mass data coming from a huge number of sensors, arising the demand for high spectrum efficiency and energy efficiency related technologies. With passive reflection, the integrated sensing and communication (ISAC) can modulate the sensor information into the communication information and decode them in a cooperative manner at the integrated receiver, offering the potential for spectrum efficiency and energy efficiency enhancement. However, the power of sensor information is much lower than that of communication information, resulting in the bit rate downgrading or bit error rate (BER) increasing. In this paper, we develop the dual quadrature phase reflection modulation (DQP-RM) scheme along with its transmission framework to not only increase the sensor information symbol rate, but also decrease the BER of ISAC. Simulation results verify that DQP-RM theme can decrease the BER of sensor information compared with existing schemes and keep the BER of communication information unchanged while making the symbol rate of sensor information reach that of communication information.

关键词： Integrated sensing and communication reconfigurable intelligent surface reflection modulation quadrature phases alternating optimization

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Design and Implementation of IoT-Enabled intelligent Fire Detection System Using Neural networks 12th

Design and Implementation of IoT-Enabled Intelligent Fire De...

引用

12th International conference on AI and Mobile Services, AIMS 2023

作者： Almohammedi, Akram A. Balfaqih, Mohammed Nahas, Sohaib Bokhari, Abdullah Alqudsi, Abdulaziz Electrical and Electronics Department Karabük University Karabük Turkey Department of Computer and Network Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9783031451393

Fire detection systems are considered an integral part of any building. However, most fire detection systems use a single passive sensor that usually faces some unavoidable problems, especially with the use of simple processing systems using threshold and trend algorithms. Although more than a single sensor and fire information are used in some existing systems, the real-time fire information and firefighting forecasting are not monitored. Such information facilitates good decision making in firefighting and rescue operations. This paper develops a fast and smart fire detection and monitoring system that can detect and monitor fire incidents with low probability of detection error. The system involves IoT sensors that detect all necessary fire information including heating release rate smoke level, and CO2 level. Moreover, a fire detection and monitoring model based on artificial neural networks is developed to identify fire information in real-time. The proposed system was tested in a chamber box with around 20 experiments. The positive fire detection rate was high with fast fire detection rate. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fires

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Empowering Cross-Device Data Security Verification for IoT sensor Nodes 7

Empowering Cross-Device Data Security Verification for IoT S...

引用

7th International conference on Machine Learning and Natural Language processing, MLNLP 2024

作者： Shi, Xin He, Sheng Wang, Ting Zhang, Chong Wang, Yang Zhang, Xiao School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China School of Information Engineering Mianyang Teachers College Mianyang China

ISBN: (纸本)9798350354973

In the process of realizing the Internet of Everything through the Internet of Things (IoT), the massive deployment of sensor nodes poses significant security challenges. This paper introduces a novel approach to anomaly detection. The proposed method focuses on generating secure replicas of sensor data, which are compared with real-time data to identify anomalies. This process ensures robust detection across multiple devices and terminals, a key challenge in the realization of the Internet of Everything. The model combines Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAE), leveraging their strengths in capturing temporal dependencies and learning latent feature representations from sensor data. To enhance detection accuracy in complex environments, an adaptive threshold algorithm is employed, dynamically adjusting thresholds based on real-time data's statistical properties. A crossattention mechanism is also integrated, allowing the model to effectively capture spatio-temporal relationships between sensors. Additionally, we employed a weighted smoothing algorithm to reduce noise while preserving data integrity, ensuring accurate anomaly detection even in noisy environments. Finally, we evaluated our method using the real-world dataset of the Belgian railway bridge KW51. The results indicate that our system can generate sensor data replicas with 99.02% accuracy and detect abnormal data with 99.8% precision. Based on these findings, we believe that this novel approach can effectively perform anomaly detection and provide robust support for IoT security across multiple terminals and devices. © 2024 IEEE.

关键词： sensor nodes

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A Hybrid, Distributed Condition Monitoring System using MEMS Microphones, Artificial Neural networks, and Cloud Computing 16

A Hybrid, Distributed Condition Monitoring System using MEMS...

