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AGRIFOG: Fog-Assisted IOT Enabled Agriculture Monitoring System

AGRIFOG: Fog-Assisted IOT Enabled Agriculture Monitoring Sys...

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Social and Sustainable Innovations in Technology and Engineering (SASI-ITE), International conference on

作者： Prashanth Ragam Hemanth Katikala Muniraj Parvathi Primilla Ch. Lakshmi Pranathi K Kavinesh Ajith Jubilson E Vellore Institute Of Technology Amaravathi Andhra Pradesh India

ISBN: (数字)9798350360790

ISBN: (纸本)9798350360806

In response to the evolving landscape of agriculture, this report introduces a Fog-enabled IoT agriculture monitoring system aimed at transforming precision farming. The system’s architecture integrates an ESP32 microcontroller interfacing with soil moisture and DHT11 sensors, establishing a resilient network for real-time data acquisition and transmission. Leveraging MQTT communication protocols, the ESP32 seamlessly interacts with a Raspberry Pi Fog node, enabling local real-time analysis and control actions. The integration with the Google Drive API ensures secure cloud storage and periodic synchronization, facilitating historical analysis and visualization. The system’s scalability and affordability are demonstrated through ESP32 firmware development using the Arduino IDE, presenting a practical solution for widespread adoption. The assessment of the system’s performance involved evaluating latency and traffic metrics. With a recorded latency of 0.393 milliseconds, the system exhibits remarkable responsiveness to requests, ensuring efficient interactions with the Google Drive API. Simultaneously, the traffic rate of 0.131 requests per second indicates a moderate level of activity between the application and the cloud platform, underscoring the system’s efficiency in handling data interactions. This Fog-enabled IoT agriculture monitoring system addresses inherent flaws in traditional IoT applications. By incorporating Fog computing, the system overcomes latency challenges, ensuring real-time responsiveness crucial for precision farming. Field tests have substantiated the system’s efficacy in providing timely and reliable data for informed decision-making in agricultural practices. Future directions for system enhancement include the incorporation of advanced machine learning techniques and additional sensors such as $\mathbf{p H}$, light intensity, NPK, and airflow sensors. Thes additions aim to broaden the range of monitored parameters offering nuanced insights

关键词： Cloud computing Protocols Microcontrollers Soil moisture Real-time systems sensor systems sensors

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Adaptive Machine learning: A Framework for Active Malware Detection

Adaptive Machine learning: A Framework for Active Malware De...

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International conference on Mobile Ad-hoc and sensor Networks, MSN

作者： Muhammad Aslam Dengpan Ye Muhammad Hanif Muhammad Asad Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cybersecurity and Engineering Wuhan University Wuhan China COMSATS University Islamabad Wah Campus Wahcantt Pakistan Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

applications of Machine Learning (ML) algorithms in cybersecurity provide significant performance enhancement over traditional rule-based algorithms. These intelligent cyber-security solutions demand careful integration of the learning algorithms to develop a significant cyber incident detection system to formulate security analysts' industrial level. The development of advanced malware programs poses a critical threat to cybersecurity systems. Hence, an efficient, robust, and scalable malware recognition module is essential for every cybersecurity product. Conventional Signature-based methods struggle in terms of robustness and effectiveness during malware detection, specifically in the case of zero-day and polymorphic viruses attacks. In this paper, we design an adaptive Machine Learning based active malware detection framework which provides a cybersecurity solution against phishing attacks. The proposed framework utilize ML algorithms in a multilayered feed-forwarding approach to successfully detect the malware by examining the static features of the web pages. The proposed framework successfully extracts the features from the web pages and performs a successful detection process for the phishing attack. In the multilayered feed-forwarding framework, the first layer utilizes Random Forest (RF), Support Vector Machine (SVN), and K-Nearest Neighbor (K-NN) classifiers to build a model for detecting malware from the real-time input. The output of the first layer passes to the Ensemble Voting (EV) algorithm, which accumulates earlier classifiers' performance. At the third layer, adaptive frameworks investigate second layer input data and formulate the phishing detection model. We analyze the proposed framework's performance on three different phishing datasets and validate the higher accuracy rate.

关键词： Adaptation models Machine learning algorithms Adaptive systems Phishing Web pages Feature extraction Malware

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An Efficient and Secure Framework for Smart Healthcare Using IoT and Machine Learning

An Efficient and Secure Framework for Smart Healthcare Using...

