EAI Endorsed Transactions on Internet of Things
type: channel
description: EAI Endorsed Transactions on Internet of Things is open access, a peer-reviewed scholarly journal focused on all areas related to the technologies and application fields related to the Internet of Things. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications on a quarterly frequency. INDEXING: Scopus, DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions
publisher: European Alliance for Innovation (EAI)
language: en
publication name: EAI Endorsed Transactions on Internet of Things
issn: 2414-1399
copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
items: System of digitizing and paying of civil status documents via an RFID card
items: Improved PIC16f877 microcontroller timer register to delimit the execution time of an operation
items: Research and Design a Low Power Electronic Shelf Label Based on E-paper Display and LoRa Technology
items: A Comprehensive Survey on Objective Functions in RPL Routing with Various Networking and Application Scenarios
items: A Trust Based Energy Efficient Routing Design Based on Hybrid Particle Swarm Optimization (HPSO) for Wireless Sensor Networks
items: A Quantitative Framework for the Selection of Hybrid Consensus Mechanisms in Blockchain-IoT Systems
items: Enhancing 5G Traffic Management with Programmable Intelligence and Open RAN Integration
items: Migration of Microservices Execution Contexts Between Processing Zones in Software-Defined Vehicular Fog Networks
items: Conformer network-guided speech recognition for smart home Internet of Things system
items: Edge Computing for Computer Vision in IoT: Feasibility and Directions
items: Extending JASPER for Jammer-Angle Allocation Evaluation: Testing Algorithm Robustness Under Jammer Failures and Varying Resource Conditions
items: An Improved & Smart Clock Synchronisation Model for Emblematic IoT Applications
items: Effective Object detection and Tracking using Attention-Driven YOLO v9 Model with Multi-Stage Cascaded Convolutional Model
items: Optimization of Deep Learning Technique for OFDM Receivers in 6G Wireless Communications
items: Facial Recognition Enabled Smart Security Lock System Using Machine Learning Approach
items: Enhanced security based on Context Aware Proactive Trust Aware Routing Protocol using secure Preemptive Verifiable Key Handover Policy for VANET Environment
items: Advanced Attention-Enhanced BiLSTM-GRU Model for Real vs. Fake News Detection
items: Synergistic Integration of Quantum and Classical Machine Learning Models for High-Fidelity Asteroid Hazard Detection
items: Variation of Production Line Energy Consumption: Stochastic Process Models for Single and Multiple Machine Systems
items: A Secure and Efficient Blockchain-Based Framework for Smart Cities Using Physics-Informed Neural Networks
items: Interlink Platform for School, Higher and Technical Education in India: Design Platform
items: All-in-Net: Scorekeeping in Basketball Training using a Mobile Phone Camera and Image
items: Spellbound Kannada: Harnessing Conditional Generative Adversarial Networks for Transformative Word Suggestion Systems in Kannada Language Processing
items: Innovative Human Interaction System to Predict College Student Emotions Using the Extended MASK-R-CNN Algorithm
items: A New Approach for Solving Fractional Differential Equations Incorporating Ramadan Group Transform and Machine Learning
items: Assessment of CatBoost for Diabetes Prevention in Comparison to XGBoost: AI model capable of predicting the onset of diabetes
items: Navigating the Future of Intelligent Transportation: Challenges and Solutions in 6G V2X and V2V Networks
items: Wireless 5G Network in Edge Computing Based On MIMO with Federated Learning and Clustering Integrated Reinforcement Learning
items: Advancing IoT Security with an Innovative Machine Learning Paradigm for Botnet Attack Detection
items: Revitalizing Image Retrieval: AI Enhancement and Metaheuristic Algorithm Adaptation

System of digitizing and paying of civil status documents via an RFID card
type: item
description: Nowadays, with the remarkable advancement of technology in almost all fields of activity, we notice a considerable decrease in data search time. Thus we are witnessing the birth of new technologies and applications that continue to make our lives easier and in the most beautiful ways. However, the computerization of systems remains a valuable aid, particularly in the field of administrations such as civil status. At the time, before the idea of computerizing civil status in Senegal, all citizens' files were managed on paper. Which continued to create problems within these services (loss of transcription, loss of time for research), queues and jostling. We had to make an observation that all civil status documents, in any case a good number in the world are computerized so that obtaining papers has become simpler than usual while in Senegal obtaining civil status documents is generally done on paper, but there are areas where the process is digitalized. First of all, the user presents the badge or RFID card which is scanned by the RFID module and the system checks the existence of the card at the database level. Then, if the card is registered, it connects to the network in order to access the web page to enter its password. And finally the person obtains his request if the password is correct. The account is recharged manually by the platform administrator or automatically by the user via mobile money operators such as Orange money, Wave and others.
subject: Technical article
  topic: section
subject: RFID card
  topic: keywords
subject: civil status documents
  topic: keywords
subject: paying
  topic: keywords
subject: IoT (Internet of Things)
  topic: keywords
creator: Mohamed E.A. Ouesse
creator: Diatta Baboucar
creator: Basse Adrien
creator: Bouallegue Ridha
rights: Copyright (c) 2025 Mohamed E.A. Ouesse, Diatta Baboucar, Basse Adrien, Bouallegue Ridha https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-18
publication date: 2025-11-18
doi: 10.4108/eetiot.10970