引用

16th CIRP conference on intelligent Computation in Manufacturing Engineering, CIRP ICME 2022

作者： Dorka, Frithjof Lucke, Dominik Richards, Grant P. Hochschule Reutlingen ESB Business School Alteburgstraße 150 Reutlingen72762 Germany Purdue University 401 N. Grant Street West LafayetteIN47907 United States Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstraße 12 Stuttgart70569 Germany

Condition monitoring supported with artificial intelligence, cloud computing, and industrial internet of things (IIoT) technologies increases the feasibility of predictive maintenance. However, the cost of traditional sensors, data acquisition systems, and the required information technology expert-knowledge challenge the industry. This paper presents a hybrid condition monitoring system (CMS) architecture consisting of a distributed, low-cost IIoT-sensor solution. The CMS uses micro-electro-mechanical system (MEMS) microphones for data acquisition, edge computing for signal preprocessing, and cloud computing, including artificial neural networks (ANN) for higher-level information processing. The system's feasibility is validated using a testbed for reciprocating linear-motion axes. © 2023 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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Action recognition of skiers based on inertial sensors 7

Action recognition of skiers based on inertial sensors

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7th International conference on intelligent Computing and Signal processing, ICSP 2022

作者： Bao, Wenxia Wang, Nian Dong, Zhen Yang, Xianjun Anhui University School of Electronics and Information Engineering Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China

ISBN: (纸本)9781665478571

In this study, we present a method for identifying ski jumpers' actions based on a variety of inertial data. Multiple convolutional neural networks are used to recognize each action phase and find the nodes with high recognition accuracy. The data acquired during ski jumping is separated into action phases and then processed to give stacked inertial signal images. According to the results, it is possible to distinguish the wearing part of a speed-type wearable inertial sensor and recognize the action phase during the ski jumping competitions more correctly and effectively. © 2022 IEEE.

关键词： Convolutional neural networks

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Unified Multi-Modal Data Aggregation for Complementary sensor networks Applied for Localization 95

Unified Multi-Modal Data Aggregation for Complementary Senso...

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IEEE 95th Vehicular Technology conference: (VTC-Spring)

作者： Berndt, Maximilian Krummacker, Dennis Fischer, Christoph Schotten, Hans D. German Res Ctr Artificial Intelligence Intelligent Networks Res Grp D-67663 Kaiserslautern Germany Univ Kaiserslautern Inst Wireless Commun & Nav D-67663 Kaiserslautern Germany

ISBN: (数字)9781665482431

ISBN: (纸本)9781665482431

Technological trends in different domains have enabled operators to integrate large numbers of sensors within their wireless communication networks. These sensors can provide data for many different fields of applications, e.g. process monitoring, eHealth or position feedback for closed loop control of autonomous vehicles. However, such systems often face the challenge to aggregate and process information from many distinct sources in a homogeneous and coherent manner. We propose a software architecture design that is based on any context management and message handling system. The general objective is to design a common interface for different information sources. We assume that by processing this data in conjunction it can be possible to obtain enhanced quality of information. The presented abstract architecture can enable the user to flexibly integrate different information Anchors - active sensors or passive tags - within a comprehensive multi-modal, multi-domain data aggregation platform. By applying a system as proposed, it is possible to generate a pool of homogeneous data sets that can be used as a sound foundation for further analysis. We demonstrate the feasibility of the concept by presenting a possible realization of a system as described in form of an inter-operable multi-technology indoor localization system.

关键词： Data handling digital twin heterogeneous networks Internet of Things (IoT) navigation

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Challenges and Opportunities of Using Data Fusion Methods for Travel Time Estimation 8

Challenges and Opportunities of Using Data Fusion Methods fo...

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8th International conference on Control, Decision and information Technologies (CoDIT)

作者： Guido, Giuseppe Haghshenas, Sina Shaffiee Vitale, Alessandro Astarita, Vittorio Univ Calabria Dept Civil Engn Via Bucci I-87036 Arcavacata Di Rende Italy

ISBN: (纸本)9781665496070

Collection and analysis of traffic data are the most critical challenges in traffic control in transportation networks. The dramatic growth of new technologies such as tiny devices equipped with elements of computing, sensors, and actuators in the smart environment of the internet of things (IoT) has led to the collection of large volumes of data. Identifying and combining data obtained from various smart devices plays a key role in data processing. Hence, data fusion is required to collect and extract usable data from numerous sources. This study discusses the challenges and opportunities of using data fusion to estimate travel time under wireless sensor networks (WSNs) in the intelligent Transportation Systems (ITS). For this purpose, the three main categories of data fusion, namely the probability-based method, the artificial intelligence-based method, and the evidence theory-based method, were considered. Consequently, discussing the advantages and drawbacks of data fusion techniques can give researchers a better perspective of travel time estimation with better accuracy.

关键词： Performance evaluation Wireless sensor networks Urban areas Data integration Estimation Transportation Traffic control

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An Efficient and intelligent IoT-Based Security Model for Enhanced Protection Using Motion Detection and Cloud Storage Optimization 2

An Efficient and Intelligent IoT-Based Security Model for En...