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Decision Aid Sciences and Application (DASA), International conference on

作者： Vivek Kumar Pandey Shiv Prakash Tarun Kumar Gupta Vandana Rathore Abhishek Singh Tiansheng Yang Rajkumar Singh Rathore Department of Electronics and Communication University of Allahabad Prayagraj India Department of Electronics and Communication University of Allahabad Prayagraj Uttar Pradesh India Department of Computer Science Miranda House University of Delhi Delhi India Institute of Engineering & Technology Dr. A.P. J. Abdul Kalam Technical University Lucknow India University of South Wales Pontypridd United Kingdom Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The healthcare systems worldwide are challenged with the increased demand for rapid and efficient patient care. In this context, an advanced solution is required to meet the growing needs. This paper presents an intelligent healthcare framework integrating IoT devices with machine learning algorithms to enable real-time predictive patient monitoring. It comprises a four-layer architecture: data acquisition from IoT sensors, data preprocessing, analytics for predictive modeling, and an application layer for user interactions. Validated with data from the MIMIC-III and PhysioNet datasets along with 12 months of IoT sensor data from a university medical center, the framework achieved 96% prediction accuracy with precision, recall, and ROC AUC scores of 0.95, 0.96, and 0.98, respectively. The system had a great response time of 200 milliseconds. It has greatly improved early detection by 36.9% and reduced false alarms by 68%. False alarms decreased from 25% to 8%, and response time was reduced from 45 to 12 minutes, or 73.3 %. Resource utilization analysis reflects optimal performance since the analytics layer hit a spike at 45% CPU utilization, thereby pointing out optimal load management. Patient satisfaction also rose to 22.7%, increasing from 75% to 92%, reflecting the framework's impact on care quality. All these results render the framework an effective and scalable solution that optimizes healthcare resources while improving care for the patient.

关键词： Patient monitoring Accuracy Machine learning algorithms Medical services Predictive models Real-time systems Internet of Things Time factors Resource management Random forests

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Surveillance and Intruder Detection using Pyramid Vision Transformer You Only Look Once Enhanced with Gradient Weighted Class Activation Mapping

Surveillance and Intruder Detection using Pyramid Vision Tra...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International conference on

作者： Sripriya Arunachalam Muntather Almusawi Shaik. Rafikiran S. Sivagami Hirald Dwaraka Praveena Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Vadapalani City Campus Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electrical & Electronics Engineering Sri Venkateswara College of Engineering Tirupati India Department of Chemistry New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University (Erstwhile Sree Vidya nikethan Engineering College) Tirupati India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In this era, wireless networks are economically feasible and flexible to establish in healthcare, defense, surveillance, and traffic monitoring, fire detection. Effective intruder detection systems utilize advanced technologies like Artificial Intelligence (AI), sensors, and cameras. Traditional approaches for surveillance and intruder detection had faced several challenges which include false alarms, high costs, privacy concerns, and vulnerabilities. To overcome these challenges, a Gradient-weighted Class Activation Mapping (Grad CAM) with Pyramid Vision Transformer-You Only Look Once Enhanced (PViT-YOLOE) namely GRADCAM-PViT-YOLOE is proposed to detect intruders in public places using images and videos. For preprocessing, noise reduction, image resizing, and image segmentation are considered to reduce false positives, enhance memory efficiency, and to reduce complexity. Then, PViT-YOLOE is considered for feature extraction to enhance detection of small objects, enhance robustness to variations. At last, Grad CAM is employed as classifier to detect intruders in public places with improved interpretability, robustness to noise, and flexibility. The results demonstrate the efficacy of the proposed GRADCAM-PViT-YOLOE in terms of accuracy (99.25 % ), precision (96.75 % ) recall (89.82 % ) and F1-Score (87.92 % ) when compared to the existing method like Faster Regions Convolutional Neural Networks (FR-CNN).

关键词： Image segmentation Accuracy Noise reduction Noise Memory management Transformers Feature extraction Robustness sensor systems Videos

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RASS: A real-time, accurate and scalable system for tracking transceiver-free objects

RASS: A real-time, accurate and scalable system for tracking...

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IEEE International conference on Pervasive Computing and Communications (PerCom)

作者： Dian Zhang Yunhuai Liu Lionel M. Ni College of Software Shenzhen University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China Shenzhen Institutes of Advanced Technology Chinese Academy and Sciences China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University China

Transceiver-free object tracking is to trace a moving object without carrying any communication device in an environment where the environment is pre-deployed with some monitoring nodes. Among all the tracking technologies, RF-based technology is an emerging research field facing many challenges. Although we proposed the original idea, until now there is no method achieving scalability without sacrificing latency and accuracy. In this paper, we put forward a real-time tracking system RASS, which can achieve this goal and is promising in the applications like the safeguard system. Our basic idea is to divide the tracking field into different areas, with adjacent areas using different communication channels. So the interference among different areas can be prevented. For each area, three communicating nodes are deployed on the ceiling as a regular triangle to monitor this area. In each triangle area, we use a Support Vector Regression (SVR) model to locate the object. This model simulates the relationship between the signal dynamics caused by the object and the object position. It not only considers the ideal case of signal dynamics caused by the object, but also utilizes their irregular information. As a result it can reach the tracking accuracy to around 1m by just using three nodes in a triangle area with 4m in each side. The experiments show that the tracking latency of the proposed RASS system is bounded by only about 0.26s. Our system scales well to a large deployment field without sacrificing the latency and accuracy.

