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Energy dissipation and Load balancing in self-organized heterogeneous WSN for e-applications 11

Energy dissipation and Load balancing in self-organized hete...

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11th International conference on Information systems and advanced Technologies, ICISAT 2021

作者： Gherbi, Chirihane Doudou, Roumaissa Aliouat, Zibouda Ferhat Abbas University Setif 1 Computer Science Department Setif Algeria

ISBN: (纸本)9781665478243

Many communication protocols, especially for wireless sensor networks, have been proposed: Multi-hop transmission protocols, cluster-based protocols (static or dynamic clustering), etc. Several works have shown the impact of these protocols on energy dissipation and load balancing. By analyzing the advantages and disadvantages of different protocols in HWSN (Heterogeneous wireless sensor networks) we have proposed a communication protocol: LBCMH (Load-Balancing based Clustering Multipath routing protocol for Heterogeneous sensor networks in environmental applications (e-applications) with the objectives of a fair distribution of the CHs in the network, the load balancing of the CHs and the self-organization of the nodes in a distributed manner. After having detailed the functioning of our protocol, we present the realization of the simulations by the NS3 simulator, whose objective is to allow us to compare the results of our LBCMH(Load-Balancing based Clustering Multipath routing protocol for HWSN) with the two protocols WBCHN and to assess the efficacy of the proposed LBCMH protocol ((Weight based clustering in wireless sensor networks) [9] and DEACP (Distributed Energy efficient Adaptive Clustering Protocol) [10]. We thus take into account several metrics for the performance evaluation of our contribution. We were able to show through the simulation results obtained that the objectives of our protocol have been reached. © 2021 IEEE.

关键词： Energy dissipation

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Wearable Web Enabled IoT-Based Health Monitoring Framework

Wearable Web Enabled IoT-Based Health Monitoring Framework

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advanced Computing and Emerging Technologies (ACET), International conference on

作者： Neha Gupta Shruti Jha Vritti Sachdeva Ritika Sharma School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad Haryana

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

The healthcare industry is one of the domains where the Internet of Things (IoT) has made the global impact. By intelligently integrating applications into medical devices, IoT enables the provision of smart and efficient healthcare facilities. Through such sensors, which can be either surgically placed or worn on the body, patients can be monitored continually using the advanced technologies. Such data collection allows for the analysis, mining, and aggregation of information to facilitate early disease prediction. Sophisticated remote health monitoring systems have been developed due to the availability of cheap smart phones and wearable sensors. Even if doctors/pharmacists are unavailable, such health monitoring IoT solutions can be helpful in disease prevention/management and accurate patient diagnosis. In this paper, a novel IoT-based wireless remote health monitoring system for a patient in an indoor environment is presented for monitoring continuous patient vital sign data like temperature and pulse. The collected data is stored on an IoT platform so that only the authorised person can obtain the data. This makes it possible for the doctors to diagnose conditions from the information that has been collected, and this improves the chances of offering effective care at the right time.

关键词： Temperature sensors Temperature measurement Wireless communication Wireless sensor networks Internet of Things Communication system security Biomedical monitoring Monitoring Wearable sensors Diseases

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A Flexible Hybrid Resource Allocation Model for Enhanced Efficiency using AI in Wireless sensor Networks

A Flexible Hybrid Resource Allocation Model for Enhanced Eff...

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Electronics and Renewable systems (ICEARS), International conference on

作者： Purushotham Endla R. Mohandas Achinta Saikia C. Lakshmi G Karthikeyan Tejashree Tejpal Moharekar Department of Physics School of Sciences and Humanities SR University Warangal Telangana India Department of ECE Chennai Institute of Technology Chennai Department of Statistics Mariani College Jorhat Assam Saveetha Engineering College Chennai Department of Artificial Intelligence & Data Science Karpagam College of Engineering Coimbatore Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Wireless sensor Networks (WSNs) have become a cornerstone technology in various fields, including environmental monitoring, industrial automation, and healthcare. This paper presents a novel hybrid resource allocation model designed to enhance efficiency in Wireless sensor Networks (WSNs) using advanced Artificial Intelligence (AI) techniques. The proposed model integrates static and dynamic resource allocation strategies with machine learning and reinforcement learning to optimize key network resources such as energy, bandwidth, and processing power. Comprehensive evaluations demonstrate that the proposed model outperforms nine existing models across multiple metrics, including energy efficiency, network lifetime, throughput, delay, latency, and accuracy of resource allocation. The proposed model achieved an energy efficiency of 0.98 J/bit, extended network lifetime to 620 days, and maintained a high throughput of 620 Kbps. Notably, the model demonstrated the lowest delay (90 ms) and latency (180 ms) while achieving the highest resource allocation accuracy of 97.8%. These results highlight the model’s capability to dynamically adjust resource allocation in response to real-time network conditions, ensuring optimal performance and resource utilization. The proposed model’s success in efficiently managing WSN resources marks a significant advancement in the field, offering a robust solution for various WSN applications requiring high efficiency and reliability.