Improved PIC16f877 microcontroller timer register to delimit the execution time of an operation
type: item
description: We often plan and build applications that carry out various processes requiring precise time management. Time management is attributed to the microcontroller, which can use various techniques such as instruction execution time, which requires delicate control of loops, or the use of functions such as delay, which cause your program to lose time in order to create delays. The problem with this kind of time management is that the time lost is of unknown duration. To alleviate this problem, new microcontrollers almost always offer specialized time-counting and time-management circuits, known as timers. A timer is a kind of clock built into the microcontroller, enabling the duration of an event to be measured. Their use is not complicated, but requires a good understanding of the structure of the various timers on a microcontroller. In this paper, we’ve chosen to demonstrate the improved and management of timers on Microchip’s PIC 16F877 microcontroller using assembly language.
subject: Research article
  topic: section
subject: PIC16f877 microcontroller
  topic: keywords
subject: timer register
  topic: keywords
creator: Souleymane Mbathie
creator: Mohamed Ouesse
creator: Baboucar Diatta
creator: Adrien Basse
creator: Ridha Bouallegue
rights: Copyright (c) 2025 Souleymane Mbathie, Mohamed Ouesse, Baboucar Diatta, Adrien Basse, Ridha Bouallegue https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-14
publication date: 2025-11-14
doi: 10.4108/eetiot.10862

Research and Design a Low Power Electronic Shelf Label Based on E-paper Display and LoRa Technology
type: item
description: Electronic Shelf Labels (ESL) are increasingly replacing traditional paper-based pricing systems due to their flexible update capabilities, ease of management, and environmental benefits. This paper presents the research and development of an optimized ESL device that incorporates LoRa technology and a 2.13-inch E-paper display capable of using low power consumption. By analyzing and considering design factors, the ESL device is engineered to achieve compact dimension, low energy consumption, long lifespan, and improved communication range. The paper also details an efficient operational workflow for integrating these devices into a system. The measurement results indicate that the ESL device can have a lifespan exceeding 5 years, highlighting its efficiency in energy consumption within an ESL system. The device in this paper can operate reliably at distances of up to 23 meters in an obstacle-rich environment, achieving a packet delivery rate of over 96%.
subject: Research article
  topic: section
subject: Internet of things
  topic: keywords
subject: LoRaWAN
  topic: keywords
subject: Miniature Antenna
  topic: keywords
subject: Low Power Wide Area Network
  topic: keywords
subject: Electronic Shelf Label
  topic: keywords
subject: E-paper
  topic: keywords
creator: Le Huy Trinh
creator: Thanh-Tuan Phan
creator: Fabien Ferrero
creator: Nguyen Vu Truong
rights: Copyright (c) 2025 Le Huy Trinh, Thanh-Tuan Phan, Fabien Ferrero, Nguyen Vu Truong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-10
publication date: 2025-10-10
doi: 10.4108/eetiot.9488

A Comprehensive Survey on Objective Functions in RPL Routing with Various Networking and Application Scenarios
type: item
description: Technological breakthroughs in the Internet of Things (IoT) have positioned the Routing Protocol for Low-Power and Lossy Networks (RPL) as a cornerstone for enabling connectivity in resource-constrained and highly dynamic environments. The Objective Function (OF) lies at the heart of RPL, which guides parent selection and route optimization. However, conventional OFs are mostly limited to basic metrics, frequently overlook critical factors such as link heterogeneity, dynamic traffic patterns, energy fairness, reliability, and application-specific Quality of Service (QoS) demands. This survey presents a systematic and technically rigorous review of 108 influential studies published between 2015 and 2024, aimed at focusing on the multidimensional challenges of OF design across varied network environments and application scenarios. Rather than offering a broader overview like existing surveys, it critically assesses the adaptability of different OFs, the trade-offs they introduce, and evaluates their impact on routing performance, while identifying unresolved research gaps that limit scalability, interoperability, and practical deployment. It further highlights emerging solutions such as multi-metric optimization, context-aware routing, and machine learning–based OFs as more promising strategies to enhance the resilience and efficiency of RPL. By integrating fragmented knowledge into a cohesive framework, this survey not only strengthens theoretical understanding but also outlines a research agenda for developing next-generation OFs that are adaptive, cross-domain, and ready for practical implementation, thereby creating lasting impact for future IoT deployments.
subject: Research article
  topic: section
subject: Industrial Internet of Things (IIoT)
  topic: keywords
subject: Objective function (FO)
  topic: keywords
subject: RPL
  topic: keywords
subject: Quality of Service (QoS)
  topic: keywords
subject: Smart Applications
  topic: keywords
creator: Ibrahim Ali Alnajjar
rights: Copyright (c) 2025 Ibrahim Ali Alnajjar https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-17
publication date: 2025-09-17
doi: 10.4108/eetiot.9683

