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Integrated IoT based Smart Agriculture Monitoring System 6

Integrated IoT based Smart Agriculture Monitoring System

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Saraswathi, R. Vijaya Rahul, L. Lokeswar, S. Varma, R. Vathsav Ajay, U. Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331522667

The adoption of IoT technology in agriculture is revolutionizing traditional farming, making it a data-driven and resource-efficient field. Proposed paper introduces an IoT-based smart agriculture monitoring system designed to provide farmers with accurate real-time insights for improved crop and resource management. The system incorporates a network of sensors to monitor essential environmental variables like soil moisture and temperature. These signals are processed by a microcontroller and securely transmitted to a cloud platform for advanced analytics. A mobile-friendly application offers actionable insights, notifications, and remote access, facilitating informed decision-making. Built on a Wi-Fi-based infrastructure, the system ensures reliable communication even in areas with inconsistent internet connectivity. Field implementations reveal notable advancements in resource utilization, crop yields, and labor efficiency. Additionally, an embedded AI module forecasts crop prices with 92% accuracy, enabling farmers to optimize sale timings for increased profitability in competitive markets. This comprehensive solution highlights the transformative potential of IoT in fostering sustainable and profitable agricultural practices. © 2025 IEEE.

关键词： Smart agriculture

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Blockchain-Enabled Intelligent Vehicle communication Systems: Trust Bit Rewards and Clustering for Autonomous Vehicles 3

Blockchain-Enabled Intelligent Vehicle Communication Systems...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Nirmala, P. Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331509675

In the absence of a permanent infrastructure, the networks are utilized for temporary events, military operations, and disaster recovery. A Blockchain-Enabled Intelligent Vehicle communication Systems: Trust Bit Rewards and Clustering for Autonomous Vehicles (BIVC-CA), an algorithm designed to ensure secure communication between devices and optimize efficient routing in intelligent vehicle systems, is also used to provide quick emergency response. Using blockchain, each vehicle is given its own Bit Trust ID to track its past data and calculate its trust levels. This system ensures transparency and trust between vehicles through cryptographic processes such as key generation, secure message encryption, and message verification, which improve overall communication security. It also includes an Emergency Vehicle communication System (EUC) that uses OBU devices and sensors to detect collisions and send GSM-based rescue messages, greatly improving road safety and the efficiency of emergency operations. The road-based clustering model optimizes route selection considering traffic conditions and distances and organizes vehicles into clusters to ensure effective communication and coordination. Using the path distance algorithm, clustering is achieved by computing the similarity of routes and grouping accordingly for efficient route management with minimal network congestion. In addition, the model introduces a comprehensive network architecture that integrates vehicle cloud technology and blockchain technology, enabling intelligent vehicles to make quick decisions based on real-time data. With these integrated components, the BIVC-CA model provides a reliable framework for secure data transmission, intelligent routing, and rapid emergency response. This framework will ultimately improve the efficiency of intelligent vehicle systems and contribute to the development of advanced vehicle networks and smarter transportation solutions. We calculated the results with routing expe

关键词： Blockchain

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Automatic Railway Gate Crossing Control And Track Crack Detection System using IoT 6

Automatic Railway Gate Crossing Control And Track Crack Dete...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Karuppusamy, P. Subiksha, A. Gayathri, J. Dhanush, A. Kavin, S. Department of EEE Erode Sengunthar Engineering College Erode India

ISBN: (纸本)9798331522667

Railways provide the economical and most convenient mode of passenger transport for both long-distance and suburban travel. Additionally, a large portion of transportation in India relies on the railway network. However, accidents remain a major concern, particularly with railway track crossings and unidentified cracks in railway tracks. Approximately 60% of accidents occur at railway track crossings or are caused by cracks in railway tracks, leading to the loss of precious lives and economic setbacks. Therefore, there is a need to consider new technology that is robust, efficient, and reliable for both automatic gate closure systems and crack detection in railway tracks. In this research, for the automatic gate control system, two Infrared (IR) sensors are used to detect the entry and exit of the train. Using the IR data, a Servo Motor is employed to open and close the railway gate. Next, for railway crack detection, a novel Artificial Intelligence (AI) model is designed using the DenseNet, with a residual layer added at the end. The global and local features obtained from the DenseNet and residual layer are combined and passed to a classification layer for crack detection. This model shows excellent results in crack detection, leading to its deployment on the cloud. Crack images are captured by a camera and sent to the cloud along with latitude and longitude details using the Global Positioning System (GPS). If a crack is detected, the Global System for Mobile communication (GSM) module is activated, sending an alert message with location details to the concerned person. Firebase cloud is used for data visualization and AI model deployment. By integrating AI and Internet of Things (IoT) technology, the proposed methodology enhances the safety of the railway system and helps prevent accidents. © 2025 IEEE.

关键词： Railroad accidents

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A distributed machine learning dynamic remote proof scheme that resists collusion attacks 4

A distributed machine learning dynamic remote proof scheme t...