关键词： Accuracy Wireless communication Wireless sensor networks Interference Lead Synchronization Predictive models

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Optimization of Energy Demand Management in Cold Storage systems: Advances through the Integration of IoT, Machine Learning, and AI

Optimization of Energy Demand Management in Cold Storage Sys...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Robinson Salgado Gaddiel Alvarado Luis Loo Maestría en Automatización Industrial Universidad Tecnológica de Honduras Tegucigalpa Honduras Escuela de Postgrado Universidad Tecnológica de Honduras San Pedro Sula Honduras

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Cold storage facilities play a crucial role in preserving perishable products across various industries, including food, pharmaceuticals, and agriculture. The high energy consumption of refrigeration systems in these facilities necessitates efficient energy management. This article uses Long Short-Term Memory (LSTM) models in a novel way to predict temperature and power demand variations in cold storage systems, leading to more accurate forecasts. The research improves demand response applications and achieves significant energy savings through real-world deployment by combining real-time IoT sensor data and deep learning. The study provides scalable framework that can be applied to a variety of industrial settings, helping to promote more sustainable energy management practices by optimizing power usage and lowering operational costs. Various methodologies and models were evaluated to predict and manage temperature variations and energy demand efficiently. The results demonstrate significant enhancements in energy efficiency, reduced energy consumption, and optimized resource usage. Among the evaluated machine learning techniques, deep neural networks, specifically Long Short-Term Memory (LSTM) models, showed superior accuracy and responsiveness. Mini-ROCKET and DrCIF, along with their precision and lower mean absolute error (MAE), make reliable predictions possible with a MAPE below 0.2 having overall accuracy of this model is 96%. Additionally, the adaptability of these models to abrupt operational changes was analyzed, highlighting their advantages and limitations. The paper concludes with recommendations for future research and development, promoting the adoption of advanced and sustainable technologies in energy management for cold storage systems.

关键词： Energy consumption Adaptation models Accuracy Costs Time series analysis Machine learning Predictive models Energy efficiency Internet of Things Long short term memory

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AI-Enhanced Unmanned Aerial Vehicles for Search and Rescue Operations

AI-Enhanced Unmanned Aerial Vehicles for Search and Rescue O...

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Innovative Trends in Information Technology (ICITIIT), International conference on

作者： K Rashida Farsath K Jitha V.K Mohammed Marwan A Muhammed Ali Jouhar K.P Muhammed Farseen K.A Musrifa Department of Computer Science & Engineering MEA Engineering College

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

This paper introduces a cutting-edge AI-empowered Unmanned Aerial Vehicle (UAV) system, enriched with state-of-the-art sensor technology, advanced image recognition algorithms, and autonomous navigation capabilities. The system represents a transformative approach to search and rescue operations, offering unparalleled precision and rapid response times. Our methodology encompasses multifaceted data collection techniques, including surveys, interviews, data mining, Internet of Things (IoT) sensors, and sophisticated video analytics. Machine learning and deep learning models are then applied to process and analyze this data, enabling real-time image recognition for precise target identification. The system’s AI-driven autonomous navigation algorithms optimize mission planning, resulting in significantly reduced response times and heightened mission success rates. Extensive real-world tests and simulations validate the exceptional performance of the proposed AI-empowered UAV system. These tests underscore its capacity to expedite emergency response efforts in dynamic and challenging environments. In parallel, this paper addresses critical ethical considerations, em-emphasizing responsible data handling practices, and robust security measures to ensure the system’s integrity in sensitive contexts. As exemplified through a compelling case study of successful rescue operations, this technology represents a groundbreaking advancement in the field. By bridging the gap between cutting- edge technology and life-saving applications, it holds the potential to redefine the landscape of search and rescue missions, ushering in an era of heightened efficiency, precision, and impact.