A Trust Based Energy Efficient Routing Design Based on Hybrid Particle Swarm Optimization (HPSO) for Wireless Sensor Networks
type: item
description: Congestion in wireless sensor networks (WSNs) reduces resource availability, often leading to sensor node failures and misbehavior. Additionally, high energy consumption decreases the overall lifespan and performance of the network. To address these limitations, this paper presents a trust-based and congestion-aware optimization method for WSNs. The proposed strategy consists of two main stages. In the first stage, congestion levels and node trust values are evaluated to derive an optimal congestion metric. In the second stage, a Hybrid Particle Swarm Optimization (HPSO) algorithm is applied to determine optimal data routing paths from Sensor Nodes (SNs) to the Base Station (BS), considering both distance and trust-congestion parameters. The proposed HPSO combines the immigration and emigration processes of the Biogeography-Based Optimization (BBO) algorithm with the mutation process of Particle Swarm Optimization (PSO) to achieve efficient data distribution. Experimental comparisons with existing methods demonstrate that the proposed approach significantly improves performance in terms of energy consumption, latency, packet delivery ratio (PDR), and network lifetime.
subject: Research article
  topic: section
subject: Trust congestion
  topic: keywords
subject: HPSO
  topic: keywords
subject: congestion index
  topic: keywords
subject: transmission
  topic: keywords
subject: latency
  topic: keywords
subject: Packet Delivery Ration (PDR)
  topic: keywords
creator: S. Gnana Selvan
creator: G. Gifta Jerith
creator: C. Mahesh
creator: S. Ravikumar
creator: S. Samsudeen Shaffi
creator: S. Jagadeesh
rights: Copyright (c) 2025 S. Gnana Selvan, G. Gifta Jerith, C. Mahesh, S. Ravikumar, S. Samsudeen Shaffi , S. Jagadeesh https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-16
publication date: 2025-09-16
doi: 10.4108/eetiot.9427

A Quantitative Framework for the Selection of Hybrid Consensus Mechanisms in Blockchain-IoT Systems
type: item
description: INTRODUCTION: The application of blockchain technology to Internet of Things (IoT) systems offers substantial potential for enhancing security, but traditional consensus mechanisms are ill-suited for resource-constrained environments. While hybrid consensus solutions have emerged as a promising alternative, a systematic framework for their classification and evaluation is notably absent. OBJECTIVES: This study addresses this critical gap by introducing a novel, application-driven framework for analyzing hybrid consensus mechanisms, underpinned by a quantitative synthesis of performance benchmarks. METHODS: We analyze diverse architectures—including combinations of Proof of Work (PoW) and Proof of Stake (PoS), PBFT-enhanced systems, and hierarchical models—through the lens of specific IoT application priorities, such as latency, energy efficiency, and scalability. Case studies of IOTA's Tangle, IoTeX's Roll-DPoS, and Hyperledger Fabric illustrate these practical trade-offs. RESULTS: Our framework reveals not only primary performance trade-offs but also critical "second-order" complexities, such as emergent vulnerabilities at the intersection of different consensus layers. CONCLUSION: Our findings demonstrate that this structured, quantitatively-grounded approach provides an effective methodology for designing and selecting regulatory-compliant hybrid consensus solutions for specific IoT applications.
subject: Review article
  topic: section
subject: Blockchain Technology
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: Hybrid Consensus Mechanisms
  topic: keywords
subject: Proof of Work (PoW)
  topic: keywords
subject: Proof of Stake (PoS)
  topic: keywords
subject: Practical Byzantine Fault Tolerance (PBFT)
  topic: keywords
subject: Hierarchical Consensus
  topic: keywords
subject: Reputation-based Systems
  topic: keywords
subject: Scalability
  topic: keywords
subject: Blockchain
  topic: subjects
subject: Internet of Things (IoT)
  topic: subjects
creator: N. A. Natraj
creator: J. J. Midhunchakkaravarthy
creator: Brojo Kishore Mishra
rights: Copyright (c) 2025 N. A. Natraj, J. J. Midhunchakkaravarthy, Brojo Kishore Mishra https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-19
publication date: 2025-11-19
doi: 10.4108/eetiot.10249

