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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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Ceramic-Based High-Q Retroreflectors for Sub-mm Localization in High-Temperature Environments

Ceramic-Based High-Q Retroreflectors for Sub-mm Localization...

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International Workshop on Mobile Terahertz systems (IWMTS)

作者： Alejandro Jiménez-Sáez Masoud Sakaki Jesús Sánchez-Pastor Martin Schüßler Rolf Jakoby Niels Benson Technical University of Darmstadt Darmstadt Germany University of Duisburg-Essen Duisburg Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

This scientific paper is an outcome of the MARIE project (for more information, see ***) C09. This research addresses the development of innovative frequency-coded tags for localization, sensing, and ranging in harsh environments. The study focuses on enhancing orientation-insensitive THz retroreflectors through advanced approaches, including multi-notch high Radar Cross-Section (RCS) Corner Reflectors (CR) and Quasi Conformal Transformation Optics (QCTO)-enabled designs. Core innovations involve the integration of Luneburg lenses with Photonic Crystal (PhC)-based coding particles, fabricated using advanced materials such as Deep Reactive Ion Etched (DRIE) High-Resistive Silicon (HR-Si) and Lithography-based Ceramic Additive Manufacturing (LCM) with Alumina (Al₂O₃). These designs enable high-Q resonators for effective signal detection in extreme conditions up to 1200 °C, as in an indoor fire scenario. Achievements include wide-field-of-view (FoV) retroreflectors, frequency-coded lens reflectors, and wireless readouts capable of sub-mm localization. Future work will explore multi-material additive manufacturing and Liquid Crystal (LC)-based Reconfigurable Intelligent Surfaces (RIS) to advance state-of-the-art localization systems for autonomous rescue applications. These findings establish a foundation for robust, high-temperature sensor fusion technologies.

关键词： Location awareness Wireless communication Wireless sensor networks Ultraviolet sources Reconfigurable intelligent surfaces Three-dimensional printing sensors Retroreflectors Resonators Lenses

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AI-Powered Soil Stability Assessment and Landslide Forecasting with Multi-sensor Networks, Edge Computing, and Jetson Nano Integration

AI-Powered Soil Stability Assessment and Landslide Forecasti...

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

作者： N. Muthukumaran A. Vignesh S. Aruna M. Jenish T. Vinoth Kumar Shanthi Madasamy Department of ECE Sri Eshwar College of Engineering Coimbatore India Department of EEE Dr. N.G.P Institute of Technology Coimbatore India Department of EEE RVS College of Engineering and Technology Coimbatore India Department of EEE JCT College Of Engineering And Technology Coimbatore India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Landslides pose a severe threat to mountainous regions, often resulting in catastrophic damage and loss of life. Existing prediction methods rely on limited data sources and infrequent monitoring, leading to delays in early warnings. Previous technologies have explored individual sensor applications, such as Ground-Penetrating Radar (GPR) and seismic sensors, but lack a comprehensive multi-sensor approach with real-time processing. Notable cases, like the Wayanad landslides in Kerala, India, highlight the urgent need for advanced predictive systems. This study proposes an AI-driven landslide prediction system using a network of 40 sensor-equipped slave kits. Each kit integrates GPR, seismic, Electromagnetic Induction (EMI), ultrasonic, LiDAR, capacitance probes, gravimeters, Electrical Resistivity Tomography (ERT), and magnetometers onto a Teensy 4.1 microcontroller with an ESP8266 for wireless data transmission. Data is processed at a central master unit featuring a Jetson Nano module, employing the ALEXNET+BILSTM model for real-time analysis of soil stability and landslide risk. By leveraging edge computing, this system enables rapid decision-making and early warnings, addressing the limitations of existing methods. Its scalable, cost-effective architecture enhances environmental monitoring, offering a robust solution for landslide-prone communities and significantly improving disaster preparedness.

关键词： Wireless communication Landslides Wireless sensor networks Soft sensors Soil Tomography Stability analysis Real-time systems Terrain factors Edge computing

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GIIDS: Generalized Intelligent Intrusion Detection System for Heterogeneous UAVs in UAM

GIIDS: Generalized Intelligent Intrusion Detection System fo...

