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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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Assessment of network performance indexes’ designing based on combined trapezoidal and intuitionistic fuzzy information axiom

Assessment of network performance indexes’ designing based ...

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2022 International Conference on Artificial Intelligence and Industrial Design, AIID 2022

作者： Zhou, Kun Deng, Kai Chen, Zhi Institute of Computer Application China Academy of Engineering Physics Sichuan Province MianYang City621900 China

ISBN: (纸本)9781510663893

The designing of network performances indexes are critical to respond to the factors that assess network performances. In this study, a novel assessment method for designing network performance indexes based on combined trapezoidal and intuitionistic fuzzy information axiom. First, the network performances indexes are designed with the customer-expected functional requirement (FR) and the existing FR as the design range and the system range respectively. Then the information content of qualitative and quantitative indexes, which facilitates the selection of the best alternative with minimum information content, are calculated separately and standardized. To solve this issue of the integral characterised by fuzzy bounds, this study proposes a defuzzification method to convert fuzzy bounds into crisp numbers. Finally, the assessment is performed to demonstrate the effectiveness of the developed approach. © 2023 SPIE.

关键词： Design

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Optimizing Stroke Diagnosis: application of Machine Learning Algorithms for Early Detection

Optimizing Stroke Diagnosis: Application of Machine Learning...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sharma, Chetan Anooja, A. Kishore, Jaydeep Bhardwaj, Vivek UpGrad Education Private Limited Bangalore India School of Computer Application Jecrc University Rajasthan Jaipur India School of Computer Science and Engineering Manipal University Jaipur Jaipur India

ISBN: (纸本)9798331509828

Being a top cause of mortality and serious disability, stroke demands urgent diagnosis and impactful prevention. Current innovations in machine learning (ML) show a great capacity for predicting the likelihood of a stroke. This study reports on applying ML algorithms to improve the timely and early identification of stroke, leading to more effective interventions and lowering morbidity and mortality numbers. This work employs a complete dataset that integrates demographic information, medical history, data on lifestyle, and physical measurements. engineering techniques of a high level were utilized in pre-processing the data to reduce the assumptions that lay underneath. This research applied five machine learning models for the prompt identification of stroke: Decision Tree, Support Vector Machine (SVM), Gradient Boosting, K-Nearest Neighbours (KNN), and XG Boost. The number of datasets used for training was 80%, and 20% went to testing. The accuracy of the Gradient Boosting model stood at 96%, outperforming XG Boost at 92% and Decision Tree at 91%. The model with Support Vector Machines (SVM) gave 84% accuracy, but all three models illustrated high precision and recall. These results indicate that machine learning models could significantly augment stroke prediction and enhance clinical decision processes. © 2025 IEEE.

关键词： Support vector machines

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Empirical Analysis of Digital Currency Herding and Contagion Effect Using Deep Learning Approaches 3

Empirical Analysis of Digital Currency Herding and Contagion...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Tripathy, Nrusingha Hota, Sarbeswara Mishra, Debahuti Dash, Biswajit Nayak, Subrat Kumar Tripathy, Jogeswar Deemed to Be University Department of Computer Science and Engineering Siksha 'O' Anusandhan Odisha Bhubaneswar India Deemed to Be University Department of Computer Application Siksha 'O' Anusandhan Odisha Bhubaneswar India Raajdhani Engineering College Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

In recent years, the use of virtual or cryptocurrency-based money has grown. Blockchain, a digital accounting system, is the foundation of cryptocurrency. The markets for cryptocurrencies are eminent for their high volatility and quick price swings. People tend to be more likely to follow the lead of others at times of notable price fluctuations out of a concern of losing money or missing out on possible gains. In this work, we taking the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), the hybrid combinations of LSTM-Convolutional Neural Network (CNN), LSTM-Gated Recurrent Unit (GRU) and for Facebook Prophet (Fb-Prophet) model smoothing, triple exponential smoothing (Holt-Winters Method) is utilized, which adds seasonality into the forecasting model. In this work bitcoin exchanges providing real-time information casing the period from January 2012 to July 2020 is utilized. The price of cryptocurrencies fluctuates significantly throughout a range of time periods, from short-term intraday swings to long-term patterns that last for weeks or months. By altering the network layer design and the duration of the input sequences, LSTM-GRU models configured to adapt to various time horizons. In contrast to previous iterations, the hybrid LSTM-GRU performs well. When compared to other models, the model's Mean Absolute Error (MAE) score of 0.056 and Root Mean Square Error (RMSE) score of 0.073 are quite acceptable. Forecasting the value of cryptocurrencies will help traders and investors in making better-informed choices when purchasing, disposing of, and keeping assets. © 2024 IEEE.