Enhancing 5G Traffic Management with Programmable Intelligence and Open RAN Integration
type: item
description: INTRODUCTION: 5G networks are complex. They must handle different types of connections. These networks support industries, cities and mobile users. Managing traffic is difficult. Traditional methods are not efficient. OBJECTIVES: This paper introduces a software framework called ns-O-RAN. It combines a real-world RAN Intelligent Controller with a network simulator. This allows testing AI solutions without expensive hardware. The study also proposes a smart handover method. METHODS: The goal is to reduce delays and improve speed. The new method uses deep reinforcement learning (DRL). DRL learns the best way to assign users to base stations. The framework collects a large amount of data. It trains the AI system using this data. The model learns from past network conditions. It then makes better decisions for the future. RESULTS: The proposed solution increases network efficiency. The researchers tested their model. They compared it with traditional handover methods. This means faster speeds and fewer connection losses. The framework also enables real-time monitoring. It detects network issues quickly and adapts to changing conditions. This ensures stable and high-quality connections for users. CONCLUSION: This approach supports different types of applications. It works well for video streaming, voice calls and industrial automation. This work has important implications. It helps telecom providers improve service quality. It also reduces operational costs. Researchers and engineers can use this framework for further development.
subject: Research article
  topic: section
subject: 5G networks
  topic: keywords
subject: open RAN
  topic: keywords
subject: deep reinforcement learning
  topic: keywords
subject: network efficiency
  topic: keywords
subject: spectral efficiency
  topic: keywords
subject: artificial intelligence
  topic: keywords
creator: U.S.B.K. Mahalaxmi
creator: Jeevana Sujitha Mantena
creator: V.V. Jaya Rama Krishnaiah
creator: Vatsala Anand
creator: B. Rajani
creator: Inakoti Ramesh Raja
creator: Pavuluri Girija Krishna Sirisha
rights: Copyright (c) 2025 U.S.B.K. Mahalaxmi, Jeevana Sujitha Mantena, V.V. Jaya Rama Krishnaiah, Vatsala Anand, B. Rajani, Inakoti Ramesh Raja, Pavuluri Girija Krishna Sirisha https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-01
publication date: 2025-09-01
doi: 10.4108/eetiot.9278

Migration of Microservices Execution Contexts Between Processing Zones in Software-Defined Vehicular Fog Networks
type: item
description: The growing need for time-sensitive applications in vehicular networks makes Fog Computing a promising model to orchestrate cloud-based services consumed by vehicle nodes, as it brings computational resources and decision-making processes near to vehicles. However, vehicular mobility presents significant challenges for implementing Fog Computing Services that require low latency. The dynamic nature of vehicle movement means that the physical location of computational resources (Fog Nodes) relative to vehicles is constantly changing. Consequently, maintaining a consistent and reliable low-latency communication path becomes challenging. Recent research suggests that the Software-Defined Networking paradigm can optimize Vehicular Fog Computing Networks in resource and service management. In this paper, a dynamic methodology is proposed for the migration of the microservice execution context between processing zones in Software-Defined Vehicular Fog Computing Networks (SDVFN). This approach was tested using a simulated use case in our SDVFN Simulation Framework, designed to support research on dynamic microservice orchestration in SDVFN, taking into account vehicular mobility.
subject: Research article
  topic: section
subject: Vehicular Fog Networks
  topic: keywords
subject: Software-Defined Vehicular Fog Networks
  topic: keywords
subject: Task Allocation
  topic: keywords
subject: Mobility-Responsive Microservice Orchestration
  topic: keywords
creator: Leonel D.C. Alvarenga
creator: Pedro Sousa
creator: António Costa
rights: Copyright (c) 2025 Leonel D.C. Alvarenga, Pedro Sousa, António Costa https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-19
publication date: 2025-08-19
doi: 10.4108/eetiot.9987

