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Soft body sensor networks: architectures and applications

Soft body sensor networks: Architectures and Applications

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2020 IEEE International conference on Networking, Sensing and Control, ICNSC 2020

作者： Dong, Wentao Cheng, Xiao Yao, Daojin School of Electrical and Automation Engineering East China Jiaotong University Nanchang China Rail Transportation Technology Innovation Center East China Jiaotong University Nanchang China

ISBN: (纸本)9781728168531

Body sensor networks (BSNs) are designed to collect, process, and transmit the biological data from different parts of human body from wearable electronics with limited resources. Conventional BSN node is also designed and fabricated by the rigid sensors, printed circuit board (PCB), and integrated chip (IC), which are not compatible with the soft human skin. The concepts of soft body sensor networks (SBSNs) are given in the manuscript based on wireless sensor networks and flexible electronics. SBSN node is composed of soft sensors, flexible data modulation and process unit and flexible wireless communication unit, and all the components of SBSN node are designed to be bended and be compatible with soft skin surface by flexible electronic. The architecture of SBSNs are built with several layers: soft sensors, data collection, wireless communication, body could, data fusion algorithm and monitoring interface for further online healthy monitoring. Multisensory decision fusion algorithm has been presented with biometric and medical monitoring applications which is subsequently analyzed for medical diagnosis. Emotion recognitions are realized to predict healthy state based on SBSNs using the intelligent data fusion algorithms. © 2020 IEEE.

关键词： sensor nodes

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Autonomous Driving architectures, Perception and Data fusion: A Review 16

Autonomous Driving Architectures, Perception and Data Fusion...

引用

IEEE 16th International conference on Intelligent Computer Communication and Processing (ICCP)

作者： Velasco-Hernandez, Gustavo Yeong, De Jong Barry, John Walsh, Joseph Inst Technol Tralee Irish Software Res Ctr IMaR Res Ctr Lero Tralee Ireland

ISBN: (纸本)9781728190808

Over the last 10 years, huge advances have been made in the areas of sensor technologies and processing platforms, pushing forward developments in the field of autonomous vehicles, mostly represented by self-driving cars. However, the complexity of these systems has been also increased in terms of the hardware and software within them, especially for the perception stage in which the goal is to create a reliable representation of the vehicle and the world. In order to manage this complexity. several architectural models have been proposed as guidelines to design, develop, operate and deploy self-driving solutions for real applications. In this work, a review on autonomous driving architectures is presented, classifying them into technical or functional architectures depending on the domain of their definition. In addition, the perception stage of self-driving solutions is analysed as a component of the architectures, detailing into the sensing part and how data fusion is used to perform localisation, mapping and object detection. Finally, the paper is concluded with additional thoughts on the actual status and future trends in the field.

关键词： Architecture Autonomous Driving Autonomous Vehicles Data fusion Localisation Mapping Perception Self-Driving Car sensor fusion

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DV-Hop localization Based on parameter correction and Improved zebra optimization algorithm

DV-Hop localization Based on parameter correction and Improv...

引用

International conference on Information, Communication and Networks (ICICN)

作者： Xun Wang Xiuwu Yu Yong Liu School of Electrical Engineering University of South China Heng Yang China College of Physics and Optoelectronic Engineering Shenzhen University Shen Zhen China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

