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Fuzzy Aggregator Based Energy Aware RPL Routing for IoT Enabled Forest Environment

Computers, Materials & Continua 2022年第8期72卷 4039-4055页

作者： S.Srividhya Suresh Sankaranarayanan Sergei A.Kozlov Joel J.P.C.Rodrigues Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyChennai603203TamilnaduIndia Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyChennai603203TamilnaduIndia ITMO University St.PetersburgRussia Senac Faculty of Ceará FortalezaCEBrazil Instituto de Telecomunicações Covilhã6201-001Portugal

Forested areas are extremely vulnerable to disasters leading to environmental *** Fire is one among them which requires immediate *** are lot of works done by authors where Wireless Sensors and IoT have been used for forest fire ***,towards monitoring the forest fire and managing the energy efficiently in IoT,Energy Efficient Routing Protocol for Low power lossy networks(E-RPL)was *** were challenges about the scalability of the network resulting in a large end-to-end delay and less packet delivery which led to the development of Aggregator-based Energy Efficient RPL with data Compression(CAAERPL).Though CAA-ERPL proved effective in terms of reduced packet delivery,less energy consumption,and increased packet delivery ratio for varying number of nodes,there is still challenge in the selection of aggregator which is based purely on probability percentage of *** has been research work where fuzzy logic been employed for Mobile Ad-hoc Routing,RPL routing and cluster head selection in Wireless *** has been no work where fuzzy logic is employed for aggregator selection in Energy Efficient *** accordingly,we here have proposed Fuzzy Based Aggregator selection in Energy-efficient RPL for region thereby forming DODAG for communicating to Fog/*** here have developed fuzzy inference rules for selecting the aggregator based on strength which takes residual power,Node degree,and Expected Transmission Count(ETX)as input *** Fuzzy Aggregator Energy Efficient RPL(FA-ERPL)based on fuzzy inference rules were analysed against E-RPL in terms of scalability(First and Half Node die),Energy Consumption,and aggregator node energy *** the analysis,it was found that FA-ERPL performed better than *** were simulated using MATLAB and results.

关键词： Fuzzy logic aggregator IoT RPL routing wireless sensor network

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A Biselection Method Based on Consistent Matrix for Large-Scale datasets

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1992-2005页

作者： Quan, Jinsheng Qiao, Fengcai Yang, Tian Shen, Shuo Qian, Yuhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Institute of Interdisciplinary Studies Changsha410081 China National University of Defense Technology College of Advanced Interdisciplinary Studies Changsha410081 China Shanxi University Institute of Big Data Science and Industry Taiyuan030006 China

Biselection (feature and sample selection) enhances the efficiency and accuracy of machine learning models when handling large-scale data. Fuzzy rough sets, an uncertainty mathematical model known for its excellent interpretability, are widely used in machine learning, particularly for feature selection. While the consistent matrix has significantly improved the computational efficiency and scalability of feature selection, like most fuzzy rough set-based methods, it focuses only on feature selection and seldom incorporates sample selection. This feature-centric approach can limit classification performance, particularly in noisy and large-scale datasets where both features and samples require judicious selection. To overcome these limitations, this article explores the integration of sample selection with feature selection. First, we introduce a β-consistent granulation method to generate more accurate and concise fuzzy information granules. In addition, a novel membership function is employed to distinguish noise samples and irrelevant features simultaneously. As a result, a biselection algorithm with lower computational complexity is proposed to select high-quality features and samples. Numerical experiments demonstrate that, compared to eleven representative algorithms, our proposed method achieves an average accuracy improvement of 9.66% and a 933-fold increase in efficiency. © 1993-2012 IEEE.

关键词： Rough sets Noise Feature extraction Classification algorithms Accuracy Machine learning Information systems data models Computational efficiency Approximation algorithms

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Cloud Intrusion Detection System Based on Honeynet, Honeywell, Honeypot, and Honeytoken Strategies

Cloud Intrusion Detection System Based on Honeynet, Honeywel...

