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Energy Efficient LEDCA Analysis for Improved Throughput in Wireless Sensor Networks 6

Energy Efficient LEDCA Analysis for Improved Throughput in W...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Padmageetha, B.G. Naik, Pramod Kumar Patil, Mallanagouda Dept.of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering RVITM Bengaluru India

ISBN: (纸本)9798331522667

Wireless Sensor Networks (WSNs) face significant challenges in terms of energy conservation, scalability, and data throughput owing to their limited node energy resources. This paper presents a low-energy data-centric algorithm (LEDCA), a novel energy-aware clustering and routing protocol designed to optimize energy efficiency and improve throughput in WSNs. Through simulations, LEDCA demonstrated a Packet Delivery Ratio (PDR) of up to 90%, throughput of 255,217 bps, and average latency of 159 ms, validating its efficiency in resource-constrained environments. The algorithm integrates residual energy awareness by prioritizing high-energy nodes for Cluster Head (CH) roles, dynamically adjusting routing paths, and balancing the network load to prevent premature node failure. By applying an Energy Mapping Node and a real-time Energy Mapping Table, LEDCA minimizes re-clustering, reduces data redundancy, and ensures stable data transmission even under high-density networks. Simulation results also show LEDCA's scalability of LEDCA, with A9_T_200 achieving the highest throughput under extended transmission ranges and A3_T_200 achieving the lowest latency, making it suitable for energy-sensitive and latency-critical applications. Future enhancements, such as integrating Non-Orthogonal Multiple Access (NOMA) and dynamic resource allocation, could further improve LEDCA’s interference management and scalability of LEDCA for IoT and 5G/6G networks, addressing the needs of next-generation applications. © 2025 IEEE.

关键词： Resource allocation

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Energy Efficient LEDCA Analysis for Improved Throughput in Wireless Sensor Networks

Energy Efficient LEDCA Analysis for Improved Throughput in W...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Padmageetha B. G Pramod Kumar Naik Mallanagouda Patil Dept.of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering Dayananda Sagar University Bangalore India Dept. of Computer science and Engineering RVITM Bengaluru India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Wireless Sensor Networks (WSNs) face significant challenges in terms of energy conservation, scalability, and data throughput owing to their limited node energy resources. This paper presents a low-energy data-centric algorithm (LEDCA), a novel energy-aware clustering and routing protocol designed to optimize energy efficiency and improve throughput in WSNs. Through simulations, LEDCA demonstrated a Packet Delivery Ratio (PDR) of up to 90%, throughput of 255,217 bps, and average latency of 159 ms, validating its efficiency in resource-constrained environments. The algorithm integrates residual energy awareness by prioritizing high-energy nodes for Cluster Head (CH) roles, dynamically adjusting routing paths, and balancing the network load to prevent premature node failure. By applying an Energy Mapping Node and a real-time Energy Mapping Table, LEDCA minimizes re-clustering, reduces data redundancy, and ensures stable data transmission even under high-density networks. Simulation results also show LEDCA's scalability of LEDCA, with A9_T_200 achieving the highest throughput under extended transmission ranges and A3_T_200 achieving the lowest latency, making it suitable for energy-sensitive and latency-critical applications. Future enhancements, such as integrating Non-Orthogonal Multiple Access (NOMA) and dynamic resource allocation, could further improve LEDCA's interference management and scalability of LEDCA for IoT and 5G/6G networks, addressing the needs of next-generation applications.

关键词： Wireless sensor networks NOMA Scalability Simulation Clustering algorithms Throughput Routing Energy efficiency Routing protocols Resource management

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Smart Irrigation System with Data Visualization

Smart Irrigation System with Data Visualization

引用

2019 International Conference on Emerging Trends in science and Engineering, ICESE 2019

作者： Bhowmick, Sudharshan Kumar Padhiari, Shraddha Apoorv, Shubhendu Annadevi, E. Sathyabama Institute of Science and Technology Dept.Of ECE Chennai India SRM Institute of Science and Technology Dept. of Computer Applications Chennai India

ISBN: (纸本)9781728118710

In India, agriculture continues to dominate all other sectors which provide the majority portion in India's marketplace. The proper circumstances are required for the production of crops. In earlier times, irrigation was developed by traditional techniques of water flow from one point to another. The proposed idea aims to help farmers to maintain and irrigate their lands in a better and efficient way. This whole system is automated with least interference of human being. The basic idea is to separate the field into blocks and monitor various parameters using sensors. Depending on the needs of the farmer he can set the threshold value for the blocks. The threshold value will help in working on the automated irrigation system. It maintains the moisture content of the soil depending on the farmers need. Hence, encouraging the healthy growth of the plant with the proper supply of water. The land will be irrigated only when needed saving water. © 2019 IEEE.

