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Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

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Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

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A Survey on Tea Plant Disease Prediction for Blister Blight 2

A Survey on Tea Plant Disease Prediction for Blister Blight

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Gowthami, M. Nila Baaradhy, I. Nithin, V. Subhash, J. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328202

This survey seeks to give a general overview of the current state of Tea Plant disease prediction for the Blister Blight disease. To construct this system, IoT and machine learning approaches are applied. The goal of the research is to improve sustainable farming methods through early detection and prevention. The climatic conditions of the farmlands are analysed with a set of sensors. Real time input from the IoT setup is processed by the Machine Learning algorithms for detection of the Blister Blight disease. © 2024 IEEE.

关键词： Internet of things

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Computational Approaches for Identifying Potato Plant Pathogens 5

Computational Approaches for Identifying Potato Plant Pathog...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Vignesh, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Potato cultivation is a vital component of global agriculture, providing a significant source of nutrition and income. However, the threat of diseases affecting potato crops remains a persistent challenge for farmers. In our study, we present a novel approach to automatically classify diseases in potato plants using advanced deep learning methods. The proposed approach makes use of Convolutional Neural Networks (CNNs), that perform efficiently for image processing applications. The dataset utilized in our research comprises high-resolution Images depicting diverse disease symptoms in potato plants, including late blight, early blight, and the oomycete pathogen Phytophthora infestans, a destructive ailment that impacts potato crops. To automatically discern between healthy and diseased plants, we implement a multi-layered CNN architecture that learns discriminative characteristics of images. This research significantly contributes to precision agriculture by providing an automated and efficient solution for identifying potato diseases. The proposed method in disease classification not only facilitates prompt disease management but also enables precise treatments, thereby reducing reliance on chemical interventions. The practical implications of these findings extend to farmers, agronomists, and researchers involved in sustainable and resilient potato cultivation. © 2024 IEEE.

关键词： disease identification early blight late blight potato disease

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Enhancing Goal Achievement through Collaborative Goal Tracking: A Recommender System Approach 13

Enhancing Goal Achievement through Collaborative Goal Tracki...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Shaik, Kousar Reddy, Kaki Manoj Vardhan, B Sai Reddy, H. Venkateswara Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350305463

An innovative goal tracking application that encourages cooperation among users with similar goals is presented in this research. The app uses a strong recommender system to link users based on goal keywords, forming a helpful community, going beyond tracking each user's progress individually. The goal of the study is to assess how well collaborative features increase user engagement and goal achievement rates by examining the shortcomings of current goal tracking apps. The outcomes demonstrate how effective teamwork can be and pave the way for a revolutionary method of goal monitoring. Setting the stage for future improvements that promise a more inclusive and flexible goal tracking experience, such as machine learning-based personalized recommendations, this research lays the groundwork. © 2024 IEEE.

关键词： Recommender systems

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Enhancing Acute Lymphoblastic Leukemia Diagnosis Through Dual Deep Learning Approaches 6

Enhancing Acute Lymphoblastic Leukemia Diagnosis Through Dua...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Muhammad Shameem, P. Jemsheer Ahmed, P. Nisa Parvin, K.P. Safa, M.T. Sahana, V.P. Hima Musthafa, T.H. Mea Engineering College Department of Computer Science & Engineering Malappuram India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381689

Leukemia, a life-threatening blood cancer, de-mands early and accurate diagnosis for effective treatment. This project aims to bring out the potential of dual deep learning techniques to enhance acute lymphoblastic leukemia detection. Our approach combines the capabilities of Convolutional Neural Networks (CNNs). We also propose a mobile application that integrates these dual deep learning models to provide a user-friendly and accessible solution for acute lymphoblastic leukemia detection. High-resolution medical images, such as peripheral blood smears undergo thorough examination using advanced CNN architectures. Sequential data, encompassing temporal information like cell morphology evolution, genetic markers, and patient histories, are systematically analyzed. The dual deep learning model is trained on an extensive and diverse leukemia dataset, encompassing various subtypes and disease stages. Fine-tuning and transfer learning techniques are employed to optimize model performance. Once trained, the system is assured to efficiently evaluate new patient data,providing rapid and highly accurate leukemia diagnosis. The app is trained by using a comprehensive dataset of annotated blood cell images. The app will be designed with a user-friendly interface, enabling medical professionals to easily upload and analyze blood samples. The system's output will provide rapid and accurate leukemia detection, reducing the time and subjectivity associated with manual diagnosis. In summary our project aims to advance acute lymphoblastic leukemia detection by combining two or more powerful deep learning methods to work together effectively. This system holds the promise of assisting healthcare practitioners in making more informed decisions, ultimately leading to improved patient outcomes and more effective management of this life-threatening disease © 2024 IEEE.

关键词： Diseases

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Brain Tumor Diagnosis Using Deep Learning: A Literature Review 2

Brain Tumor Diagnosis Using Deep Learning: A Literature Revi...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Bhuvaneswari, S. Thomas, Joel Nithish, S. Prithvi, S. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328202

Brain tumor diagnosis is a critical task in the field of medical imaging, with the potential to significantly impact patient outcomes and treatment planning. The use of deep learning has been more well-known within the last ten years as a potential method for improving and automating the detection and categorization of brain tumors. The goal of this literature review is to present a thorough overview of the application of deep learning to the diagnosis of brain tumors. © 2024 IEEE.