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International Conference on data science and Network Engineering, ICDSNE 2023

作者： Yasotha, B. Arthy, M. Shoba, L.K. Loganathan, Muralidaran Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Computer Science and Information Systems Engineering St. Joseph University Dares Salaam Tanzania United Republic of

ISBN: (纸本)9789819967544

The security aspect of cloud computing is much more challenging for researchers. Preventing the attack requires knowledge about the type of attack, its origin, and how vulnerabilities and tools are used for the attack. The cloud security methodology protects customer data, information, and applications from attackers. Due to digitalization, the volume of data is increased. The protection of data is very challenging for cloud service providers. This work proposes a new cloud intrusion detection system security infrastructure based on Honeynet, Honeywell, Honeypot, and Honeytoken Strategies. The proposed strategy effectively identifies intrusion detection, attack behavior, and attack scenario. The testing of data was carried out in OpenStack environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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FairTP: A Prolonged Fairness Framework for Traffic Prediction 39

FairTP: A Prolonged Fairness Framework for Traffic Predictio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Jiangnan Yang, Yu Shen, Jiaxing Wang, Senzhang Cao, Jiannong School of Computer Science and Engineering Central South University China Centre for Learning Teaching and Technology The Education University of Hong Kong Hong Kong School of Data Science Lingnan University Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Traffic prediction is pivotal in intelligent transportation systems. Existing works focus mainly on improving overall accuracy, overlooking a crucial problem of whether prediction results will lead to biased decisions by transportation authorities. In practice, the uneven deployment of traffic sensors in different urban areas produces imbalanced data, making the traffic prediction model fail in some urban areas and leading to unfair regional decision-making that eventually severely affects equity and quality of residents’ life. Existing fairness machine learning models struggle to maintain fair traffic prediction over prolonged periods. Although these models might achieve fairness at certain time slots, this static fairness will break down as traffic conditions change. To fill this research gap, we investigate prolonged fair traffic prediction, introducing two novel fairness metrics, i.e., region-based static fairness and sensor-based dynamic fairness, tailored to fairness fluctuations over time and across areas. An innovative prolonged fairness traffic prediction framework, namely FairTP, is then proposed. FairTP achieves prolonged fairness by alternating between "sacrifice" and "benefit" the prediction accuracy of each traffic sensor or area, ensuring that the number of these two actions are balanced over time. Specifically, FairTP incorporates a state identification module to discriminate whether the traffic sensors or areas are in a "sacrifice" or "benefit" state, thereby enabling prolonged fairness-aware traffic predictions. Additionally, we devise a state-guided balanced sampling strategy to select training examples to further enhance prediction fairness by mitigating the performance disparities among areas with uneven sensor distribution over time. Extensive experiments in two real-world datasets show that FairTP significantly improves prediction fairness without causing significant accuracy degradation. Copyright © 2025, Association for the Advancement of Ar

关键词： Traffic control

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Securing 3D Point and Mesh Fog data Using Novel Chaotic Cat Map

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Computers, Materials & Continua 2023年第3期74卷 6703-6717页

作者： K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md Areej Malibari Department of Data Science and Business Systems School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of *** major concerns in this domain are security and ***,attaining a reliable level of confidentiality in the fog computing environment is a pivotal *** different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing ***,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog *** Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,*** boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat *** sequence generated by this map is used to transform the coordinates of the fog *** improved range of the proposed map is depicted using bifurcation *** quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate *** also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical *** experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

关键词： Chaotic cat map fog computing encryption 3D point fog 3D mesh

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From Laughter to Inequality: Annotated dataset for Misogyny Detection in Tamil and Malayalam Memes 30

From Laughter to Inequality: Annotated Dataset for Misogyny ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ponnusamy, Rahul Pannerselvam, Kathiravan Rajiakodi, Saranya Kumaresan, Prasanna Kumar Thavareesan, Sajeetha Sivagnanam, Bhuvaneswari Anshid, K.A. Kumar, Susminu S. Buitelaar, Paul Chakravarthi, Bharathi Raja Data Science Institute University of Galway Ireland Department of Computer Science Central University of Tamil Nadu India Department of Computing Eastern University Sri Lanka WMO Imam Gazzali Arts and Science College Kerala India Revemax International Pvt.Ltd. Bangalore India

ISBN: (纸本)9782493814104

In this digital era, memes have become a prevalent online expression, humor, sarcasm, and social commentary. However, beneath their surface lies concerning issues such as the propagation of misogyny, gender-based bias, and harmful stereotypes. To overcome these issues, we introduced MDMD (Misogyny Detection Meme dataset) in this paper. This article focuses on creating an annotated dataset with detailed annotation guidelines to delve into online misogyny within the Tamil and Malayalam-speaking communities. Through analyzing memes, we uncover the intricate world of gender bias and stereotypes in these communities, shedding light on their manifestations and impact. This dataset, along with its comprehensive annotation guidelines, is a valuable resource for understanding the prevalence, origins, and manifestations of misogyny in various contexts, aiding researchers, policymakers, and organizations in developing effective strategies to combat gender-based discrimination and promote equality and inclusivity. It enables a deeper understanding of the issue and provides insights that can inform strategies for cultivating a more equitable and secure online environment. This work represents a crucial step in raising awareness and addressing gender-based discrimination in the digital space. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Classification (of information)