引用

2nd IEEE International conference on networks, Multimedia and information Technology, NMITCON 2024

作者： Gangadharaswamy, H.B. Kumar, Narendra Sharmila, N. Shilpa, M. Mallikarjunaswamy, S. Shivaji, R. Basavaraju, N.M. Balachandra, P. Komala, M. Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Rns Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jagadambha college of Engineering & Technology Dept Electronics and Telecommunication Engineering India Sjb Institute of Technology Dept of Mathematics Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350372892

The proposed IoT-based Smart Security Solution (IoT-SSS) for CCTV and network nodes addresses the challenges of high data volume management, power consumption, and cost inefficiency in traditional security systems. Leveraging Python's image processing for motion detection via passive infrared (PIR) sensors, IoT-SSS intelligently stamps video recordings and sensor data, activating only when necessary. This method ensures data is initially saved on local storage, significantly reducing the need for continuous recording and, thereby, minimizing power usage and storage demands. When data hits a pre-defined threshold, influenced by location sensitivity, it's automatically uploaded to the cloud, further optimizing storage needs and eliminating manual intervention. This smart approach not only conserves energy by powering down inactive devices but also slashes maintenance costs. Time, date, and sensor information are embedded directly onto each video frame, facilitating easy review without excessive data overload. Simulation results demonstrate that IoT-SSS outperforms traditional security systems in terms of data storage efficiency and power consumption, offering a cost-effective and energy-efficient alternative for modern security needs. © 2024 IEEE.

关键词： Cloud upload Data storage efficiency IoT-based Smart Security Solution (IoT-SSS) Motion detection Power consumption

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Design of intelligent Image Recognition Algorithms Based on Optical sensors and Generative Adversarial networks

Design of Intelligent Image Recognition Algorithms Based on ...

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Power, Electrical Engineering, Electronics and Control (PEEEC), International conference on

作者： Cong Ye Department of big Data and Internet Suzhou College of Information Technology Jiangsu China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

This paper presents the design of an intelligent image recognition algorithm based on optical sensors and Generative Adversarial networks (GAN). In today’s digital era, the growth of image data is increasing exponentially, which puts higher requirements on image recognition algorithms. Optical sensors, as an important data acquisition tool, play an important role in the field of image processing. Generative Adversarial networks, on the other hand, is a powerful deep learning model that can learn and generate images with realistic features. In this paper, an intelligent image recognition algorithm is designed by combining optical sensors with GAN, which realizes efficient recognition and classification of images by taking full advantage of data acquisition and feature learning. This study first introduces the basic principles of optical sensor technology and its application in image recognition. With the development of technology, optical sensors have continuously improved the accuracy and speed of image acquisition, and have been widely used in industrial, medical and other fields. Then, this paper elaborates the working principle of Generative Adversarial networks (GAN) and its importance in image *** realizes the ability of learning from data and generating realistic images by means of competitive learning, which brings new breakthroughs in the field of image recognition. An intelligent image recognition algorithm design based on optical sensor and GAN is proposed. The algorithm realizes the effective extraction and learning of image features through the steps of data preprocessing, optical sensor feature extraction and GAN feature learning. In the process of algorithm design, the features and advantages of optical sensors and GAN in image processing are fully considered, which makes the algorithm have high recognition accuracy and robustness. Finally, this paper verifies the performance of the proposed algorithm in image classification and recognition

关键词： Representation learning Deep learning Image recognition Accuracy Data preprocessing Generative adversarial networks Feature extraction Robustness Classification algorithms Optical sensors

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Smart Distributed Parking System using Internet of Things 6

Smart Distributed Parking System using Internet of Things

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6th International conference on intelligent Computing and Control Systems, ICICCS 2022

作者： Leelipushpam Paulraj, Getzi Jeba Johnraja Jebadurai, Immanuel Khan, Pathan Firoz Sumanth, Neela Reddy, Kothapally Vishal Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665410359

With the growth of cities and population, urban corporation faces potential challenges. One such challenge is regulating parking spaces. This become a tedious job during weekends and festival season. Improper regulation in parking spaces lead to traffic congestion and affects the fuel economy. Various parking solutions using sensor networks and image processing techniques have been proposed in literatures. For developing smart city environment, single parking space management is not sufficient and image processing based parking system can be applied only to large spaces. This paper proposes distributed parking management system. Three parking slots have been created. The IR sensors capture the status of the space and communicate to the cloud. The user can also check the real time parking space availability in mobile application. Prototype has been developed and checked under various condition. The proposed solution provides parking space information on real time and improves fuel efficiency. © 2022 IEEE.

关键词： sensor networks

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