关键词： Medical imaging

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Machine Learning (ML) based Electricity Price Prediction with Visualization 3

Machine Learning (ML) based Electricity Price Prediction wit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Abisha, D. Kumar, Prasanna D. Christy, Ida J. Evanjalin, Navedha R. Rajesh, P. Haris, Mohamed K. Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9798331532420

Electricity price prediction is crucial for businesses reliant on heavy machinery or computing services, as it aids in cost management and energy optimization. Accurate predictions allow these businesses to forecast annual electricity expenses, enhancing cost efficiency. This project aims to forecast electricity prices using machine learning techniques, specifically the Random Forest regression algorithm. The project utilizes historical data on electricity consumption and other influencing factors, including date and time, holiday indicators, day of the week, week of the year, forecasted and actual wind production, national load, temperature, wind speed, and CO2 intensity. The methodology involves converting string values to numerical values, handling missing data, and normalizing the dataset, identifying key features that significantly impact electricity prices, Splitting the data into training and testing sets, and training the model using the Random Forest regression algorithm and assessing the model's performance with appropriate metrics to ensure accuracy and reliability. The results demonstrate that the Random Forest model effectively predicts electricity prices with high accuracy. Visualizing these predictions in Power BI provides intuitive insights into the factors influencing electricity prices. These findings enable businesses to better anticipate electricity expenses, optimize energy usage, and improve financial planning. The comprehensive approach outlined in this project offers a valuable tool for understanding and forecasting electricity prices, thereby enhancing resource management and cost efficiency. © 2024 IEEE.

关键词： Data Preprocessing Electricity Pricing Energy Cost Optimization Machine Learning Random Forest Regression

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Performance Evaluation of Two-Tier Hybrid Cryptographic Models for Secure Data Transactions in Cloud Computing 2

Performance Evaluation of Two-Tier Hybrid Cryptographic Mode...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Debnath, Pankoj Kar, Tonoy Chandra Ashraf, Din Md Arif, Redwanul Islam Lysuzzaman, Md Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350383652

Cloud computing is a trending and enormous technology. With the improvements in the field of Cloud computing, the main challenge arises in the transaction of data securely from one platform to another. Various cryptographic algorithms are used to provide a secure transaction of data. However, conventional cryptographic algorithms might not be sufficient enough for modern threats. To face these potential vulnerabilities, hybrid cryptographic algorithms can be the solution. Hybrid cryptography integrates multiple cryptographic algorithms to enhance performance and overall safety. RSA(Rivest-Shamir-Adleman) is the most common public key algorithm for encryption and decryption. This paper analyzes a modified form of RSA known as Modified-RSA that enhances security in hybrid cryptographic algorithms by incorporating three prime numbers instead of two. The Modified-RSA(MRSA) hybrid models are compared with the original RSA hybrid models with some theoretical and practical aspects. In this research, a comparative study of two-tier hybrid cryptographic models applied to secure data on the cloud is presented. Practical implementations were conducted with hybrid models of RSA and Modified-RSA using Python simulation. Encryption time, decryption time, average throughput, and efficiency were found using relatively more extensive data. The main motive was to find out the performance of every cryptographic hybrid and see which hybrid model is more efficient. © 2024 IEEE.

关键词： Cloud platforms

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Real-Time Eye State Detection Using Deep Learning for Blink and Dry Eye Diagnosis using Smart Phone

Real-Time Eye State Detection Using Deep Learning for Blink ...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Vijayalakshmi, R. Kayalvizhi, S. Dharaniya, R. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

Inspection of blinks and real-time recognition of eye states is crucial for diagnosing ophthalmologic symptoms: dry eye, drowsiness, fatigue, etc. This work presents a new eye state recognition method using a VGG16-based model with deep learning. The model is trained on 1234 training photos and 218 test images with equal numbers of closed and open in a balanced dataset to detect eye conditions accurately. To increase the robustness of the model, data augmentation techniques like random rotation, zooming, and horizontal flip are applied to training data. The model learns to fine-tune after transfer learning on the pre-trained VGG16 and several dense layers make improvements on feature extraction. Experimental results show that the proposed model has achieved an excellent accuracy of 98% for both closed and open-eye classification. Similarly, we can attain over 97% precision, recall, and F1 for both classes. The high throughput of the system to obtain blink patterns shows how important it is as a powerful tool for real-time eye monitoring, such as dry eye detection and sleep alertness systems installation. This work demonstrates the effectiveness of deep learning models in medical and safety-critical domains, showcasing superior generalization abilities across various test environments. © 2024 IEEE.

关键词： Convolutional neural networks

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A Survey on Marine Debris Detection and Location Using Machine Learning Technologies 2

A Survey on Marine Debris Detection and Location Using Machi...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Abins, A Arockia Darwin Wilfred, S.M. Ashwin Ram, K. Hari Prasad, D. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

The large amounts of debris present in our ocean water poses a major threat to the flora and fauna of our marine ecosystem. The continuous increase in dumping of solid waste, and the very slow rate of degradation of most items, are together leading to a gradual increase in marine litter found in the ocean. While a large number of human-based campaigns have been proposed to tackle this issue, the sheer amount of litter present in the ocean results in their efforts to be insufficient. But the increasing development in the field of computer vision combined with the help of Autonomous Underwater Vehicle (AUV), presents itself to be a better solution to reduce the human complexities and automate the tedious task of decontaminating our ocean. This paper contains a literature review of the different Machine learning models using various neural architectures currently used to detect and locate various debris present in marine water (i.e., plastic, metals, glass, styrofoam, cigarette butts etc.). © 2024 IEEE.

关键词： computer vision

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