关键词： Convolutional neural networks

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IOT-based Smart Ground water Recharge System 1

IOT-based Smart Ground water Recharge System

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Uniyal, Manas Bhatt, Chandradeep Mohd, Noor Kumar, Devendra Graphic Era Hill University Computer Science Department Dehradun India Abes Engineering College Computer Application Department Ghaziabad India

ISBN: (纸本)9798350338027

Water is the most important commodity for the existence of life. In India, the main sources of water are rainwater(monsoon) that fills the rivers with water and underground water. However, we have seen a steady decline in underground water levels across India at an alarming rate that affects the agriculture and daily water needs of the citizens. This paper suggests a smart ground water recharge system that automatically recharges the ground water after rain falls. The system uses smart IOT devices, solar panels, and a filter to filter out unwanted materials that come along the rainwater. In this paper a fully automatic rainwater harvesting is introduced that collects rainwater and filters out dirt and unwanted materials(trash) that comes along the rain. It also senses the acidic content of rain and filters it out when needed. Later on, the filtered water can be directly recharged in ground, or it can also be collected in artificial pools for irrigation purposes. Polluted water can be very harmful for our environment and food cycle. © 2023 IEEE.

关键词： Aquifers

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Communication Countermeasure Strategy Decision-Making Based on Incomplete Information Game 27

Communication Countermeasure Strategy Decision-Making Based ...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Liu, Jinyue Gao, Xiang Yang, Haowei Zhang, Jihao Liu, Yuhan Gong, Peng National Key Laboratory of Mechatronic Engineering and Control School of Mechatronical Engineering Beijing Institute of Technology Beijing China Institute of Computer Application Technology Norinco Group Beijing China

ISBN: (纸本)9791188428137

In modern battlefields, the stability of wireless communications is crucial for intelligence transmission, command coordination, and maintaining strategic advantage. However, with the rapid advancement of communication technologies, the electromagnetic environment has become increasingly complex, making intelligent decision-making in communication countermeasures a prominent research focus. However, most existing studies adopt deep learning and reinforcement learning methods, which require extensive data samples and computational resources. To address these limitations, this paper proposes a game-theoretic model for intelligent radio communication countermeasures under incomplete information. It quantitatively evaluates common attack and defense tactcis used in communication countermeasures and explores the mixed-strategy equilibrium of the game when both the attackers and defenders consist of multiple types. Furthermore, simulation-based comparative analyses are conducted to assess the factors influencing the utility of both the attacker and defender. The findings offer theoretical insights and decision-making support for the deployment of communication countermeasure strategies in practical military operations, contributing to significant strategic value. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Deep reinforcement learning

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A Survey of Dynamic Exploration Symmetric Encryption with Backward & Forward Secrecy 3

A Survey of Dynamic Exploration Symmetric Encryption with Ba...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Singh, Satendra Bhagat, Samriti Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

A sophisticated type of symmetric encryption is called Multiuser Dynamic Searchable Symmetric Encryption. It expands the capabilities of conventional symmetric encryption to enable actions such as adding and removing encrypted data as well as the ability for a verified set of information users to access their preferred encrypted data within the dynamic database. The latter can use using erased data and search results to comprehend the content, while the former allows attackers to discover by performing file injection attacks, keywords from the injected data were exposed. can do. To our knowledge, the available literature has not completely addressed these privacy problems for M-DSSE. In light of this, our attention is directed toward multiuser dynamic searchable symmetric encryption that satisfies the requirements for forwarding and backward security. It uses Inverted indexes are created and updated using random number generator Functions (RNG) and Authenticator primitives search tokens to suggest specific solutions. In the accidental model, the suggested approach is shown to be secure. Additionally, the performance study demonstrates that the suggested approach provides increased security assurances at a fair cost to efficiency. © 2023 IEEE.