Conformer network-guided speech recognition for smart home Internet of Things system
type: item
description: In response to the fact that the World is gradually entering an aging society, and the problem that traditional Internet of Things (IoT) systems are operationally complex and lack humanization, a conformer network-guided speech recognition for smart home Internet of Things system is proposed in this paper. Firstly, by introducing a voice recognition module with an embedded processor, not only traditional voice recognition has been achieved, but also cloud transmission of voice has been realized, breaking through the bottleneck of low computing and storage capabilities of the main control chip. Then, by using Internet of Things technology, the complex algorithms are transferred to the cloud for execution. There is a significant improvement in voice recognition. By leveraging the distributed storage feature of the cloud, a user-specific voice database can be established categorically. This enables the provision of a vast amount of data basis when users are learning. In response to the shortcomings of the existing Conformer speech recognition model, such as insufficient extraction ability of time-frequency features, redundant model structure and large number of parameters, this paper proposes a speech recognition model based on asymmetric convolution and gated feed-forward neural network. Different-sized asymmetric convolutions are used to perform multi-scale fusion and down-sampling on the time-frequency features of the speech sequence. This not only enhances the model's ability to extract time-frequency features but also effectively reduces the information loss during down-sampling. At the same time, the gated feed-forward module is introduced to replace the double half-step feed-forward network in Conformer, reducing the number of network parameters while simplifying the model structure. Finally, based on a large amount of data, the system gradually builds a personalized speech recognition library for the user through learning. Through experiments, the effectiveness…
subject: Research article
  topic: section
subject: Internet of Things
  topic: keywords
subject: conformer network
  topic: keywords
subject: asymmetric convolution
  topic: keywords
subject: speech recognition
  topic: keywords
subject: gated feed-forward neural network
  topic: keywords
creator: Shengjun Huang
creator: Dong-hyun Kim
rights: Copyright (c) 2025 Shengjun Huang, Dong-hyun Kim https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-20
publication date: 2025-11-20
doi: 10.4108/eetiot.9818

Edge Computing for Computer Vision in IoT: Feasibility and Directions
type: item
description: The convergence of decentralized architectures integrating Machine Learning, Computer Vision and Low Power Wide Area Networks is increasingly becoming an integral part of our daily existence. Internet of Things serves as a real-time data conduit enhancing decision making via embedded technology and continuous data exchange. This paper explores the feasibility of Edge Computing as a foundational pillar in this evolving landscape. We experiment under real world, dynamic conditions, evaluate the technological aspects, strategies, process flows and key observations under the broad Edge Computing domain. Research pathways include Multi-access Edge topologies in future 6G networks, model quantization, and satellite-enhanced communication platforms. Additionally, a discussion is added supporting the advanced AI functionalities, including zero-shot learning, multi modal perception, and decentralized generative AI, thereby broadening the scope of intelligent applications across various domains. The significance and research objective of this study are threefold: (1) evaluation of LoRaWAN and satellite IoT communication strategies, (2) analysis of CV workloads on edge hardware and (3) future research directions where Edge Computing can support low-latency, energy-efficient and socially impactful IoT applications. By explicitly addressing these aspects, we aim to establish a clear link between the technological feasibility, ultimately with a practical and socioeconomic relevance.
subject: Research article
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: LoRaWAN
  topic: keywords
subject: Edge AI
  topic: keywords
subject: SatIoT
  topic: keywords
subject: Precision Agriculture
  topic: keywords
subject: Embedded Systems
  topic: keywords
subject: Resource Management
  topic: keywords
creator: Panagiotis Savvidis
creator: George A. Papakostas
rights: Copyright (c) 2025 Panagiotis Savvidis, George A. Papakostas https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-28
publication date: 2025-10-28
doi: 10.4108/eetiot.9404

Extending JASPER for Jammer-Angle Allocation Evaluation: Testing Algorithm Robustness Under Jammer Failures and Varying Resource Conditions
type: item
description: JASPER is a scenario-driven simulation testbed for analyzing jammer-angle allocation (JAA) strategies in anti-UAV defense systems. It provides a modular simulation environment where users can run controlled experiments that reflect realistic anti-UAV scenarios. In this extended version, we introduce a new failure mechanism that allows jammers to fail dynamically during simulation. This addition enables the testing of algorithmic robustness under partial system degradation and is showcased through a newly designed case study focused on how algorithms recover and adapt after resource loss. We also conduct a sensitivity analysis by varying the number of jammers, exploring trade-offs between coverage, swivel workload, and algorithm runtime. Beyond enabling performance comparisons under realistic constraints, these experiments also reinforce the reliability and adaptability of the JASPER platform. Together, these additions make JASPER a more expressive and fault-aware testbed for evaluating directional jamming strategies under realistic conditions.
subject: Research article
  topic: section
subject: Simulation-based test and evaluation
  topic: keywords
subject: Agent-based simulation
  topic: keywords
subject: Jammer allocation algorithms
  topic: keywords
subject: Threat evaluation and weapon allocation
  topic: keywords
subject: Anti-drone systems
  topic: keywords
creator: Ahmet Burak Barakli
creator: Okan Topçu
creator: Halit Oğuztüzün
rights: Copyright (c) 2025 Ahmet Burak Barakli, Okan Topçu, Halit Oğuztüzün https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-18
publication date: 2025-07-18
doi: 10.4108/eetiot.9702