The Internet of Things (IoT) makes extensive use of wireless sensor networks to monitor the physical environment. Since location data is the foundation of network applications in wireless sensor networks, precise node localization is essential to the operation of the entire system. An improved DV-Hop localization algorithm (IZDV) based on the fusion of Chaos, Flight, and Wandering strategies for zebra optimization is proposed in order to improve the localization accuracy of the widely used Distance Vector Hopping (DV-Hop) localization algorithm. The DV-Hop localization algorithm relies solely on network connectivity for localization, which results in a large error in the localization result. First, various communication radii are used to substitute the original integer hop information with fractional hop information. Second, the average hop distance information of the unknown nodes is corrected using the mean square weighted average error plus additional beacon node information. Lastly, the original least squares approach for node position estimation is replaced with the improved zebra optimization algorithm, which optimizes the classical zebra algorithm by combining chaotic, dynamic weighting and Levy. This accelerates the algorithm's convergence and strengthens its global and local search capabilities to keep the algorithm from prematurely entering the local optimum while still maintaining the algorithm's time complexity. The simulation results indicate that the average localization error of this paper's algorithm is reduced by about 60.51%, 22.65% and 19.32% in comparison with the classical DV-Hop algorithm, GDV-Hop algorithm and ISSADV-Hop algorithm in default network conditions, which verifies the better localization accuracy and stability of the proposed algorithm.

关键词： Location awareness Wireless communication Wireless sensor networks Accuracy Heuristic algorithms Vectors Stability analysis Internet of Things Time complexity Optimization

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An Optimization of Deep sensor fusion Based on Generalized Intersection over Union 20th

An Optimization of Deep Sensor Fusion Based on Generalized I...

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20th International conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Meng, Lianxiao Yang, Lin Tang, Gaigai Ren, Shuangyin Yang, Wu Harbin Engn Univ Informat Secur Res Ctr Harbin Peoples R China Chinese Acad Mil Sci Inst Syst Engn Natl Key Lab Sci & Technol Informat Syst Secur Beijing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

3D object detection is a major topic in unmanned driving and robotics, which is suffering from the low accuracy recently. We found that the loss function of 3D object detection network is the main cause to the low accuracy. For this, we proposed an optimized realization of deep sensor fusion network model (DSFN) based on Generalized Intersection over Union (GIoU). In DSFN, the designed backbone network is used to fuse point cloud features and image features, making full use of heterogeneous sensor information. Specifically, we introduced the GIoU as the loss function of the backbone network. We evaluated our model on KITTI dataset which is resulted from a LIDAR-camera setup. Compared with similar models, our model shows a higher accuracy.

关键词： Deep sensor fusion Network (DSFN) 3D bounding box estimation Loss function Deep learning Camera and Lidar

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Distributed Detection of Critical Nodes in Wireless sensor Networks Using Maximum Independent Set

Distributed Detection of Critical Nodes in Wireless Sensor N...

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2025 IEEE Wireless Communications and Networking conference, WCNC 2025

作者： Gao, Jieyu Li, Jie Wu, Qihui Wu, Youbiao Xu, Haikuo Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9798350368369

Identifying key nodes in a network is crucial for practical applications, especially when it comes to accurately detecting cut vertices, which play a vital role in maintaining network stability. As network complexity increases, relying on a single method to identify key nodes often fails to provide a comprehensive assessment of node importance in real-world scenarios. To address this, this paper proposes a novel framework for cut vertex identification that evaluates node importance from three perspectives: weighted fusion centrality metrics, the spanning tree algorithm, and topology graph properties. Additionally, existing methods based on centrality metrics often struggle with low accuracy in identifying cut vertices. To overcome this limitation, we have devised a method, termed the MIS-CV algorithm, that enables the accurate identification of network cut vertices by solely evaluating the intersection of node neighborhoods within the maximum independent set. We validated our proposed algorithm through simulation analysis on the Barabasi-Albert scale-free network. The results demonstrate that the MIS-CV key nodes identification algorithm surpasses traditional search tree algorithms in terms of both accuracy and operational efficiency. This proves that the MIS-CV algorithm has higher recognition accuracy without increasing computational complexity. © 2025 IEEE.

关键词： Trees (mathematics)

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Evaluating Lightweight GAN- and Adapted CTGAN-Based Data Synthesis for Predictive Maintenance in High-Radiation Environments

Evaluating Lightweight GAN- and Adapted CTGAN-Based Data Syn...

