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Enhancing Energy Efficiency in IoT networks with AI optimized LEACH Protocol and Digital Twin Simulation

Enhancing Energy Efficiency in IoT Networks with AI optimize...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International conference on

作者： Adarsha Harinaiha Pavan Chaudhary Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The convergence of the Internet of Things (IoT) via artificial intelligence (AI) will undoubtedly revolutionize networking. IoT is a technological paradigm shift facilitating the incorporation of many applications, including smart homes and wearable gadgets, via automation, wireless sensors, and embedded systems. Furthermore, the production of AI enhances this phenomenon by delivering data-driven insights, streamlining procedures, and creating new avenues for development. The extensive implementation of devices in Internet of Things (IoT) networks has raised worries over escalating energy usage. Utilizing environmentally friendly techniques is crucial to maintain network service for nodes that sense with limited power supplies. One approach that substantiates this assertion is LEACH, an acronym for Low Energy Adaptive Clustering Hierarchy. The objective of this form of communication is to optimize the transfer of information to base sites by successfully configuring clusters within networks and executing real-time adjustments across cluster nodes. This work enhances the effectiveness of IoT systems by using a virtual twin simulation to optimize the protocol known as LEACH. Employ artificial intelligence analysis and virtual network models to evaluate power consumption and efficiency. Cache nodes are essential in this architecture since they receive information from the cluster's head & transmit it through the main hub. Utilizing simulation tools and artificial intelligence, we can enhance the reliability and energy efficiency of IoT devices. The results indicate an 84% decrease in inactive nodes & a 1.67-fold rise in node energy use relative to traditional methods. The present study suggests a prospective future trajectory for LEACH-based, AI-augmented, low-power IoT infrastructure.

关键词： Wireless communication Wireless sensor networks Network topology Energy efficiency Topology Digital twins Internet of Things Data communication Artificial intelligence intelligent sensors

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Uncertainty-Aware Dynamic Fusion for Multimodal Clinical Prediction Tasks

Uncertainty-Aware Dynamic Fusion for Multimodal Clinical Pre...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jiayu Guo Ying Cheng Wen He Yuejie Zhang Rui Feng Xiaobo Zhang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China National Children’s Medical Center Children’s Hospital of Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal fusion offers significant potential for enhancing medical diagnosis, particularly in the Intensive Care Unit (ICU), where integrating diverse data sources is crucial. Traditional static fusion models often fail to account for sample-wise variations in modality importance, which can impact prediction accuracy. To address this issue, we propose a dynamic Uncertainty-Aware Weighting (UAW) strategy that adaptively adjusts the importance of different modalities based on their reliability. This strategy is coupled with an Expert Ensemble Fusion (EEF) module, which leverages self-attention mechanisms and modality-specific FeedForward networks (FFNs) to preserve and integrate critical information from various modalities. The proposed method demonstrates its efficacy through extensive experiments on phenotype classification and mortality prediction tasks, showing improved accuracy and robustness in handling diverse clinical data.

关键词： Accuracy Phenotypes Soft sensors Mortality Signal processing Predictive models Robustness Medical diagnosis Speech processing X-ray imaging

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SMCNet: Supervised Surface Material Classification Using mmWave Radar IQ Signals and Complex-valued CNNs

SMCNet: Supervised Surface Material Classification Using mmW...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Stefan Hägele Fabian Seguel Driton Salihu Adam Misik Eckehard Steinbach School of Computation Information and Technology Chair of Media Technology Munich Institute of Robotics and Machine Intelligence Technical University of Munich

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Understanding surface material properties is crucial for enhancing indoor robot perception and indoor digital twinning. However, not all sensor modalities typically employed for this task are capable of reliably capturing detailed surface material characteristics. By analyzing the reflected RF signal from a mmWave radar sensor, it is possible to extract information about the reflective material and its composition from a certain surface. We introduce a mmWave MIMO FMCW radar-based surface material classifier SMCNet, employing a complex-valued Convolutional Neural Network (CNN) and complex radar IQ signal input for classifying indoor surface materials. While current radar-based material estimation approaches rely on a fixed sensing distance and constrained setups, our approach incorporates a setup with multiple sensing distances. We trained SMCNet using data from three distinct distances and subsequently tested it on these distances, as well as on two more unseen distances. We reached an overall accuracy of 99.12-99.53% on our test set. Notably, range FFT pre-processing improved accuracy on unknown distances from 25.25% to 58.81% without re-training.

关键词： Accuracy RF signals Radar Robot sensing systems Radar signal processing sensors Convolutional neural networks Millimeter wave communication Speech processing Surface treatment

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Risk Probability Assessment and Prediction Based on K-means and CNN

Risk Probability Assessment and Prediction Based on K-means ...

