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Modeling the Performance of Wireless Body sensor networks 5

Modeling the Performance of Wireless Body Sensor Networks

引用

5th international conference on Engineering Technology and its Applications, IICETA 2022

作者： Saleem, Ali Samir Hasson, Saad Talib University of Babylon College of Information Technology Babylon Iraq

ISBN: (纸本)9781665472159

Wireless Body Area Network (WBAN) is a special purpose network of sensor nodes. Large number of patient's conditions could be continuously monitored, alarms the person who wears sensors about auto medication during emergency condition. Due to the advancement in technology which has led to a tremendous increase in the use of wireless devices, the wireless body area network sensors are becoming a necessity part of the human daily life. In this paper the proposed system is based on the on-body store and flood routing (OBSFR) routing techniques to control flooding packet in WBAN depending on the time required to build route among WBAN network elements. OBSFR decreased total elapsed time to build routing table for overall Internet of things (IoTs) sensors, which showed enhancement during increased number of body sensors in the network. the results showed the proposed adaptive OBSFR routing techniques has better performance in reducing the total average of each packet delivery delay as 21.672 seconds, throughput as 35.877 Bps, and packet delivery ratio as 98.964 (%) of 23 IoT sensor nodes. © 2022 IEEE.

关键词： sensor nodes

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Comparative Analysis of Quantization Schemes for Physical Layer Key generation 5

Comparative Analysis of Quantization Schemes for Physical La...

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5th IEEE international conference on Advances in Science and Technology, ICAST 2022

作者： Kadam, Sujata Gomes, Joanne R.A.I.T Dept. Extc Engg. Nerul India S.F.I.T Dept. Information Technology Borivli India

ISBN: (纸本)9781665492638

Wireless networks get subjected to the different attacks because of it's open nature. Eavesdropping attack, which occurs at the Physical layer of the OSI model, has a great impact on wireless network security. Conventional security in wireless networks depends on the cryptographic techniques. But in wireless sensor networks where a key management infrastructure may not be present and the power resources are also limited, generating a cryptographic key between two mobile entities becomes difficult. Hence Physical layer Security (PLS) has become a novel research solution. In PLS based key generation, a symmetric secret key is obtained at both the ends of the legitimate users using the properties of wireless channel. the key generation process includes four major blocks namely channel measurement, quantization of input samples, error detection and correction and privacy amplification. the output of quantizer has a great effect on the bit mismatch rate (BMR) between the two legitimate users. It also has an impact on key generation rate (KGR). Hence selecting a proper quantizer becomes very essential. In this paper different Quantization schemes are analyzed to achieve effective PLS between the two legitimate users. © 2022 IEEE.

关键词： Wireless sensor networks

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A Critical Analysis Toward Sustainable Farming: the Role of AI in Weed Identification and Management 5th

A Critical Analysis Toward Sustainable Farming: The Role of ...

引用

5th international conference on Communication and Intelligent Systems (ICCIS)

作者： Singh, Shweta Yadav, Pritaj Rabindranath Tagore Univ Bhopal India

ISBN: (纸本)9789819720521;9789819720538

An artificial intelligence-based weed detection system is a computerized system designed to automatically identify and classify different types of weeds in agricultural fields. the system utilizes advanced computer vision techniques and machine learning algorithms to accurately detect and differentiate weeds from crops or other elements in the field. the weed detection system typically consists of hardware components such as cameras or drones which capture high-resolution images or videos of the agricultural area. these images are then analyzed by the artificial intelligence algorithms which have been trained on large datasets of weed images to recognize and distinguish various weed species. this paper examines the pivotal role of AI in weed detection, a critical aspect of farming that determines crop yield and health. through a comprehensive review, we shed light on the diverse AI-driven techniques including image recognition using Deep Learning, real-time automation, data augmentation, multispectral imaging, and predictive analysis, among others. the ability of AI to distinguish between crops and weeds, often in real-time and under varied environmental conditions, underscores its transformative potential. As weed management represents a significant challenge in agriculture, the precise and proactive capabilities offered by AI can lead to optimized herbicide usage, reduced costs, and enhanced crop productivity.

关键词： Image processing Image enhancement Weed detection Machine learning

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Deep Audio Spectral processing for Respiration Rate Estimation from Smart Commodity Earbuds

Deep Audio Spectral Processing for Respiration Rate Estimati...