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Enhancing EEG-based Covert Speech Decoding through Knowledge Transfer

Enhancing EEG-based Covert Speech Decoding through Knowledge...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Zhiwei Jiang, Muyun Liu, Chenyu Wu, Min Lu, Jia Gulyás, Balázs Guan, Cuntai College of Computing and Data Science Nanyang Technological University Singapore Singapore Yong Loo Lin School of Medicine National University of Singapore Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore

ISBN: (纸本)9798350368741

Covert speech, the imagination of articulation without any actual movement of vocal apparatus, can aid individuals with speech impairments. Recent studies have shown the possibilities of decoding covert speech from non-invasive techniques such as electroencephalogram (EEG). Decoding covert speech from EEG may find broader applications than invasive approaches, but it poses additional challenges due to its less distinct speech-related brain patterns compared with overt speech. In this paper, we propose a novel knowledge transfer framework to build a pretrained model for covert speech from overt speech, including both EEG and audio data. We further integrate acoustic features to enforce a direct neural-to-phonetic mapping from EEG signals to audio representations using data from other subjects. Finally, we fine-tune the pretrained model with covert EEG from the target subjects for decoding. To validate the proposed strategy, we test it on our covert/overt EEG dataset collected from 54 subjects with concurrent EEG and audio recording. Results show that the proposed framework outperforms SOTA methods in terms of classification accuracy of covert speech decoding using EEG. © 2025 IEEE.

关键词： Brain Computer Interface (BCI) Classification Covert speech decoding EEG Pretraining

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An Evaluation of Security Features for Preventing Illegal Distribution of Digital Content Using Quantitative Methods 4

An Evaluation of Security Features for Preventing Illegal Di...

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4th International Conference on computing and Information Technology, ICCIT 2025

作者： Bin Mohd Razak, Muhammad Isma Farhan Majid, Mazlina Abdul Ajra, Husnul Islam, Md. Shohidul Abdullah, Aziman Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Faculty of Computing Kuantan Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Faculty of Computing Centre of Excellence for Artificial Intelligent & Data Science Kuantan Malaysia

ISBN: (纸本)9798350353839

In the current digital landscape, safeguarding digital content from unauthorized sharing is of paramount importance. This study concerns three key issues: the prevalent unauthorized sharing of content, user dissatisfaction with existing security measures, and the lack of alignment between expert opinions and user perceptions. This research addresses three critical problem statements. Firstly, it seeks to identify the current security features utilized to prevent the unauthorized distribution of digital content. Secondly, it aims to assess user perceptions of these features through a meticulously constructed survey;and lastly, the study endeavors to compare findings from a thorough literature review with user perceptions. Methodologically, this research encompasses an exhaustive literature review and a user perception survey to gain insights into effective security measures and recommendations for enhancement. The expected outcomes include a more profound understanding of the effectiveness of current security measures and suggestions for enhancing digital content protection. Through this study, it has been emphasized the significance of digital content security and put forward collaborative avenues for improvement. © 2025 IEEE.

关键词： digital content distribution illegal quantitative security

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MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-Computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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Convergence Analysis of Hierarchical Split Federated Learning

Convergence Analysis of Hierarchical Split Federated Learnin...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Hualei Du, Jun Hou, Xiangwang Jiang, Chunxiao Wang, Jintao Niyato, Dusit Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Nanyang Technological University College of Computing and Data Science Nanyang Avenue 639798 Singapore

ISBN: (纸本)9798350351255

Federated Learning (FL) enables distributed intelligence in Internet of Things (IoT) networks, facilitating decentralized machine learning without the need for exchanging raw data. However, the growing complexity of training models significantly hinders their deployment on resource-constrained IoT devices. To address this challenge, Split Federated Learning (SFL) has emerged as a promising solution by partitioning the entire model into client-side and server-side sub-models to alleviate the computational burden on IoT devices. Considering that the client-edge-cloud architecture can enhance data privacy, support connections to a wider range of devices, and reduce communication costs, we explore a hierarchical SFL (HierSFL) system. This system is supported by a HierSFL algorithm that allows for different aggregation frequencies between the client-side and server-side sub-models. Then, we present a convergence analysis of HierSFL that quantifies the effects of client-side and server-side model aggregation on learning performance, providing a theoretical foundation. Empirical experiments verify the theoretical analysis and demonstrate the superiority of the hierarchical architecture within a wireless IoT network. In particular, it is validated that adopting different aggregation frequencies can enhance the training performance. Moreover, the HierSFL algorithm outperforms traditional hierarchical FL algorithm, achieving superior test accuracy in a shorter time. © 2024 IEEE.

关键词： Federated learning

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