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International conference on advanced Communication Technology (ICACT)

作者： Fahmina Kabir Nishat I Mowla Inshil Doh Division of Artificial Intelligence and Software Ewha Womans University Seoul Korea Department of Industrial Systems RISE Research Institutes of Sweden Sundsvall Sweden Department of Cyber Security Ewha Womans University Seoul Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

Unmanned Aerial Vehicles (UAVs) are increasingly integral in various sectors, simultaneously encountering rising security threats as UAV and Urban Air Mobility (UAM) networks continue to expand. This paper addresses the challenge of securing UAM networks while also emphasizing generalizability of the security solution to protect heterogeneous UAVs against threats that compromise their stability, reliability and can cause catastrophic failures such as a crash landing. The deployment of traditional machine learning (ML) based intrusion detection systems (IDSs) is often hampered in real-world applications due to a lack of generalizability of the security solution. As a result, the system fails to provide adequate security across the varying models and platforms of UAVs, each with its unique statistical properties and data distributions. To address these challenges, we focus on employing a comprehensive set of UAV sensor parameters, tailored feature engineering and selection to develop multi-stage cross-validated ensemble learning systems to facilitate generalized detection of attack and non-attack cases. For additional analysis, we cross-validate the models using two different cross-validation techniques. The proposed stacking ensemble systems provide the overall best performance, with AUC within the range of 92% to 100% across different cross-validations.

关键词： Analytical models Scalability Stacking Intrusion detection Performance gain Autonomous aerial vehicles Robustness Data models Stability analysis Thermal stability

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Advancements in Weather Monitoring System: A Comprehensive Review

Advancements in Weather Monitoring System: A Comprehensive R...

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International conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Manjyot Singh Soni Mitra Jisan Bera Kaustav Rajib Ghosh Institute of Engineering and Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This review provides an in-depth analysis of recent advancements and key trends in weather monitoring systems. It emphasizes the progress made in technologies and highlights their significant contributions to the field. These systems are essential tools for collecting and interpreting atmospheric data to predict and monitor weather conditions, supporting applications such as disaster management, agriculture, aviation, and climate research. The review investigates modern sensor innovations, IoT-enabled systems, and remote sensing technologies that have transformed real-time data collection and analysis. It discusses advancements in performance factors, including accuracy, scalability, energy efficiency, and costeffectiveness. Furthermore, the paper explores advanced topics like machine learning-driven weather prediction, satellite-based observation systems, and the integration of cloud computing for improved data processing capabilities. Challenges such as hardware constraints, data reliability, and integration complexities are also examined, along with potential strategies to overcome them. By synthesizing existing research, this study offers valuable insights for professionals in the field, helping them enhance and innovate weather monitoring systems. The findings provide overview of present state-of-the-art technologies and propose directions for future research and development, emphasizing the importance of this field in addressing global challenges.

关键词： Cloud computing Technological innovation Accuracy Reviews Weather forecasting Real-time systems Hazards Forecasting Monitoring Remote sensing

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Generative Adversarial Network based Digital Predistortion Linearization for 6G Joint Communications and Sensing

Generative Adversarial Network based Digital Predistortion L...

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IEEE Topical conference on Power Amplifiers for Wireless and Radio applications (PAWR)

作者： Kevin Chuang Muhammad Saad Zia Weite Zhang Communications & Cloud Analog Devices Inc Wilmington MA USA Central AI Analog Devices Inc Boston MA USA

ISBN: (数字)9798331507275

ISBN: (纸本)9798331507282

advanced digital predistortion (DPD) linearization systems have been the cornerstone of successful and energy-efficient base station deployments, particularly when the radio frequency power amplifier (PA) is pushed to operate near its maximum power ratings to achieve high wireless capacity and provide new service features. In this work, we explore how machine learning (ML) techniques can be effectively incorporated into the design methodology of PA linearization for the emerging 6G use case, namely joint communications and sensing (JCAS). Our experimental results indicate that adopting a generative adversarial network for ML-based DPD model parameters extraction consistently yields better adjacent channel leakage power ratios and normalized mean square errors for different JCAS waveforms compared to the adaptive moment estimation method using a mean squared error loss function. Moreover, this improvement is achieved using less training data, resulting in shorter convergence times to reach a certain level of linearity performance.

关键词： 6G mobile communication Wireless communication Adaptation models Wireless sensor networks Adaptive systems Linearity Generative adversarial networks Predistortion sensors Parameter extraction

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Affordable IoT Security: A Raspberry Pi System for Attendance and Intruder Detection

Affordable IoT Security: A Raspberry Pi System for Attendanc...