引用

IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Tamim M. Al-Hasan Xiaojun Zhai Klaus D. McDonald-Maier Faycal Bensaali Alice Cryer School of Computer Science and Electrical Engineering University of Essex Colchester Essex United Kingdom Department of Electrical Engineering College of Engineering Qatar University Al Tarfa Doha Qatar Remote Applications in Challenging Environments United Kingdom Atomic Energy Authority Culham Science Centre Abingdon Oxfordshire United Kingdom

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

This paper presents a comparative analysis of two developed Generative Adversarial Network (GAN) architectures for synthesizing sensor data in predictive maintenance (PdM) applications within high-radiation environments. The study ad-dresses the challenge of data scarcity in such settings, where experimental runs are constrained by the risk of device failure and economic considerations. The two GAN models: GAN-1 uses the Conditional Tabular GAN (CTGAN) architecture, and GAN-2 employs a custom network. These models generated synthetic datasets that were used to train and evaluate three machine learning algorithms: Random Forest, k-Nearest Neighbours, and eXtreme Gradient Boosting. The performance of these PdM models trained on synthetic data was compared against models trained on the original limited dataset. Results demonstrate that GAN-1 produced synthetic data closely mirroring the characteristics of the original dataset, enabling PdM models to achieve comparable performance levels. This study highlights the potential of GAN-based data synthesis in enhancing PdM model development for high-radiation environments, offering a viable solution to the challenges of limited data availability in such harsh settings. The findings have significant implications for improving operational reliability and safety in nuclear and other extreme environments where electronic systems are deployed.

关键词： Economics Electric potential Analytical models Biological system modeling Computer architecture Generative adversarial networks Data models Safety Synthetic data Predictive maintenance

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Hybrid Intelligent fusion-Based Perspectives for WSN-IOT

Hybrid Intelligent Fusion-Based Perspectives for WSN-IOT

引用

Network, Multimedia and Information Technology (NMITCON) International conference on

作者： Manjula Gururaj Rao K Hemant Kumar Reddy ChSree Kumar Priyanka H Sumathi Pawar NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karkala Department of Computer Science and Engineering VIT AP University Karkala Department of Computer Science NIST Berhampur Computer Science & Engineering PESU Bengaluru

Currently, numerous applications in modern life incorporate IoT (Internet of Things). The fundamental principle of IoT involves connecting various devices to the internet to access information. Among these devices are sensor nodes (SN) that utilize Wireless sensor Networks (WSN) for internet connectivity. The SN's battery life will steadily decrease as it is communicating with the BS (Base Station). Several methods are suggested to lengthen the lifespan of the SNs. By using a hybrid technique that separates the entire WSN network into clusters and leverages the cluster head (CH) to cut down on communication between the IOT devices. The suggested hybrid model focuses on managing the WSN-IOT energy efficiently. The system enhances its energy efficiency during communication, which includes inter-cluster communication, intra-cluster communication, and communication with the BS. The proposed model utilizes M-LEACH, M-CS, and M-PSO algorithms in distinct stages. The network is divided into three levels: The first level concentrates on inter-cluster formation and CH selection using the M-LEACH. In the second stage, M-CS facilitates communication between multiple CHs and the BS. Finally, the M-PSO algorithm is employed based on the threshold of CH nodes for improved outcomes.

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Edge Computing in Wireless Sensing applications

Edge Computing in Wireless Sensing Applications

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Mediterranean conference on Embedded Computing (MECO)

作者： Tudor Voicu Sâ ntejudean George Dan Mois Teodora Sanislav Silviu Corneliu Folea Department of Automation Technical University of Cluj-Napoca Cluj-Napoca Romania

Edge computing has emerged as the solution to the continuously increasing numbers of Internet-connected devices and to the ever increasing amounts of data transmitted by them. Through the use of advanced algorithms and hardware components it allows sensor data processing at the source and reduces the volumes of data transmitted within the network. This paper focuses on the idea of edge computing, looks at some of the most important achievements in the field, and presents a use case system that shows how edge intelligence is becoming more common. The developed use case system is an environmental wireless sensor that is able to run for long periods of time on a single coin-cell battery while measuring parameters like equivalent CO 2 (carbon dioxide) and VOC (Volatile Organic Compounds). This was made possible through the device's design, components, and advanced firmware, that uses high-level signal processing and fusion algorithms.