关键词： Soil moisture

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A Learning Performance Assessment Model Using Neural Network Classification Methods of e-Learning Activity Log Data 2

A Learning Performance Assessment Model Using Neural Network...

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2nd International Conference on Smart Systems and Inventive Technology, ICSSIT 2019

作者： Arumugam, S. Kovalan, A. Narayanan, A.E. Dept. of Computer Science and Applications Periyar Maniammai Institute of Science and Technology Thanjavur India Dept.of Computer Science VSB College of Arts and Science VSB Group of Institutions Karur India Department. of Computer Science Engineering Periyar Maniammai Institute of Science and Technology Thanjavur India

ISBN: (纸本)9781728121192

Interactive learning and computed aided learning methods increased a lot of developments in Learning Systems. It results a collection of log data of learning activities increased. Machine Learning(ML) based Data Mining(DM) is used to find some meaningful information from huge and complex datasets. Learning behavior of the students can be accessed by using ML based data mining techniques on the log data of the students' activity. It helps to Even there are possibilities to recognize the performance of the students before the examination. Time-series students log data during their lab section is taken from the UCI dataset. The modified numeric data file is taken and applied classification techniques to predict the performance of the students. Among the implemented models J48, Random Tree, Multilayer Perceptran Neural Network(MLPNN), Radial Basis Neural Network(RBFNN), the RBFNN based classification is better than other classification techniques to predict the students' grades. © 2019 IEEE.

关键词： Students

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LCS based Text Steganography through Indian Languages

LCS based Text Steganography through Indian Languages

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2010 3rd IEEE International Conference on computer science and Information Technology—ICCSIT 2010

作者： S.Changder D.Ghosh N.C.Debnath Dept.of Computer Applications Dept.of CSE National Institute of TechnologyDurgapur Dept.of Computer Science Winona State University

Steganography is the method to hide a message inside another message without drawing any suspicion to the others and that the message can only be detected by its intended *** the other steganography methods such as Image,Audio,Video,a number of text steganography algorithms have been *** paper presents some new approaches for steganography in Indian *** the availability of more characters and flexible grammar structure of Indian Languages this approach hide the secret message in the text by creating meaningful sentences after finding the longest common subsequence of two binary string among which one is the secret message and another may be any binary *** collection of these created Indian sentences will be used as the cover media for this steganography *** by applying the reverse method to the cover file that is after finding the longest common subsequence of sentences from the cover file and replacing the matched character by the bits of another binary string the system extract the original message from the cover file.

关键词： Information Security Text Steganography Information Hiding Text watermarking LCS based Text Steganography

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An Effective Analysis of Machine Learning Models for Prediction of Heart Disease 2

An Effective Analysis of Machine Learning Models for Predict...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Patil, Rudragoud Kamkar, Shambahvi C. Goudar, Shwetha Indudhar K.L.S.Gogte Institute of Technology Dept of Computer Science and Engineering Karnataka Belagavi India K.L.S Gogte Institute of Technology Dept.of Master of Computer Applications Karnataka Belagavi India

ISBN: (纸本)9798350372892

Heart failure is a chronic illness that impacts millions of individuals worldwide. In order to forecast heart failure health status early and take the necessary activities to address this global issue an effective machine learning-based technique is required. Globally, heart disease is one of the leading causes of death. Medical professionals find it challenging to forecast since it demands experience and a deeper level of predictive knowledge. The healthcare industry operates in a knowledge-deficient but information-rich environment. Healthcare systems have access to a vast amount of data via the internet, but there aren't many useful tools for analyzing the data to find hidden patterns. Medical efficiency will increase with an automated system, which will also save money and time. This software uses information acquired from Kaggle to forecast the likelihood of a disease developing. To create a prediction model, the following algorithms are used: Random Forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR). Phases for feature selection and data preprocessing were finished before the models were created. When the models were evaluated based on accuracy and the F1-score, the RF model performed the best. © 2024 IEEE.

关键词： Adversarial machine learning

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Continuous Tweet Stream Analysis using Apache Spark Streaming API & Kafka 15

Continuous Tweet Stream Analysis using Apache Spark Streamin...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nisha, A.G. Shivaprasad, N. Nischitha, C.B. Shashikala, B.M. Sujithra, M. Chaba, Yogesh Electronics & Communications Engineering JSS Science & Technology University Mysuru India Dept.of Electronics & Communications Engineering Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Dept.of Computer Science St.Joseph's First Grade College Mysore India Dept.of Computer Applications Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Social media has become a trend nowadays people are spending more time in social media platform like Facebook, Twitter, Instagram to share their opinions, creating new interests, ideas and generate *** provide a major platform for collecting information which helps us to analyze them for our *** such microblogging social network is twitter which allows information collaboration and quick information sharing that provides relief from rising *** has several millions of people tweeting and expressing their perspectives on a due to its distinctively brief and straightforward expressing *** enormous volume of raw data from tweets can be utilised for analysis and processing based on industrial or business *** this technical paper, the application of tweet sentiment analysis by extracting a vast number of tweets using continuous tweet stream provided by Spark and Kafka has been *** system fetches live tweets from Twitter based on the search criteria, process the tweets, by using text mining techniques and classify them based on different labels. © Grenze Scientific Society, 2024.