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4th IEEE-EMBS international conference on Wearable and Implantable Body sensor networks (BSN) / 18th IEEE-EMBS international conference on Biomedical and Health Informatics (BHI)

作者： Ahmed, Mohsin Y. Ahmed, Tousif Rahman, Md Mahbubur Wang, Zihan Kuang, Jilong Gao, Alex Samsung Res Amer Mountain View CA 94043 USA

ISBN: (数字)9781665459259

ISBN: (纸本)9781665459259

Respiration rate is an important health biomarker and a vital indicator for health and fitness. With smart earbuds gaining popularity as a commodity device, recent works have demonstrated the potential for monitoring breathing rate using such earable devices. In this work, for the first time we utilize deep image recognition techniques to infer respiration rate from earbud audio. We use image spectrograms from breathing cycle audio signals captured using Samsung earbuds as a spectral feature to train a deep convolutional neural network. Using novel earbud audio data collected from 30 subjects with both controlled breathing at a wide range (from 5 upto 45 breaths per minute), and uncontrolled natural breathing from 7-day home deployment, experimental results demonstrate that our model outperforms existing methods using earbuds for inferring respiration rates from regular intensity breathing and heavy breathing sounds with 0.77 aggregated MAE for controlled breathing and with 0.99 aggregated MAE for at-home natural breathing.

关键词： respiration audio spectrogram convolutional neural network earbuds

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An Efficient Maximum Subcarrier Power Detection Scheme for OFDM-IM Systems 19

An Efficient Maximum Subcarrier Power Detection Scheme for O...

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19th IEEE international Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Lian, Yuhao Ying, Mingjun Wang, Shuyu Wang, Yuhua Chongqing Univ Posts & Telecommun Chongqing Sch Commun & Informat Engn Chongqing 400065 Peoples R China

ISBN: (纸本)9798350333398

As a promising technology, Orthogonal Frequency Division Multiplexing-Index Modulation (OFDM-IM) has received significant attention in wireless communication systems. However, as the number of subcarriers increases, the complexity of the Maximum Likelihood (ML) detector grows exponentially. In this paper, we propose a novel detection method, referred to as the Maximum Subcarrier Power (MSP) detection algorithm. the MSP algorithm leverages the power information of each subcarrier to detect its activation status and employs a power threshold to determine the appropriate modulation method. Specifically, the MSP algorithm switches from SIPM-OOFDM to OFDM-IM in low signal-to-noise ratio (SNR) scenarios. In comparison to ML detection and Log-Likelihood Ratio (LLR) detection techniques based on optimum transceiver design in OFDM-IM, the proposed MSP detection algorithm has lower complexity and is more robust in terms of Bit Error Rate (BER) performance at varying noise levels. Additionally, the MSP algorithm effectively reduces the cost of Digital Signal processing (DSP) and the detection time. this consequently leads to enhanced communication efficiency, demonstrating great potential for future low-latency B5G/6G wireless networks.

关键词： OFDM-IM ML detection MSP detection Signal processing Robustness

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Surveying Lightweight Neural Network Architectures for Enhanced Mobile Performance 5th

Surveying Lightweight Neural Network Architectures for Enhan...

引用

5th international conference on Smart Applications and Data Analysis for Smart Cyber Physical Systems

作者： Briouya, Hasnae Briouya, Asmae Choukri, Ali Ibn Tofail Univ Comp Sci Lab Fac Sci Kenitra Morocco

ISBN: (纸本)9783031770425;9783031770432

this study explores the creation of lightweight neural network architectures specifically designed for mobile devices. the main focus is on MobileNet, but we also examine other models like ShuffleNet, SqueezeNet, EfficientNet, MnasNet, and NASNet Mobile. the main challenge is to carefully evaluate these models to find the best balance between simplicity, accuracy, and efficiency, considering the diverse needs of mobile applications. We assess important metrics such as simplicity, accuracy, and efficiency within these architectures. the main goal is to provide guidance to practitioners in choosing the most suitable architecture. We offer insights into the trade-offs and advantages of each model through both quantitative and qualitative assessments. We consider factors like computational resources, accuracy requirements, and processing speed. the findings of this research provide valuable insights for practitioners who want to make informed decisions about the best neural network architecture for mobile devices. this guidance is tailored to their specific computational limitations and application requirements, helping them make strategic decisions in this specialized field.

关键词： MobileNet Lightweight neural networks Computational efficiency Semantic segmentation Mobile applications

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A Light Traffic Load and Highly Reliable Emergency Message Broadcasting Mechanism for VANETs 5

A Light Traffic Load and Highly Reliable Emergency Message B...

引用

5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Xu, Huai-Ting Lei, Xiao-Ying College of Information Engineer Yangzhou University Yangzhou China

ISBN: (纸本)9781665485890

In Vehicular Ad-hoc networks (VANETs), nodes are equipped to collaboratively broadcast emergency messages in case of any transportation emergence events, which can improve the road safety. However, heavily repeated rebroadcasting of messages leads to severe redundancy and collisions, bringing in inefficient channel utilization and low reliability. In order to improve this issue, we propose a probabilistic rebroadcast method to reduce the traffic load caused by repeated rebroadcasting of a same message. Instead of transmitting an emergency message once receives one, a node broadcasts under a certain probability, which is determined according to the node density, traveling velocity, and the distance of the node itself to the source node. Moreover, a waiting process is applied to eliminate the collisions caused by simultaneous broadcasting from multiple nodes. through extensive simulations, we demonstrate that proposed mechanism can effectively reduce traffic loads and alleviate transmission collisions. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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Power Line Partial Discharge Detection Using Multi-Scale 1D Convolutional Neural networks 5

Power Line Partial Discharge Detection Using Multi-Scale 1D ...