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KF K-means: A High Performance K-means Implementation using Kernel fusion

KF K-means: A High Performance K-means Implementation using ...

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IEEE International conference on Big Data

作者： Kaiming Ouyang Vincent Tran Jinyang Liu Bryan M. Wong Zizhong Chen NVIDIA Corporation Santa Clara CA USA University of California Riverside CA USA

The K-means algorithm is one of the simplest and most universal clustering algorithms. Significant work has been carried out over several years to improve its performance in both academic and industrial applications. Researchers have optimized K-means not only on the algorithm level but also on the architecture level. Notably, GEMM, a rigorously studied matrix multiplication operation, has been used to speed up the Euclidean-distance calculations in the K-means algorithm. The Intel DAAL library currently provides a fast K-means implementation based on the Intel Math Kernel Library GEMM subroutine and low-level architecture information. However, in spite of utilizing the MKL GEMM subroutine and architecture properties, the performance of the state-of-the-art K-means implementation is still far from its hardware peak performance. This paper presents a faster fused-matrix K-means kernel that is superior to current K-means designs. Based on our experimental results, the fused matrix K-means kernel runs around 76% faster than the state-of-the-art Intel DAAL K-means algorithm and is able to achieve nearly double floating point performance on Intel x86-84 Ivy micro-architectures.

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International conference on Emerging Wireless Communication Technologies and Information Security, EWCIS 2020

International Conference on Emerging Wireless Communication ...

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International conference on Emerging Wireless Communication Technologies and Information Security, EWCIS 2020

ISBN: (纸本)9789813363922

The proceedings contain 41 papers. The special focus in this conference is on Emerging Wireless Communication Technologies and Information Security. The topics include: Modern Radar Topology for Bio-medical applications;Ensuring Reliability in Vehicular Collision Avoidance Using Joint RFID and Radar-Based Vehicle Detection;dimensional Analysis and Gradation of Rice Grain Using Image Processing;feature Dimension Reduction for Efficient Classification of Dermoscopic Images with Feature fusion;feature Edge-Detail Preservation of Random-Valued Impulse Noise in Images;sparse Auto-encoder Improvised Texture-Based Statistical Feature Estimation for the Detection of Defects in Woven Fabric;Evaluation of ML-Based Sentiment Analysis Techniques with Stochastic Gradient Descent and Logistic Regression;A Comparative Analysis of Sentiment Analysis Using RNN-LSTM and Logistic Regression;android-Based Assistance System for Visually Impaired Person Using Deep Learning and Augmented Reality;a Study of Black Hole Attacks in Delay Tolerant Network;towards Data Storage, Availability and Scalability with the Aid of Blockchain;countermeasures of Different Jamming Attacks in Wireless sensor Networks;Detecting Acute Lymphoblastic Leukemia Through Microscopic Blood Images Using CNN;blockchain Marketplace—A Novel Overview for Real-Time Implementation;applying Machine Learning algorithms in Network-Based Intrusion Detection Systems;principal Component Analysis in Body sensor Networks for Secure Data Transmission;quantum Inspired Multiobjective Optimization in Clustered Homogeneous Wireless sensor Networks for Improving Network Lifetime and Coverage;A Review of Wireless Charging in WSN;vibration Measurement Using Accelerometer sensor and Fast Fourier Transform;intelligent Fire Outbreak Detection in Wireless sensor Networks;A Survey on Open-Source SCADA for Industrial Automation Using Raspberry Pi;lightweight Authenticated Encryption for Cloud-assisted IoT applications.

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