关键词： Cryptography

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Research and Implementation of DM1 Based on the J1939 3

Research and Implementation of DM1 Based on the J1939

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3rd IEEE International Conference on computer Systems, ICCS 2023

作者： Wang, Ying Guo, LiWei Tang, Tian Xi'an Aeronautics Computing Technique Research Institute Avic Laboratory of Computer Application Engineering Xi'an China

ISBN: (纸本)9798350313666

The J1939 protocol is a communication protocol widely used in automotive electronic network. It supports real-time closed-loop control and communication among electronic control systems which are distributed in the whole vehicle. This paper focuses on the overall structural characteristics of the J1939 protocol and DM1(the Active Diagnostic Trouble Codes), deeply studies and analyzes the storage mode of the DM1 and the packet transmission, it designs and implements it function, improves the storage structural characteristics and filling mode of DM1. The improved storage structure and filling mode can effectively reduce the high demand for storage space, and not only access efficiently, but also reduce the size of the used space. Through the in-depth research, analysis, implementation and improvement of the paper, DM1 in J1939 protocol has further improved the communication efficiency in the practical applications. © 2023 IEEE.

关键词： Filling

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ChatGPT Based Image Steganography (CGIS): A Novel Intelligent Information Hiding Approach to Achieve Secure Covert Communication 1

ChatGPT Based Image Steganography (CGIS): A Novel Intelligen...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Mukherjee, Subhadip Mukhopadhyay, Somnath Sarkar, Sunita Assam University Department of Computer Science and Engineering Silchar788011 India Sikkim University Department of Computer Application Gangtok737102 India

ISBN: (纸本)9798350342796

Covert communication refers to the practice of exchanging information or messages in a discreet or secretive manner, with the intent of keeping the communication hidden from unintended or unauthorized recipients. Steganography is a widely used, strategy for establishing covert communication, for maintaining the privacy and security of sensitive information. Steganography ensures that the message remains concealed from surveillance or eavesdropping. Artificial Intelligence (AI) plays a crucial role in enhancing security across various domains of covert communication. ChatGPT is an AI model, specifically a natural language processing (NLP) model. It belongs to the broader category of AI models which can comprehend and produce language for humans. In this paper, a new image steganographic model named CGIS is developed for covert communication using ChatGPT. Proposed steganographic method has achieved 3.0 bpp of hiding capacity. In situations where confidentiality is critical, such as in military operations, intelligence agencies, or corporate strategies, proposed method ensures that the message cannot be detected and or extracted by any surveillance or eavesdropper. © 2023 IEEE.

关键词： Artificial Intelligence CGIS ChatGPT Covert Communication Image Steganography

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An Efficient AdaBoost and CNN Hybrid Model for Weed Detection and Removal

An Efficient AdaBoost and CNN Hybrid Model for Weed Detectio...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Tiwari, Shivam Srivastava, Anurag Kumar Rathore, Yogesh Singh Vats, Satvik Rastogi, Ashish Kumar Department of Computer Science & Engineering Sharda University Greater Noida India Computer Science and Application Sharda University Greater Noida India

ISBN: (纸本)9798350305708

With the aid of AI-based methods like convolution neural networks, this article seeks to enhance weed detection and removal. A plant that is not wanted and is growing wild, especially one that does so on cultivated land and produces a low crop yield, is referred to as weed. In order to increase production in agriculture, it is essential to have a strong and positive weed identification and removal. This paper suggested a weed control programme for wheat fields indicated on the RGB combination of image elements with essential attributes due to the low weed control rate and availability of inefficient weed removal approaches in the wheat sector. AdaBoost estimation and the CNN model are combined to enhance the performance of the weed distinguishing proof. The approach tests the bounds of the 2-D topographical from RGB images while seeing wheat-like grass weeds. The accuracy of the test results displayed by the algorithm during the process was 94.7%. In comparison to previously employed methods, the combination of both algorithms used provides a high degree of accuracy in the estimation of various types of weeds. © 2023 IEEE.

关键词： Adaboost algorithm Convolutional Neural Network (CNN) Depth features Feature Extraction weed

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