An Improved & Smart Clock Synchronisation Model for Emblematic IoT Applications
type: item
description: The Internet of Things (IoT) has revolutionized modern living by facilitating seamless data exchange between interconnected devices across diverse applications such as healthcare, smart cities, and industrial automation. These devices operate in dynamic and distributed environments, where accurate timekeeping is crucial for synchronizing processes, ensuring reliable communication, and maintaining data consistency. Clock synchronization plays a critical role in coordinating the activities of IoT entities, especially when processing and communication require precision. To address challenges associated with synchronization errors, this paper introduces a novel clock synchronization algorithm grounded in linear quadratic regression. By leveraging a linear model to estimate clock parameters such as skew and offset, the algorithm improves the reliability and accuracy of time synchronization in IoT networks. The effectiveness of the proposed algorithm was evaluated using key statistical metrics, including R-Square and Root Mean Square Error (RMSE). The results demonstrated the superiority of the algorithm, achieving an R-Square error value of 0.71% and an RMSE of 0.379%, outperforming traditional synchronization methods. Furthermore, the stability and robustness of the model were validated through a correlation coefficient analysis, which revealed a strong correlation of 86% between the variables. These findings underscore the algorithm's potential to significantly reduce synchronization errors, thereby enhancing the efficiency and reliability of IoT applications. By addressing a critical challenge in IoT communication, this research contributes to the advancement of time-sensitive applications and underscores the importance of innovative synchronization mechanisms in the growing IoT ecosystem.
subject: Technical article
  topic: section
subject: Clock Synchronisation
  topic: keywords
subject: Clock Skew
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: Sensors
  topic: keywords
subject: Linear Quadratic Model
  topic: keywords
subject: Regression
  topic: keywords
creator: Divya Upadhyay
creator: Ashwani Kumar Dubey
rights: Copyright (c) 2025 Divya Upadhyay, Ashwani Kumar Dubey https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-18
publication date: 2025-06-18
doi: 10.4108/eetiot.5579

Effective Object detection and Tracking using Attention-Driven YOLO v9 Model with Multi-Stage Cascaded Convolutional Model
type: item
description: INTRODUCTION: Object detection and tracking are essential for computer vision, particularly for vehicle monitoring within digital images and video streams. Traditional methods, such as background subtraction and template matching, rely on heuristic algorithm and handcrafted features, which often struggles with diverse vehicle appearance and complex backgrounds. These techniques, while foundational, exhibit limitations in flexibility and scalability, resulting in lower accuracy and high computational costs. OBJECTIVES: In contrast, advanced Deep Learning (DL) approaches, particularly those utilizing Conventional Neural Network (CNNs), have revolutionized the field by enabling automatic feature extraction from large datasets. Despite their advantages, existing DL models like You Only Look Once (YOLO) face challenges in detecting small or closely packed vehicles and can be computationally intensive. METHODS: This study proposed an Attention Driven YOLO v9 architecture that integrates with a proposed mechanism combining spatial and channel attention to detect the small size vehicle accurately. RESULTS: Additionally the architecture incorporates multi stage cascaded convolution layers to enhance the feature extraction and robustness against occlusion and background noise. The model is trained using the UA-DETRAC dataset, providing a rich set of images for learning. CONCLUSION: Performance evaluation metric such as Mean Average Precision (mAP), precision, recall, and tracking accuracy demonstrating significant improvement over traditional methods and existing state of the art models. This research contributes to the field by addressing the limitations of previous studies through technique to speed and accuracy in vehicle detection and tracking.
subject: Research article
  topic: section
subject: Object Detection
  topic: keywords
subject: Tracking
  topic: keywords
subject: Vehicle
  topic: keywords
subject: CNN
  topic: keywords
subject: YOLO v9
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Attention mechanism
  topic: keywords
subject: Spatial mechanism
  topic: keywords
creator: Krishna Mohan A
creator: P. V. N. Reddy
creator: K. Satyma Prasad
rights: Copyright (c) 2025 Krishna Mohan A, P. V. N. Reddy, K. Satyma Prasad https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-11
publication date: 2025-06-11
doi: 10.4108/eetiot.8231