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Digital Analysis and processing, intelligent Computation (DAPIC), International conference on

作者： Wan Wang Luran Cao Zixuan Wang Wenqi Zhang Zijian Wei China North Industries Corporation Beijing China College of Computer and Information Science School of Software Southwest University Chongqing China School of Artificial Intelligence and Software JiangSu Normal University KeWen College Xuzhou China SWUFE-UD Institute of Data Science Southwestern University of Finance and Economics Chengdu China School of Mathematics and Information Sciences Guangzhou University Guangzhou China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This paper presents a comprehensive framework for risk probability assessment and prediction, integrating K-means clustering, Critic-Topsis methodology, and Convolutional Neural networks (CNNs). Initially, a risk evaluation model is established by leveraging K-means clustering to categorize probability data, followed by selecting the top seven most relevant features for each cluster. Objective weights for these features are calculated using the Entropy Weight Method (EWM). Subsequently, the Critic-Topsis approach is utilized to compute comprehensive evaluation scores, which, when combined with EWM weights, yield risk warning values for individual events within each risk category. Furthermore, a CNN-based occurrence probability prediction model is developed, utilizing the top ten most correlated indicators and probability data. Through convolutional and pooling operations, a regression model is trained and evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Comparative analysis with Random Forest and Support Vector Machine models demonstrates the superiority of the CNN model, achieving an R 2 of 98.6% and maintaining a high R 2 of 91.5% even with five features.

关键词： Support vector machines Analytical models Accuracy Sensitivity analysis Computational modeling Soft sensors Predictive models Robustness Convolutional neural networks Random forests

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Sustainable Water Management in Precision Agriculture

Sustainable Water Management in Precision Agriculture

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Sharad Jagtap G. Sudhagar Rahul Jadhav ECE Bharath Institute of Higher Education and Research Chennai India Anantrao Pawar College of Engineering and Research Pune India AISSMS Institute of Information Technology Pune India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

This paper uses machine learning models to optimize the utilization of water in precision agriculture. The main objective is to create a dependable system that can forecast the ideal irrigation conditions, increasing sustainability and efficiency. The process entails the thoughtful placement of sensors, the creation of a wireless network of sensors (WSN), as well as the examination of a carefully selected dataset that includes vital variables like humidity, temperature, precipitation, wind speed, along with soil moisture. The dataset's integrity is guaranteed by further data pre-processing, which includes operations like cleaning, feature significance analysis, and refining. The 105,217-entry dataset provides the basis for training and assessing machine learning models, including Random Forest, the Decision Tree, and Support Vector Machines (SVM), along with Neural Network. The findings show that the Random Forest model performs better than the other models, with an astounding accuracy of 92.68%. At 91.83% accuracy, the model based on Decision Trees comes a close second. With accuracy levels of 90.78% & 90.21%, respectively, SVM as well as Neural Network models perform competitively. The Random Forest model's accuracy in forecasting ideal irrigation conditions is further supported by the precision along with F1-Score measures. This paper provides a strong foundation for environmentally friendly water use and offers insightful information about using machine learning techniques for precision agriculture.

关键词： Support vector machines Precision agriculture Temperature sensors Irrigation Wireless sensor networks Accuracy Neural networks Decision trees Sustainable development Random forests

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Research on modular unmanned detection robot based on 2-in-1 land-air-amphibious robot

Research on modular unmanned detection robot based on 2-in-1...

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IEEE information Technology and Mechatronics Engineering conference (ITOEC)

作者： Bowen Jiang Haodong Xu Junzhe Zhang Jianping Liu Hanqing Zhao Han Wang Baocong Ma Xiangke Tian School of Mechanical & Vehicle Engineering Linyi University Linyi China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

This rescue robot is designed in the context of the global rise in disasters and the development of intelligent disaster rescue. This robot is designed for unmanned detection and rescue in disaster affected areas. The study is based on the remote control detection and rescue robot composed of Windows computer and STM32F107 microcontroller with upper and lower computer. The overall structure of the robot consists of two modules: a six-football body device and an amphibious ellipsoidal device, with three types of motion: crawling, rolling and flying. The device is equipped with a variety of detection and sensing components and redundant component interfaces, and can perform a variety of tasks. The robot is amphibious, unmanned and modular in design to increase the size, safety and flexibility of exploration of the affected area.

关键词： Legged locomotion Wireless communication Wireless sensor networks Microcontrollers Disasters Wheels Robot sensing systems sensors Remote control Rescue robots

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Real-Time Monitoring of Sleep Patterns Using Wearable EEG sensors

Real-Time Monitoring of Sleep Patterns Using Wearable EEG Se...

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Automation and Computation (AUTOCOM), International conference on

作者： Girija Shankar Sahoo Sorabh Sharma Subash Chandra Tripathy Abhishek Singla V. Pushparajesh Azra Jeelani Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electrical and Electronics Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Novel approach in sleep research and monitoring: Real-time monitoring of sleep patterns using wearable EEG sensors. It works using non-invasive EEG sensors that can be placed on the head. This enables the continuous and personalized tracking and assessment of sleep patterns. This kind of detailed information on various phases associated with sleep has the possibility of dramatically changing diagnoses and management related to sleep problems. Sleep disorders are typically under diagnosed or misdiagnosed today because traditional approaches self-report or overnight sleep studies only provide limited data. These newly developed wearable EEG sensors are lightweight and comfortable, which makes the latter ideal for long-term use. They are also provided with a wireless connection to an external monitoring system to enable the real-time transfer of data. This allows a more detailed examination of sleep cycles, including total time spent in each stage, along with disruptions via apnea and somnambulism. The potential applications of this technology are fully connected to daily devices used like smart watches and fitness trackers, which could benefit more people in a wider range for self-monitoring and tracking purposes on sleeping patterns.