引用

5th international conference on Power and Energy Technology, ICPET 2023

作者： Jiang, Yuliang Xu, Zhenhuan Fang, Dan Zhang, Guoliang Zhi, Bihen Wang, Bing State Grid Shanghai Electric Power Corporation Training Center Shanghai China Nanjing Haoqing Information Technology Co. Ltd Nanjing China

ISBN: (纸本)9798350339673

In recent years, due to the widespread access of distributed power supply, the topology of electrical network becomes more and more complex such that it is difficult to analyze the fault causes of transmission lines, and partial discharge (PD) is a type of failure that often occurs. An automated transmission lines PD detection system requires automated feature extraction and online identification of PD. Firstly, A PD detection method based on multi-scale one dimensional convolutional neural network (MS-Conv1D) is proposed in this paper, which takes an arbitrary-long time series of fault signals as input. Furthermore, the single-phase and three-phase electrical signals can be dealt with an unified structure of neural network. Secondly, a complete flow of training MS-Conv1D and data processing is given. Finally, simulations on a real PD dataset shows that the detection accuracy of MS-Conv1D reaches 90% with 20MHz sampling rate, and the receiver operating characteristic (ROC) points indicate the robustness of MS-Conv1D at different frequencies. © 2023 IEEE.

关键词： automated feature extraction convolutional neural network fault diagnosis partial discharge

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5G-Based Body sensor Network for Real-Time Feedback in Running 12

5G-Based Body Sensor Network for Real-Time Feedback in Runni...

引用

Proceedings of the 12th international conference on Sport Sciences Research and Technology Support, icSPORTS 2024

作者： Mayr, Sebastian Rieser, Harald Human Motion Analytics Salzburg Research Forschungsgesellschaft MbH Salzburg Austria

ISBN: (纸本)9789897587191

Immediate feedback is vital in sports and motor learning. Traditionally, coaches provide this feedback. However, recent developments in wearable sensing and feedback devices, state-of-the-art processing algorithms and sensor data integration allow for new applications and insights. While this allows for more direct feedback, it also introduces new demands on feedback systems in terms of network transmission speed, energy consumption and needed calculation power. the 5G networking standard is a promising technology to address these issues. It promises reduced energy usage, high availability, latency in the low millisecond range and high data rates up to 10 Gbps. Despite these characteristics, there has not been much research on the use of 5G in sports biofeedback systems. therefore, this work aims to assess the usability of 5G for a real-time feedback system in the sports context. We did this by developing a distributed feedback system for the assessment of running gait symmetry. the system utilizes internal smartphone IMU as sensors and employs a 5G infrastructure for data transmission to an offsite server for the calculation of running metrics and generation of the feedback signal. Pilot tests, using the feedback system, showed mean roundtrip times of 140.14ms (sd = 14.47ms) for outdoor use and 128.92ms (sd = 25.83ms) for indoor use. these results indicate that promised low latencies by the 5G standard are currently not reachable outside of controlled testing environments. Nonetheless, as these times are below average reaction times in recreational athletes (150ms) it is still promising for real-time feedback applications in running. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Body sensor networks

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Dynamic Reinforcement and Alignment Graph Convolution networks for Few-Shot Learning 5

Dynamic Reinforcement and Alignment Graph Convolution Networ...

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5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Yan, Minghao Lu, Qinxin State Grid Wuxi Power Supply Company Wuxi China

ISBN: (纸本)9781665485890

the dependence of deep learning models on large-scale labeled training data limits their application in real-world scenarios. To address this problem, researchers have proposed few-shot learning. However, most existing few-shot learning methods tend to ignore the contribution of local detailed information with class characteristics to classification. In this paper, we propose the dynamic reinforcement and alignment graph convolution networks (DRAGCN). Our proposed model can learn to generate the reinforcement basis that contains valuable information of local details with class characters based on experiential knowledge and obtain the reinforced feature maps by solving the neural ordinary differential equations (Neural ODE). these reinforced feature maps of the input images are constructed as graph-structured data, and the node features and edge features of the graph are optimized with the semantic alignment graph convolution networks, which introduces the semantic alignment operation to prevent the over-smoothing phenomenon. Experimental results on two popular datasets show that the proposed DRAGCN outperforms existing methods on few-shot learning tasks. © 2022 IEEE.

关键词： Ordinary differential equations

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