关键词： Image recognition Machine learning algorithms Heuristic algorithms Autonomous aerial vehicles Emergency services Real-time systems Time factors

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Predictive Maintenance in IoT Networks Using a Temporal Attention Mechanism Based Federated Convolutional Neural Network

Predictive Maintenance in IoT Networks Using a Temporal Atte...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Naga Venkata Ramakrishna Guduri R.M. Dilip Charaan Kalaiyarasi R Samuthira Pandi V D.Laxma Reddy Selvaprabhu. T Faculty of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad India Department of CSE Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

In the era of Industry 4.0, edge devices in industrial settings generate vast amounts of data crucial for process optimization and Predictive Maintenance (PM). Traditional cloud-based approaches for handling this data are becoming untenable due to cost, latency, and privacy issues. Federated learning emerges as a viable solution, offering localized and efficient data processing while mitigating these challenges. This paper presents a Federated Convolutional Neural Network with Temporal Attention Mechanism (FedCNN-TAM) for PM tasks in Internet of Things (IoT) networks. This model leverages temporal features in sensor data to enhance predictive accuracy. Evaluated on the CMAPSS dataset, FedCNN-TAM outperforms representative models with a significant performance gain. This model exhibits the potential for broader applications in industrial IoT, setting the stage for future research on its scalability and adaptability to multi-modal data types.

关键词： Adaptation models Temperature distribution Technological innovation Attention mechanisms Accuracy Predictive models Data models Real-time systems Convolutional neural networks Predictive maintenance

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Sensitivity of the distributed optical fiber sensor to measure soil movement for landslide monitoring application

Sensitivity of the distributed optical fiber sensor to measu...

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Student conference on Research and Development (SCOReD)

作者： Abdulwahhab Essa Hamzah Tan Wui Kin Mohd Saiful Dzulkefly Zan Nor Shahidah Mohd Nazer Hisham Mohamad Yosuke Tanaka Mustafa Essa Hamzah Ahmad Ashrif A. Bakar Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Photonics Technology Laboratory Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia Department of Communications Engineering Iraq University College Basrah Iraq Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia (UKM) UKM Bangi Selangor Malaysia Civil & Environmental Engineering Department Universiti Teknologi PETRONAS seri Iskandar Perak Malaysia Division of Advanced Electrical and Electronic Engineering Institute of Engineering Tokyo University of Agriculture and Technology Japan

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Landslides are one of the major global geohazards, contributing to significant economic and social losses to private and public properties. In this paper, we deployed a Brillouin optical time-domain analysis (BOTDA) interrogator to measure soil movement and identify the soil movement location. The method was analyzed experimentally, detecting the soil movement location at around 935 m to 955 m of fiber. In addition, the system’s sensitivity was analyzed for soil movement within the displacement range of 0 mm to $\mathbf{4 0 \mathrm { mm }}$, with increments of 5 mm. The experimental result shows that the frequency shift of 1.66 MHz corresponds to 1 mm, and the tensile strain of $31.4 \mu \varepsilon$ corresponds to 1 mm. Moreover, the high reliability of measurement accuracy, demonstrated by an average of nine measurements with an error of 2.76 MHz, further supports the effectiveness of the system for detecting and monitoring soil movement in real-world applications.

关键词： Optical fibers Optical fiber sensors Accuracy Sensitivity Soil measurements Scattering Soil Optical variables measurement Motion measurement Monitoring

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RF Sensing-enabled Interference Mitigation for Wi-Fi-based IoT systems: A Deep Learning Approach

RF Sensing-enabled Interference Mitigation for Wi-Fi-based I...

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IEEE conference on Communications and Network Security (CNS)

作者： ChunChih Lin Gen Li Xiaonan Zhang Linke Guo The Holcombe Department of Electrical and Computer Engineering Clemson University Department of Computer Science Florida State University

ISBN: (数字)9798350375961

ISBN: (纸本)9798350375978

The proliferation of the Internet of Things (IoT) over the 2.4GHz has transformed the way people interact with the world in their daily lives. For emerging applications requiring diverse types of data, using IoT devices from different protocols, such as Wi-Fi, Bluetooth, and ZigBee, for data collection will help fulfill designated objectives. Hence, the increasing use of these IoT devices will form a heterogeneous environment with inevitable wireless coexistence. Due to the dynamic IoT environment, the wireless transmission among heterogeneous IoT devices becomes unknown and notoriously hard to manage, where the network performance is always compromised. This paper leverages the RF sensing information to develop a deep learning-based framework for mitigating the cross-technology interference of the Wi-Fi-based IoT system. Each IoT device can provide detailed Channel State Information (CSI) of the transmission link, by which the gateway uses Generative Adversarial Networks (GAN) for building a detailed environmental RF map. Then, we propose a CSI-inspired gateway topology management strategy to find the optimal gateway location, anticipating reaching the highest network throughput. To enhance the efficiency of the proposed scheme, we also adopt the advanced differentiable augmentation approach to reduce the training data size for gateway deployment. Extensive experimental results have demonstrated both the feasibility and efficiency of our design.

关键词： Wireless communication Radio frequency Training Wireless sensor networks Prevention and mitigation Zigbee Logic gates Throughput sensors Internet of Things

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