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AIML-applications for Engineering & Technology (ICAET), International conference on

作者： Karra Shivam Sharma Ashish G. Hallur Shreya Venkatesh G. Sumathi Sujatha Rajkumar E. Konguvel School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This paper introduces an innovative and budget-friendly IoT-based facial recognition system tailored for attendance tracking and intruder detection, leveraging the power of Raspberry Pi. By utilizing the Local Binary Pattern Histogram (LBPH) algorithm, the system achieves remarkable accuracy and quick response times, outperforming traditional methods like Fisher-Face. It integrates real-time face detection with instant alerts to bolster security measures. The system not only automates attendance records, updating them in real time and sending them via email, but also identifies unrecognized individuals, capturing their images and notifying authorities promptly. Designed with cost efficiency in mind, it operates on low power and uses readily available components, such as an external camera and a PIR sensor for motion detection. This practical solution is particularly well-suited for use in hostels, schools, industrial facilities, and other environments requiring secure, contactless systems. Future plans for enhancement include incorporating advanced features like liveness detection, expanding the dataset for higher accuracy, and upgrading the hardware for improved portability and seamless IoT integration. These upgrades aim to position the system as a cutting-edge tool for modern surveillance needs.

关键词： Training Histograms Accuracy Face recognition Surveillance Transforms Cameras Feature extraction Real-time systems Hardware

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ML Enhanced Autonomous Night Patrol Robot for Intelligent Security

ML Enhanced Autonomous Night Patrol Robot for Intelligent Se...

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

作者： L.Raja Ganeshkumar.A Abinesh.K Aswin.V Hariharan.S Department of ECE Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The ML-Enhanced Autonomous Night Patrol Robot discussed here is designed to enhance security in any environment when carrying out operations during the night. Using advanced sensor technologies such as LiDAR, ultrasonic sensors, and night vision cameras combined with machine learning algorithms, the robot can navigate complex terrain independently and correctly detect and classify any danger in real-time. Through the utilization of deep learning models, the surveillance of the robot is enhanced as it can recognize objects and adapt to dynamic security conditions. Edge computing also facilitates easy management of data in real-time and interaction with security personnel, which ensures timely incident responses. Overall, the proposed robot provides an extensible solution for smart security applications, significantly enhances security efficiency, and reduces reliance on human patrols.

关键词： Night vision Materials science and technology Navigation Microcontrollers Surveillance Robot vision systems Cameras Real-time systems Security Personnel

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1.2 From Chips to Thoughts: Building Physical Intelligence into Robotic systems

1.2 From Chips to Thoughts: Building Physical Intelligence i...

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IEEE International conference on Solid-State Circuits (ISSCC)

作者： Daniela Rus CSAIL Massachusetts Institute of Technology Cambridge MA

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

The rapid growth of AI technologies has brought unprecedented advancements across numerous domains, from healthcare to autonomous systems, yet this progress has been accompanied by substantial energy demands. Generative AI has revolutionized the accessibility of advanced machine learning models, putting powerful AI capabilities directly into the hands of everyday users. Tools that generate text, images, video, and audio have made AI more democratic, fueling creativity, productivity, and innovation across industries. This explosion of generative AI applications, from chatbots to art generators, has transformed AI from a niche tool into an indispensable asset in our digital lives, as many can now access these technologies through their phones or computers. However, this widespread accessibility comes with significant trade-offs, particularly in terms of the energy required to train and deploy these enormous models. Large generative models such as GPT, DALL-E, and other multi-modal AI systems demand immense computational resources, translating to high energy consumption both during the extensive training process and when they are used in practice.

关键词： Energy consumption Technological innovation Liquids Generative AI Computational modeling Energy efficiency State-space methods Smart grids Artificial intelligence sensor arrays

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Enhanced Clustering Framework for Unveiling Hidden Patterns in IoT-Based Structured and Unstructured Data

Enhanced Clustering Framework for Unveiling Hidden Patterns ...

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International Symposium on advanced Computing and Communication (ISACC)

作者： Prabhat Das Karthik Kovuri Sajal Saha Department of IT The Assam Kaziranga University Department of CSE Adamas University Department of CSE The Assam Kaziranga University

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Clustering algorithms play a pivotal role in uncovering hidden patterns and relationships within diverse datasets. This study discusses about an enhanced real-time clustering approach based on the Birch methodology, optimized for analyzing both structured and unstructured data. Structured data, consisting of IoT sensor readings, and unstructured image data, captured using Raspberry Pi devices deployed in various locations, were analyzed to identify meaningful patterns. For structured data, the algorithm effectively clustered environmental parameters, revealing correlations and anomalies. For unstructured data, high-dimensional image features extracted using a pre-trained VGG16 model were clustered, uncovering patterns in texture, color, and structure. Experimental results demonstrate the algorithm’s scalability and efficiency, achieving high-quality clusters with average Silhouette Scores of 0.87 and 0.81 for structured and unstructured datasets, respectively. These findings highlight the algorithm’s adaptability and its potential for real-time analytics in diverse applications such as environmental monitoring and image-based decision-making systems.

关键词： Visualization Temperature distribution Correlation Image color analysis Scalability Clustering algorithms Feature extraction Real-time systems Environmental monitoring Object recognition

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