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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer Science 2024年第5期5卷 1-22页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

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22nd International Learning and Technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

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Sinhala Named Entity Recognition Model: Domain-Specific Classes in Sports 4

Sinhala Named Entity Recognition Model: Domain-Specific Clas...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Wijesinghe, W.M.S.K. Tissera, Muditha Department of Statistics and Computer Science Faculty of Science University of Kelaniya Sri Lanka Department of Software Engineering Faculty of Computing & Technology University of Kelaniya Sri Lanka

ISBN: (纸本)9798350398090

Named Entity Recognition (NER) is one of the crucial and vital subtasks that must be solved in most Natural Language Processing (NLP) tasks. However, constructing a NER system for the Sinhala Language is challenging. Because it comes under the category of low-resource languages. Therefore, the proposed approach attempted designing a mechanism to identify specific named entities in the sports domain. Firstly, a domain-specific corpus was built using Sinhala sport e-News articles. Then a semi-automated, rule-based component named as "Class_Label_Suggester" was built to annotate pre-defined named entities. After auto annotation, the outcome was further validated manually with a little effort. Finally, it was trained using the annotated data. Linear Perceptron, Stochastic Gradient Descent (SGD), Multinomial Naive Bayes (MNB), and Passive Aggressive classifiers were used to train the NER model. Though, the above Machine Learning (ML) algorithms showed approximately 98% accuracy, the MNB model demonstrated highest accuracy for the identified class labels of which, 99.76% for 'Ground', 99.53% for 'School', 98.55% for 'Tournament', and 97.87% for 'Other' classes. Additionally, high precision values of the above classes were 81%, 72%, 62%, and 98% respectively. An accurately annotated Sinhala dataset and the trained Sinhala NER model are main contributions of the study. © 2022 IEEE.

关键词： Sports

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Detection of Covid Disease using Computed Tomography Images 14

Detection of Covid Disease using Computed Tomography Images

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Tripathi, Amrita Singh, Tripty Bhanu Prakash, K.N. Rajamani, Kumar Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India AStar Bioinformatic Institute Bioinformatics Institute Singapore Marwadi Education Foundation's Group of Institutions Faculty of Engineering and Technology Gujarat India

ISBN: (纸本)9798350335095

After emerging in Wuhan, China, in December 2019, the coronavirus 2019 (COVID-2019) quickly spread throughout the world and sparked a pandemic. Covid-19 The pandemic affected human health globally. In a few cases of Covid19 humans faced death situations also. The Respiratory system of the human body gets affected by Covid-19 and the severity of Covid-19 leads to death so there is urgent need of fast and efficient automatic diagnosis system of Covid-19 to help medical experts to diagnose this disease quickly and efficiently. This paper's main objective is to provide a Binary Classification of Covid-19 disease using a dataset obtained from computed tomography. Four Deep Learning existing models ResNet50, Inception V3, DenseNet121 and Efficient Net B0 are employed here. Compared to ResNet50, InceptionV3, and Efficient Net B0, DenseNet121 performs better. DenseNet121 model showed 96.83% as training accuracy and 92% as validation accuracy. © 2023 IEEE.

关键词： Diseases

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Generative AI in Academic Writing: A Comparison of DeepSeek, Qwen, ChatGPT, Gemini, Llama, Mistral, and Gemma

arXiv

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arXiv 2025年

作者： Aydın, Ömer Karaarslan, Enis Erenay, Fatih Safa Džakula, Nebojša Bačanin Manisa Celal Bayar University Faculty of Engineering and Natural Sciences Department of Computer Sciences Manisa Turkey University of Waterloo Faculty of Engineering Management Science and Engineering Ontario Canada Mugla Sitki Kocman University Faculty of Engineering Department of Computer Engineering Mugla Turkey Singidunum University Faculty of Informatics and Computing Belgrade Serbia

Deepseek and Qwen LLMs became popular at the beginning of 2025 with their low-cost and open-access LLM solutions. A company based in Hangzhou, Zhejiang, China, announced its new LLM, DeepSeek v3, in December 2024. Then, Alibaba released its AI model, Qwen 2.5 Max, on January 29, 2025. These tools, which are free and open-source have made a significant impact on the world. Deepseek and Qwen also have the potential to be used by many researchers and individuals around the world in academic writing and content creation. Therefore, it is important to determine the capacity of these new LLMs to generate high-quality academic content. This study aims to evaluate the academic writing performance of both Qwen 2.5 Max and DeepSeek v3 by comparing these models with popular systems such as ChatGPT, Gemini, Llama, Mistral, and Gemma. In this research, 40 articles on the topics of Digital Twin and Healthcare were used. The method of this study involves using generative AI tools to generate texts based on posed questions and paraphrased abstracts of these 40 articles. Then, the generated texts were evaluated through the plagiarism tool, AI detection tools, word count comparisons, semantic similarity tools and readability assessments. It was observed that plagiarism test result rates were generally higher for the paraphrased abstract texts and lower for the answers generated to the questions, but both were above acceptable levels. In the evaluations made with the AI detection tool, it was determined with high accuracy that all the generated texts were detected as AI-generated. In terms of the generated word count comparison, it was evaluated that all chatbots generated satisfactory amount of content. Semantic similarity tests show that the generated texts have high semantic overlap with the original texts. The readability tests indicated that the generated texts were not sufficiently readable. © 2025, CC BY.