关键词： Wireless communication Wireless sensor networks Sleep Watches Real-time systems Electroencephalography Biomedical monitoring Monitoring Wearable sensors intelligent sensors

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Flood Forecasting System based on Water Monitoring using IoT-Machine Learning

Flood Forecasting System based on Water Monitoring using IoT...

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Trends in Material Science and Inventive Materials (ICTMIM), International conference on

作者： R. Ramya Mohamed Thanseem K Vimalraj V Vinith Kumar S Electronics and Communication Engineering K.S. Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Floods are among the most devastating natural disasters, resulting in massive loss of life, widespread property damage, and overall economic disruption Conventional flood monitoring systems, which depend on manual data gathering and old models, tend to lag behind interventions This project suggests an IoT-based flood forecasting system for real-time monitoring, precise predictions, and timely warnings. IoT sensors monitor water level, rainfall, and moisture in flooded regions and send information to a cloud platform for processing. Machine learning models analyze the data, combining hydrological, meteorological, and geographic variables to improve prediction. The system employs low-power IoT sensors with communication protocols such as LoRa WAN, Zigbee, or cellular networks for real-time monitoring. Modular and scalable, it adjusts to varied environments. Visualization interfaces and mobile/web interfaces offer decision-makers and community’s clear data and timely notifications, enabling proactive disaster management, evacuation planning, and resource allocation. Simulated and tested through real-life applications, the system proves to have efficient early flood detection, minimizing damage and enhancing resilience in limited-resource areas.

关键词： Wide area networks Accuracy Rain Biological system modeling Predictive models sensor systems Real-time systems sensors Floods Forecasting

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Analysis of Various Routing Protocols for Air Pollution Monitoring Systems in Wireless sensor networks 1st

Analysis of Various Routing Protocols for Air Pollution Moni...

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1st International conference on Innovation and Emerging Trends in Computing and information Technologies, IETCIT 2024

作者： Arzoo Kaur, Kiranbir Guru Nanak Dev University Amritsar India

ISBN: (纸本)9783031808418

This review examines an array of machine learning algorithms applied to optimize the performance of the WSN sensors concerning vital metrics such as remaining energy reserves, node operational lifespan, and overall energy usage for air pollution monitoring systems. Additionally, the review underscores the value of integrating reinforcement learning approaches and Arduino-based technologies to effectively monitor carbon dioxide, nitrogen dioxide, and ozone pollutants. The study also surveys diverse energy-efficient models like the Traffic-Balanced Energy-Efficient Zone-Based Multipath (TEZEM) method, Cluster-Based Load-Balanced Routing with Power Awareness (CLWA) algorithm, Pressure-Driven Energy-Aware Geographic Forwarding (PEG-GAS), and intelligent Energy-Efficient LEACH (IEE-LEACH) protocol, comparing their potencies based on residual battery power and active component counts. Furthermore, potential real-world deployments within an urban environment in Tasikmalaya, Indonesia, are examined, underscoring the advantages of developing air quality benchmarks and wireless sensor network detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Kyoto Protocol

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Natural Language processing in Geographic information Systems

Natural Language Processing in Geographic Information System...

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intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Kushmitha S. Jaspher W. Kathrine Abisha M. B. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The integration of Geographic information Systems (GIS) with Natural Language processing (NLP) has transformed how spatial data is accessed and utilized. By enabling GIS interfaces to interpret natural language queries, NLP enhances user experience, democratizes geospatial data, and improves query accuracy. This paper reviews state-of-the-art methodologies for geo-parsing, geo-coding, and spatial query interpretation, addressing challenges like geographic ambiguity, imprecise spatial relationships, and scalability limitations. Key contributions include a systematic literature review identifying research gaps such as insufficient contextawareness and a lack of annotated datasets, alongside case studies demonstrating the benefits of NLP integration in applications like disaster management and urban planning. Future opportunities are explored in leveraging deep learning, multimodal data integration, and adaptive user interfaces to further enhance GIS accessibility and usability. Future opportunities are explored in leveraging deep learning for spatial reasoning, integrating multimodal data sources for enhanced decision-making, and developing adaptive user interfaces tailored for diverse audiences. Furthermore, the paper highlights the potential of NLP in generating geodescriptions for distributed sensor networks, supporting accessibility for impaired users, and enhancing real-time data analysis in smart city initiatives. Addressing these gaps and building scalable frameworks will pave the way for GIS systems that are not only powerful but also inclusive, fostering global accessibility and broader adoption of geospatial technologies.

关键词： Deep learning Visualization Scalability Urban planning Disaster management User interfaces Natural language processing Spatial databases Geospatial analysis Usability

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