关键词： Tweets

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Intrusion Detection System Using Decision Tree Algorithm

Intrusion Detection System Using Decision Tree Algorithm

引用

2012 IEEE 14th International Conference on Communication Technology

作者： Manish Kumar M.Hanumanthappa T.V.Suresh Kumar Dept.of Master of Computer Applications M.S.Ramaiah Institute of Technology Research Scholar Department of Computers Science and ApplicationsBangalore University Dept.of Computer Science and Applications Jnana Bharathi CampusBangalore University

Intrusion Detection System (IDS) is the most powerful system that can handle the intrusions of the computer environments by triggering alerts to make the analysts take actions to stop this ***’s are based on the belief that an intruder’s behavior will be noticeably different from that of a legitimate user.A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious attacks by using traditional statistical methods to new data mining approaches in last ***,today's commercially available intrusion detection systems are signature- based that are not capable of detecting unknown *** this paper we analyze a classification model for misuse and anomaly attack detection using decision tree algorithm.

关键词： Intrusion Detection System KDD Dataset Network Security Decision Tree Algorithm

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New Method of POS based on Artificial Intelligence and Cloud Computing

New Method of POS based on Artificial Intelligence and Cloud...

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2019 International Conference on Recent Advances in Energy-efficient Computing and Communication, ICRAECC 2019

作者： Dey, Arnab Jain, Sudhanshu Nandi, Shovan Bengal Institute of Technology Dept. of Computer Science and Engineering Kolkata700150 India Chitkara Institute of Engineering and Technology Dept.of Master of Computer Applications Punjab140401 India Bengal Institute of Technology Dept. of Electronics and Communication Engineering Kolkata700150 India

ISBN: (纸本)9781538694824

Point of Sale systems have been rapidly getting advanced and powerful. In recent times, we can see credit and debit card based POS systems apart from traditional cash POS with manual entry. POS systems with Barcode readers are also developed. What we must recognize is that still many fraud and security issues are present. POS system that we have proposed, is based on new age technology that is 'AI subset based POS' which is a new method. We can find POS system with face recognition and Card swipe system at few places. Considering that the security problems still exists with the current system. We have proposed face recognition along with password as a method of payment making POS systems both card less and cashless. Cloud Computing technology is suggested way of implementation to make solution overall energy efficient as well. © 2019 IEEE.

关键词： Face recognition

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Cross-Layer Framework for IoT Data Protection Using Blockchain Based Federated Learning Model (BCFL) 4

Cross-Layer Framework for IoT Data Protection Using Blockcha...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Murali, Srividhya B Raja, S.R. Kanimozhi, E. Akila, D. Khan, Abdul Azeem Alajmani, Samah H. SRM Institute of Science and Technology Dept. of Computer Applications Kattankulatur Chennai India Saveetha College of Liberal Arts & Sciences SIMATS Deemed University Dept.of CS Chennai India Shri Shankarlal Sundarbai Shasun Jain College Dept. of Computer Applications Chennai India Saveetha College of Liberal Arts & Sciences SIMATS Deemed University Dept. of Computer Applications Chennai India Faculty of Islamic Technology Brunei College of IT Taif University Department of IT Saudi Arabia

ISBN: (纸本)9798350353266

In recent years, concerns about the security of data exchanged between the Internet of Things (IoT) devices have increased with respect to information security. Data security and the prevention of critical information leaks are both facilitated by a security model. Using Blockchain technology, the issues with data security in IoT devices may be resolved. Decentralized ledger technology known as blockchain enables many useful applications, including distributed storage, consensus, encryption, and Machine-to-Machine (M2M) data transmission. This paper develops a cross-layer framework for protecting the sensitive data of IoT by applying Blockchain based Federated Learning model (BCFL). The data records are organized into blocks in the BCFL paradigm, and then encode and decode methods are used to send the data to the servers. Using the Federated learning method, the block size and coding redundancy factor are calculated in a block-by-block transmission manner. To protect the integrity of data, a hash function and a digital signature are computed for each block of data. By experimental results, it was shown that the proposed BCFL model attains higher detection accuracy and correctness of data with reduced encoding/decoding time and reconstruction time. © 2024 IEEE.

关键词： Federated learning

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