关键词： Wireless sensor networks Renewable energy sources Accuracy Reinforcement learning Throughput Energy efficiency Real-time systems Delays Resource management Reliability

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Application and optimization of FPGA-based real-time motion control systems in robotics

Application and optimization of FPGA-based real-time motion ...

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International conference on Electrical Engineering and Intelligent Control (EEIC 2024)

作者： Zimo Chen School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol BS8 1QU UK

ISBN: (数字)9781837242160

With the increasing popularity of robotic applications in various fields and the important role robots play in technological innovation and high-end manufacturing, it is crucial to optimise their performance. Field Programmable Gate Arrays (FPGAs) have greatly advanced control technology, leading to their integration into traditional robotic motion control systems, thereby enhancing performance, efficiency, and flexibility. The paper reviews traditional motion control techniques such as open-loop, closed-loop, model predictive, and adaptive control, then explores the unique capabilities of FPGAs, including parallel processing, data acceleration, system-level integration, and low-power design. These capabilities are analyzed in the context of improving sensor data processing, motion control algorithms, path planning, motor control, communication, image processing, safety, and power management in robots. The article demonstrates the advantages of using FPGAs for precise control, efficient data processing, parallel processing, and low power consumption in robot motion control. It reveals the great potential of FPGA technology in improving robot performance and efficiency. This not only provides a new path to enhance the adaptability and robustness of robots in complex environments, but also provides guidance for future technology development and industrial applications.

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Learning Topological Representation of sensor Network with Persistent Homology in HCI systems

Learning Topological Representation of Sensor Network with P...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Yan Yan Chengdong Li Jing Xiong Lei Wang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Wenzhou Institute of Technology Wenzhou China

Hand gesture and movement analysis is a crucial learning task in Human-computer interaction (HCI) applications. sensor-based HCI systems simultaneously capture the information with multiple locations to track the coordination of different regions of muscles. Based on the fact that there exists a temporal correlation between the regions, the connectivity analysis of sensor signals builds a network. The graph-based approach for analyzing the sensor network has provided novel insight into the learning in HCI, which has not been broadly investigated in hand gesture recognition tasks. This work proposes a topological representation learning scheme as a graph-based approach for sensor network analysis. Through investigation of the topological properties with persistent homology, the spatial-temporal characteristics are well described to build recognition models. Experiments on the NinaPro DB-2, DB-4, DB-5, and DB-7 datasets with sensor networks built with sEMG signal and IMU signal demonstrate exceptional performance of the proposed topological approach. The topological features are effective in graph representation learning with sensor networks used in hand gesture recognition. The proposed work provides a novel learning scheme in HCI systems and human-in-the-loop studies.

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Soft sensor for monitoring dynamic changes in cell composition 9

Soft sensor for monitoring dynamic changes in cell compositi...

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9th IFAC conference on Foundations of systems Biology in Engineering, FOSBE 2022

作者： Espinel-Ríos, Sebastián Morabito, Bruno Bettenbrock, Katja Klamt, Steffen Findeisen, Rolf Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

ISBN: (纸本)9781713867890

Precise and accurate online monitoring methods are needed to enable smart biomanufacturing and automation. Most of the sensors available focus on process parameters such as metabolite and dissolved gas concentrations, cell density or viability, among other variables like pH, temperature, etc. In this work, we develop a soft sensor algorithm to estimate the cell composition online, a very important aspect often overlooked in the bioprocess monitoring literature. Our strategy is based on full information estimation, an optimization-based estimator that takes into account the dynamics of the cell metabolism and considers all the available measurements from the beginning of the process, thus it has a memory effect. Being able to track dynamic changes in cell composition can open the door to promising applications, e.g., predictive control and automation of biosystems. As a case study, we consider the Escherichia coli's metabolism growing on glycerol under different levels of oxygen supply. We compare the performance of our soft sensor method against resource balance analysis, a previously proposed estimator based on steady-state assumptions. Overall, the presented full information estimator was able to track the dynamic changes in cell composition significantly more accurately. We also discuss how our estimation strategy can be transformed into a moving horizon estimation, where only the available measurements in a fixed and moving window are considered, thereby reducing possible computational burdens. Copyright © 2022 The Authors.