Optimization of Deep Learning Technique for OFDM Receivers in 6G Wireless Communications
type: item
description: INTRODUCTION: This paper presents an innovative deep learning-based optimization technique for orthogonal frequency division multiplexing (OFDM) receivers in wireless communication systems. OBJECTIVES: The proposed method utilizes an enhanced deep convolutional neural network (Enhanced DCNN) architecture with a time-frequency domain fusion mechanism to address the issues of interference and temporal information loss. The model incorporates attention mechanisms and causal convolutions to extract long-term dependencies within the received OFDM signals. It enables accurate channel estimation and signal recovery. METHODS: The methodology is validated using simulations based on 3GPP-defined channel models. It includes extended typical U (ETU), extended pedestrian A (EPA) and extended vehicular A (EVA) across varying signal-to-noise ratio (SNR) conditions. RESULTS: Results demonstrate that the proposed receiver significantly improves bit error rate (BER) performance compared to traditional Least Squares (LS) and LMMSE methods. Particularly, in scenarios with large delay spreads and high mobility. Additionally, the model has a lower computational complexity (CC) and thus is appropriate for real-time implementation. CONCLUSION: We view this work as a strong scheme to improve the performance of OFDM systems in future wireless networks.
subject: Research article
  topic: section
subject: Deep Learning
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: OFDM
  topic: keywords
subject: 6G Communications
  topic: keywords
subject: LS
  topic: keywords
subject: MMSE
  topic: keywords
creator: Kasetty Lakshminarasimha
creator: V. Saraswathi
creator: Mummidi SubbaRaju
creator: M. Koteswara Rao
creator: Kapula Kalyani Kalyani
creator: Anil Kumar R
rights: Copyright (c) 2025 Kasetty Lakshmi Narasimha, V. Saraswathi, Mummidi SubbaRaju, M. Koteswara Rao, Kapula Kalyani Kalyani, Anil Kumar R https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-06
publication date: 2025-06-06
doi: 10.4108/eetiot.8536

Facial Recognition Enabled Smart Security Lock System Using Machine Learning Approach
type: item
description: INTRODUCTION: In today's modern world of networking and intelligent devices, there is an imperative need to upgrade everyday things and make them intelligent; additionally, this is not the era to unquestioningly trust old and traditional security measures, particularly when it comes to smart door lock systems. Mostly, every smart door lock system has a security access code or fingerprint access outside the door that makes it vulnerable. The password in a classic security system can be readily hacked with advanced technology and, therefore, is no longer suitable for today's real-time environment. OBJECTIVES: As a result, this paper intends to provide enhanced security for the user through facial recognition using a machine-learning approach with high accuracy and remote access via an Android application. Automated solutions leveraging machine learning have shown to be quite effective in security. METHODS: Machine Learning (ML) algorithms are used to train different sets of images to identify and classify various sorts of faces. The specific algorithm that is to be used is Dlib and Support Vector Machine (SVM); Dlib is utilized for face recognition along with HOG (Histogram of Oriented Gradients), whereas SVM is used for image classification, which is used to authorize the personnel. RESULTS: When compared to other cutting-edge methodologies, empirical results reveal that the proposed approach achieves 96% accuracy rate, with a recognition speed of 0.5 seconds per face in facia recognition which is more effective, reliable, and utilizes fewer resources. CONCLUSION: Real-time face recognition enables quick and secure identification, supporting multi-factor authentication and monitoring. The proposed smart lock system uses the HOG+SVM approach, achieving higher accuracy and faster face detection.
subject: Advances in Internet of Things and its cybersecurity applications
  topic: section
subject: Face recognition
  topic: keywords
subject: Dlib
  topic: keywords
subject: SVM algorithm
  topic: keywords
subject: OpenCV
  topic: keywords
subject: Raspberry Pi
  topic: keywords
creator: M. Marimuthu
creator: G. Mohanraj
creator: J. Akilandeswari
creator: V. Sathiyapriya
rights: Copyright (c) 2025 M. Marimuthu , G. Mohanraj, J. Akilandeswari, V. Sathiyapriya https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-05
publication date: 2025-06-05
doi: 10.4108/eetiot.5657

Enhanced security based on Context Aware Proactive Trust Aware Routing Protocol using secure Preemptive Verifiable Key Handover Policy for VANET Environment
type: item
description: INTRODUCTION: Road safety improvements through Vehicular Ad-hoc Networks (VANETs) occur because these networks enable vehicles to exchange messages with Road Side Units (RSUs). The secure exchange of data in VANET evolves as a fundamental challenge in practical systems because nature-based implementation problems combine with protection threats from both data failure and sabotage-based message releases. OBJECTIVES: This research develops a new routing protocol to strengthen VANET communication security processes by filling existing vulnerabilities and building reliable data transmission methods. METHODS: CAPTARP represents a routing protocol which integrates PVKHP as its Secure Preemptive Verifiable Key Handover Policy. Data from RSU communications first needs collection before the lookup environment can be established. The Payload Data Impact Rate (PDIR) detects both transmission defects and congestion through analysis. The Transmission Behavioral Delay Tolerance Rate detects the patterns of deliberate transmission anomalies. SRSU-FIS operates as a security enhancement mechanism for routing decisions while maintaining context-based security protection capabilities. RESULTS: Test simulations show that the proposed system delivers better results than current approaches by achieving 94.19% throughput alongside 96.03% packet delivery ratio while reaching 95.03% security performance and 95.24% authentication level and maintaining 18.06 ms end-to-end delay. CONCLUSION: The combination of CAPTARP protocol with PVKHP protocol effectively strengthens VANET security by providing better authentication coupled with trust validation and rapid communication procedures. The security performance surpasses traditional methods because this innovative approach represents a strong solution for protecting vehicular communication networks.
subject: Blockchain-Based Digital Trust in IoT for Edge Computing
  topic: section
subject: Authentication
  topic: keywords
subject: routing protocol
  topic: keywords
subject: delay
  topic: keywords
subject: RSU
  topic: keywords
subject: Secure communication
  topic: keywords
subject: VANET
  topic: keywords
creator: K N Anupama
creator: R Nagaraj
rights: Copyright (c) 2025 K N Anupama, R Nagaraj https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-28
publication date: 2025-05-28
doi: 10.4108/eetiot.7648