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Description and comparison of fault detection algorithms based on a selected building automation device

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Bulletin of the Polish Academy of Sciences: Technical Sciences 2025年第3期73卷

作者： Wlostowska, Sandra Kawa, Bartlomiej Borkowski, Piotr Department of Electrical Apparatus Faculty of Electrical Electronics Computer and Control Engineering Lodz University of Technology Lódź Poland

The article compares selected classification algorithms and those dedicated to anomaly detection. The models used temperature measurements in four rooms simulated in the MATLAB Simscape environment as test signals. The empirical part of the work consists of two parts. In the first one, an example data from the simulated building heating model object, models were built using unsupervised and supervised machine learning algorithms. Then, data from the facility was collected again with changed parameters (failures occurred at times other than the test ones, and the temperature patterns differed from those recorded and used to train the models). The algorithm effects and test signals (temperature changes) were saved in the database. The results were presented graphically in the Grafana program. The second part of the work presents a solution in which the analysis of the operating status of the heating system takes place in real time. Using an OPC server, data was exchanged between the MATLAB environment and the database installed on a virtual machine in the Ubuntu system. The conclusions present the results and collect the authors’ suggestions regarding the practical applications of the discussed classification models. © 2025 The Author(s).

关键词： Support vector machines

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Prescriptive Procedure for Manual Code Smell Annotation

SSRN

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SSRN 2024年

作者： Prokić, Simona Luburić, Nikola Slivka, Jelena Kovačević, Aleksandar Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

Code smells are structures in code that present potential software maintainability issues. Manually constructing high-quality datasets to train ML models for code smell detection is challenging. Inconsistent annotations, small size, non-realistic smell-to-non-smell ratio, and poor smell coverage hinder the dataset quality. These issues arise mainly due to the time-consuming nature of manual annotation and annotators’ disagreements caused by ambiguous and vague smell *** address challenges related to building high-quality datasets suitable for training ML models for smell detection, we designed a prescriptive procedure for manual code smell annotation. The proposed procedure represents an extension of our previous work, aiming to support the annotation of any smell defined by Fowler. We validated the procedure by employing three annotators to annotate smells following the proposed annotation *** main contribution of this paper is a prescriptive annotation procedure that benefits the following stakeholders: annotators building high-quality smell datasets that can be used to train ML models, ML researchers building ML models for smell detection, and software engineers employing ML models to enhance the software maintainability. Secondary contributions are the code smell dataset containing Data Class, Feature Envy, and Refused Bequest, and DataSet Explorer tool which supports annotators during the annotation procedure. © 2024, The Authors. All rights reserved.

关键词： Maintainability

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Fake News Detection Regarding COVID-19 Tweets Using Machine Learning Approaches 8

Fake News Detection Regarding COVID-19 Tweets Using Machine ...

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8th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Hussin, Muhammad Hazimmuddin Mahmud, Yuzi Mohd Hanafiah, Nurul Izzah Azma Nasruddin, Zan Mohd Ariffin, Nor Hapiza Ince, Murat Senel, Fatih Ahmet Universiti Teknologi MARA School of Computing Sciences College of Computing Informatics and Mathematics Shah Alam Malaysia Sohar University Faculty of Business MIS Department Sohar Oman Isparta University of Applied Sciences Department of Computer Technologies Isparta Turkey Suleyman Demiral University Department of Computer Engineering Isparta Turkey

ISBN: (纸本)9798350315516

The pervasiveness of misinformation surrounding the COVID-19 pandemic has garnered heightened attention due to its implications, as a noteworthy proportion of the populace is being exposed to spurious and unsubstantiated narratives concerning the crisis. This research utilizes a dataset sourced from Codalab, comprising 8,560 tweets, with 4,480 labelled as real and 4,080 as fake. The research explores the effectiveness of different machine learning models, including logistic regression (LR), random forest (RF), and deep learning models such as Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). In addition to model comparison, experiments were conducted to analyze the impact of different data splits (70:30, 80:20, and 90:10), batch sizes (16, 32, and 64), and the number of epochs (5, 10, and 15) on model performance. The experiments provided insights into the optimal configurations for the models. The results showcase the model's capabilities, with high accuracy achieved across the different models. Specifically, logistic regression achieved an accuracy of 92%, random forest 91%, Bi-LSTM 93%, and CNN 95%. These findings highlight the potential of deep learning models, particularly CNN, in accurately detecting fake news from COVID-19-related tweets. © 2023 IEEE.

关键词： COVID-19

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Noise Reduction in Industry Based on Virtual Instrumentation

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computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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Detection of Hyperbolic Reflections on Real GPR Data Using YOLO Model Trained with Synthetic Data

Detection of Hyperbolic Reflections on Real GPR Data Using Y...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Željko Bugarinović Milan Gavrilović Aleksandar Ristić Milka Šarkanović Bugarinović Aleksandra Radulović Miro Govedarica Department for computing and control engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

This paper analyzes the application of the YOLO algorithm to automatically recognize hyperbolic reflections on radargrams. A specific shape of hyperbolic reflection on radargrams occurs with objects of circular cross-section that are scanned perpendicular to their axis, such as underground pipes. Requirements for processing a large number of radargrams are increasingly being imposed in practice. Therefore, the development of algorithms for the automated finding of hyperbolic reflections on radargrams has been intensified in recent years. Conventional methods take a lot of time and are more susceptible to noise than algorithms based on deep learning. One of such algorithms is also applied in this paper. The model was trained with 2000 synthetic radargrams generated using gprMax software. The basic idea is reflected in the analysis of the successful detection of hyperbolic reflections on real radargrams using a model trained only with synthetic radargrams. Experimental results show a high level of success.

关键词： YOLO Training Software algorithms Noise Signal processing algorithms Reflection Data models

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