关键词： Metabolism

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Autonomous Agriculture System

Autonomous Agriculture System

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Smart systems for applications in Electrical Sciences (ICSSES), International conference on

作者： Rakesh Vallabha Vaidya A N Rudresh H R Naveen Gowda Somshekar Banappa Hallad B N Shashikala Dept. of E&CE Siddaganga Institute of Technology Tumkur Karnataka

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

Agriculture, vital to India’s economy, faces challenges due to outdated technology and rural-to-urban migration, impacting productivity. The rise of IoT promises advanced farming, where autonomous robots play a key role in reducing human errors during manual spraying in plantations. As a crucial income source, Indian agriculture contends with diseases, pests, and climate changes. The IoT forms the basis for a server-based system that detects leaf diseases. A robot captures and categorizes plant images, triggering the pesticide sprayer for diseased plants. The robot, equipped with a weed removing blade, also sows seeds. An application, driven by a trained Neural Network model (CNN), identifies multiple plant diseases through image segmentation. A soil moisture sensor guides water supply, and the robot’s seed sowing mechanism, featuring a plough, pelton wheel, and leveller, enhances efficiency by addressing various farm tasks. This holistic approach optimizes farming practices without compromising effectiveness.

关键词： Service robots Neural networks Soil moisture Spraying Wheels Robot sensing systems Seeds (agriculture)

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[DC] Data-Driven Expertise Assessment in XR: Analyzing Multimodal sensor Data for Psychomotor and Cognitive Tasks

[DC] Data-Driven Expertise Assessment in XR: Analyzing Multi...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Dong Woo Yoo Northeastern University

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

In this doctoral research, a novel approach is developed for the real-time assessment of user expertise, focusing on skill-based and cognitive tasks within Extended Reality (XR) environments. This approach combines advanced user modeling with state-of-the-art sensors and includes an in-depth analysis of gaze behavior and physiological responses. A distinctive aspect of this research is the real-time analysis of multimodal sensor data, which provides deeper and more precise insights into user skills and cognitive abilities. The goal is to advance the field of expertise assessment by introducing a nuanced and dynamic perspective that is suitable for a variety of applications across different domains. This research aims to establish new standards in the assessment of user expertise, meeting the evolving needs of modern educational and professional settings.

关键词： Solid modeling Three-dimensional displays Multimodal sensors conferences User interfaces Real-time systems Physiology

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2-Axis Planar Hall Magnetic Field sensors with Sub NanoTesla Resolution

2-Axis Planar Hall Magnetic Field Sensors with Sub NanoTesla...

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Magnetic conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： P.T. Das H. Nhalil V. Mor M. Schultz N. Hasidim A. Grosz L. Klein Department of Physics Institute of Nanotechnology and Advanced Materials Bar-Ilan University Ramat-Gan Israel Intelligent Materials and Systems (FWID) Helmholtz Center Dresden-Rossendorf Dresden Germany Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Magnetic sensors based on the planar Hall effect (PHE) are attractive for applications where sub nano-Tesla field resolution is required. Here we present detailed noise study of PHE sensors made of two crossing masnetic ellipses that measure two axes of the magnetic field in the sensor plane with equivalent magnetic noise level better than 350 $\text{pT} / \sqrt{}\text{Hz}$ at 10 Hz in exactly the same region while keeping the size and noise level of a single axis sensor.

关键词： Magnetic field measurement Magnetometers Magnetic sensors Noise Magnetic devices Magnetic noise Size measurement

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Health Gaming based Activity Recognition Using Body-Worn sensors via Artificial Neural Network

Health Gaming based Activity Recognition Using Body-Worn Sen...

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International conference on Communication Technologies (ComTech)

作者： Sumbul Khan Adnan Ahmed Rafique Ahmad Jalal Dept. of Computer Science & IT University of Poonch Rawalakot Rawalakot AJK Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331515331

ISBN: (纸本)9798331515348

Integrating body-worn sensors with virtual reality technology has revolutionized user engagement in health gaming and e-learning applications. This paper presents an activity recognition system using the Wireless sensor data mining dataset, designed to enhance interaction in Virtual reality environments. The system employs a multi-step process that includes signal preprocessing with a second-order Butterworth filter, feature extraction via Fourier transform, Random forest, and Power spectral density, followed by feature selection using Recursive feature elimination. Classification is performed using artificial neural network to ensure accurate and real-time activity recognition. The proposed approach addresses challenges in computational complexity and sensor data noise by integrating optimized signal processing techniques with advanced machine learning models. The integration of activity recognition with Arduino and Unity platforms demonstrates significant improvements in user interaction quality within Virtual reality based health and educational gaming. Experimental results demonstrate that our proposed method achieves 78% accuracy in activity recognition, outperforming Random Forest (75.9%) and SVM-Radial (65.77%), resulting in a 2.1 percentage point improvement. Additionally, the integration of optimized preprocessing, feature selection, and an artificial neural network classifier enhances computational efficiency, leading to a noticeable reduction in processing latency, thereby ensuring real-time performance suitable for Virtual Reality applications.

关键词： Wireless sensor networks Electronic learning Accuracy Virtual reality Artificial neural networks Activity recognition Feature extraction Real-time systems sensors Random forests

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