引用

4th international conference on Network communication and Information Security, ICNCIS 2024

作者： Wang, Ke Wu, Qiuxin Qin, Yu School of Applied Science Beijing Information Science and Technology University Beijing100192 China Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Science Beijing100190 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Based on the security of distributed machine learning data and model based on remote proof, a cross-domain dynamic remote proof scheme based on ring signature improves the efficiency of computer cluster proof, the application of dynamic signature, realizes the real-time measurement of the confidence of computer cluster, and strengthens the real-time protection of data and model. Computers with different security domain may adopt different remote proof mechanism, and convert the proof information of different proof methods into unified proof information, thus realizes the cross-domain dynamic remote proof between computer clusters. Noise is added when the cloud server distributes data to a distributed computer cluster. Due to the particularity of distributed architecture and the unsolution of indefinite equations, the enemy cannot conspire with one or more computers to steal data or the model parameters trained by endpoints. Finally, the safety of this method is verified through safety analysis and experimental analysis, and the computational overhead required in the proof process is reduced compared with other methods. © 2025 SPIE.

关键词： Authentication

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SLO and Cost-Driven Container Autoscaling on Kubernetes Clusters 15

SLO and Cost-Driven Container Autoscaling on Kubernetes Clus...

引用

15th international conference on cloud computing and Services Science, CLOSER 2025

作者： Marchese, Angelo Tomarchio, Orazio Dept. of Electrical Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789897587474

Modern web services must meet critical non-functional requirements such as availability, responsiveness, scalability, and reliability, which are formalized through Service Level Agreements (SLAs). These agreements specify Service Level Objectives (SLOs), which define performance targets like uptime, latency, and throughput, essential for ensuring consistent service quality. Failure to meet SLOs can result in penalties and reputational damage. Service providers also face the challenge of avoiding over-provisioning resources, as this leads to unnecessary costs and inefficient resource use. To address this, autoscaling mechanisms dynamically adjust the number of service replicas to match user demand. However, traditional autoscaling solutions typically rely on low-level metrics (e.g., CPU or memory usage), making it difficult for providers to optimize both SLOs and infrastructure costs. This paper proposes an enhanced autoscaling methodology for containerized workloads in Kubernetes clusters, integrating SLOs with a cost-driven autoscaling policy. This approach overcomes the limitations of conventional autoscaling by making more efficient decisions that balance service-level requirements with operational costs, offering a comprehensive solution for managing containerized applications and their infrastructure in Kubernetes environments. The results, obtained by evaluating a prototype of our system in a testbed environment, show significant advantages over the vanilla Kubernetes Horizontal Pod Autoscaler. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cluster computing

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Enhancing cloud Security with Machine Learning: Tackling Data Breaches and Insider Threats 3

Enhancing Cloud Security with Machine Learning: Tackling Dat...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Nair, Sreejith Sreekandan Lakshmikanthan, Govindarajan Parthasarathy, J. Dinesh, P.S. Shanmugakani, Kavitha Jegajothi, B. Anna University Chennai India Jayalakshmi Institute of Technology Thoppur Dharmapuri India Sathyamangalam India Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College avadi India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331509675

cloud computing remains susceptible to internal threats and data compromises, notwithstanding its foundational role in contemporary business operations within this age of automation. To mitigate this concern, a machine learning framework is suggested for the preemption of prospective threats. Most, breaches of organizational internal systems are concerns about user non-compliant behaviors aimed at messing up activity logs, which can be combatted using supervised models and change detection. There seems to be a class of threats that go undetected that the authors would refer to as behaviour patterns that do not follow standard operating behaviour for business. An interface of RF and LSTMs would be able to establish common patterns and knit rule-based threats to create business insights and acceptable threats. User and System exhaustion of RF was used to minimise covert system identifiers of users to examine access pattern data streams, which were unlimited in dimensional centers. Evaluation of detection of anomalous content, such as R- scoring, classification of log events as generic clustering, etc. It could prevail on the challenges of log records analysis and the checkpoints of Structural Semantic communication principles. It can free up a range of security checkpoints by glaring at user access limits and ensuring multi-channel compliance. This approach would significantly strengthen data security on the cloud by targeting high-risk actions across oriented sections of breaching data storage centers. © 2025 IEEE.

关键词： cloud security

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Enhancing IoT-Enabled Electric Vehicle Battery Performance Monitoring and Prediction using AI Techniques 3

Enhancing IoT-Enabled Electric Vehicle Battery Performance M...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Tabassum, Saleha Bharathi, M. Peramasani, Narasimharaju Nagadivya, Nagadivya Busetty, Bhargavi Syed, Afrin Sulthana Vfstr Guntur India K.S.R.M. College of Engineering Kadapa India Mohan Babu University Thirupathi India K.S.R.M. College of Engineering Kadapa India

ISBN: (纸本)9798331515911

Strong battery management systems (BMS) are required to guarantee the best possible performance, safety, and longevity of battery packs due to the quick development of electric vehicles (EVs). This study introduces an Internet of Things (IoT)-enabled battery performance monitoring and control system that uses artificial intelligence (AI) methods to improve EV batteries' dependability and efficiency. In order to gather data on vital battery parameters like voltage, current, temperature, and state of charge (SoC) in real time, the suggested system combines a number of sensors. Through the use of a Wi-Fi communication module, this data is sent to a cloud-based platform for analysis, where artificial intelligence (AI) algorithms more precisely, a machine learning model are used to accurately forecast possible faults and monitor battery health. The technology maximizes battery life and energy efficiency by optimizing charging and discharging cycles through the use of predictive analytics. In order to prevent critical failures and guarantee safe operation, the system is also built to deliver alerts when battery characteristics surpass safe thresholds. The usefulness of the suggested strategy and its potential for use in smart EV technologies in the future are validated by simulation results showing notable increases in battery performance. © 2025 IEEE.

关键词： State of charge

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Email Validator Dashboard: A Comprehensive Tool for Accurate Email Validation

Email Validator Dashboard: A Comprehensive Tool for Accurate...

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2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Chand, Syed Riyyan Ahmed, Uzair Ismail, T Mohammed Kumar, B.P. Pradeep Chand, Syed Bilal Chaitra, P. Design Atria Institute of Technology Dept. of Computer Science Bangalore India Atria Institute of Technology Dept. of Computer Science Engineering Bangalore India HostUp Cloud Technologies Private Limited Bangalore India

ISBN: (纸本)9798331538538

Email authentication is of the utmost importance in maintaining the reliability and quality of email communication, specifically in database management and bulk email marketing. The centerpiece of the project is the development of an Email Validator Dashboard, which is a very user-friendly functionality filled with other useful parts that guarantee the accurate verification of email addresses. The dashboard really presents valuable inputs quickly in terms of deliverability, risk factors, and domain reliability due to its dynamic visualization along with real-time email validation procedures. The project's structure is such that the users get real-time data in the form of interactive charts and graphs, which make them see the transformations occurring as validation is going on, thus distinguishing it from the traditional tools. It comes with cutting-edge back-end data integrity validation processes, such as mailbox verification, domain DNS checks, and syntax verification, alongside powerful analytics. This project is really useful for organizations and developers who work with large email lists and at the same time can ensure high deliverability and compliance with data standards. © 2025 IEEE.

关键词： Marketing

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Optimizing Transmission Line Insulator Defect Detection: Lightweight Edge AI on UAVs 6

Optimizing Transmission Line Insulator Defect Detection: Lig...

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6th IEEE international conference on Image Processing, Applications and Systems, IPAS 2025

作者： Song, Zhifan Zhang, Yuan Liao, Xinrui Al Rahman M. Abu Ebayyeh, Abd Sorbonne University LIP6 Laboratory CNRS UMR 7606 Paris France University of California BerkeleyCA United States Imperial College London Department of Civil and Environmental Engineering London United Kingdom Imperial College London Department of Electrical and Electronic Engineering London United Kingdom

ISBN: (纸本)9798331506520

Effective transmission line insulator defect detection is crucial for maintaining power grid reliability and safety. Traditional methods, which often rely on ground-based inspections or cloud-based computing, can be slow and inefficient. This paper introduces EdgeIDD, a lightweight solution for real-time UAV-based insulator defect detection leveraging edge computing. By enabling on-site data processing, EdgeIDD reduces the reliance on high-bandwidth communication with central servers and addresses connectivity challenges in remote areas where most power transmission towers are constructed. Built upon YOLOv10, the latest state-of-the-art model with faster speed compared to precedent iterations by removing the post-processing step, EdgeIDD integrates minimalist design principles, replacing the complete backbone with lightweight vanilla blocks and incorporating our novel C2fP block and utilized distribution shifting convolution for accelerated feature fusion. Our model achieves a remarkable 45.2% reduction in total parameters compared to YOLOv10n while maintaining a high mean average precision (mAP) of 97.5% with minimal accuracy loss. With hardware acceleration, EdgeIDD reaches 23.3 FPS on a Raspberry Pi 5 with 7.3x speedup. This advancement in edge computing ensures rapid, reliable insulator defect detection directly from the UAV, even in areas with limited connectivity. © 2025 IEEE.

关键词： Deep learning

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Comparison of Point cloud and Surface Based Mapping for Autonomous Vehicles 11

Comparison of Point Cloud and Surface Based Mapping for Auto...

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11th international conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Brückner, Christoph Patino-Studencki, Lucila Faculty Mechanical and Automotive Engineering Coburg University of Applied Sciences and Art Güterstraße 8 Kronach Germany

ISBN: (纸本)9789897587450

Mapping and localization are essential processes in robotics and autonomous systems, providing precise environmental representation and real-time positioning. Unlike Simultaneous Localization and Mapping (SLAM), which combines these tasks simultaneously, mapping and localization are often decoupled in applications that require higher accuracy and efficiency from the outset, like autonomous vehicles. This study summarizes the main families of map representations used in SLAM and investigates the applicability for standalone mapping and localization tasks. Point cloud and surfaced based Mapping Methods, namely KISS-ICP and PUMA are explored and evaluated numerically using the KITTI database. Key performance metrics accuracy, registration time during localization, and map size are analyzed to compare their effectiveness. The results provide insights into the strengths and limitations of SLAM-based techniques when applied to decoupled processes. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Mapping

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