Advanced Attention-Enhanced BiLSTM-GRU Model for Real vs. Fake News Detection
type: item
description: INTRODUCTION: Social media has become one of the primary platforms for the rapid dissemination of both real and fake information. In today's digital society, individuals are often easily influenced by misleading content, making fake news a powerful tool for manipulation. A common strategy employed on social media is the intentional spread of false information with the aim of deceiving and misleading the public. OBJECTIVES: In some cases, this misinformation is so convincing that it significantly influences public opinion and behaviour, with long-term consequences. According to research, only 54% of people are capable of detecting deception without external assistance. To address this growing concern, the proposed model leverages Deep Learning techniques to identify fake news content in both Tamil and English languages. METHODS: The dataset for this study was sourced from an open-access repository on GitHub. After thorough pre-processing, the dataset was divided into training (80%) and testing (20%) subsets. Additionally, the proposed modified attention-based revamping weighted BiLSTM-GRU model is introduced and evaluated. RESULTS: The effectiveness of each model is measured using various performance metrics, including accuracy, precision, recall, F1-score, Bilingual Evaluation Understudy, Area under the ROC Curve, and the Receiver Operating Characteristic curve. Comparative analysis shows that the proposed model outperforms the existing methods across all evaluation parameters. CONCLUSION: A key advantage of the proposed approach is its capability to accurately detect fake text in both Tamil and English. Furthermore, the model demonstrates strong performance when tested on datasets generated using AI tools like ChatGPT, effectively identifying real and fake content with high precision.
subject: Research article
  topic: section
subject: Fake text
  topic: keywords
subject: DL
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: BiLSTM
  topic: keywords
subject: Bi-directional Long Short Term Memory
  topic: keywords
subject: BiLSTM-AM
  topic: keywords
subject: BiLSTM with Attention Mechanism
  topic: keywords
subject: GRU
  topic: keywords
subject: Gated Recurrent Unit
  topic: keywords
subject: GRU-AM
  topic: keywords
subject: BiLSTM-GRU
  topic: keywords
creator: M. Arunkrishna
creator: B. Senthilkumaran
rights: Copyright (c) 2025 M. Arunkrishna, B. Senthilkumaran https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-20
publication date: 2025-05-20
doi: 10.4108/eetiot.7829

Synergistic Integration of Quantum and Classical Machine Learning Models for High-Fidelity Asteroid Hazard Detection
type: item
description: This research investigates the use of quantum machine learning (QML) to classify asteroids into non-hazardous and hazardous groups, which yields successful results in detecting the hazard. In addition to the complexity involved in analyzing orbits and physical objects, QML performs better than traditional machine learning in modeling the relationship between data. This method involves data-intensive preprocessing steps, such as feature selection by removing unnecessary features and correlation analysis to find predictors. Quantum circuits are used for specification and classification, and the standard evaluation is based on accuracy, recall, F1 score, and precision. A strong and weak method is provided by cross-validation and hyperparameter tuning. The best classical model here is the decision tree, which is a good model for high-resolution and low-budget social benefit with 0.883 accuracy, 0.955 recovery rate, 0.981 F1 score, and 0.883 sensitivity. However, the quantum model has made great leaps. AMSGRAD QCNN (Adaptive Moment Estimation with Gradient Thresholding Quantum Convolutional Neural Networks) achieves a non-uniform accuracy of 0.997, which is 13% higher than the decision tree with 0.984 accuracy, 0.955 recovery rate, and 0.981 F1 score. The accuracy of SPSA QCNN(Simultaneous Perturbation Stochastic Approximation) is 0.993, the recall is 0.974, and the F1 score is 0.977. This improvement shows the excellent ability of the quantum model to better resolve correlation data and, more importantly, to reduce false negatives in detecting stars. These results demonstrate the ability of quantum computing to analyze complex data and provide the best results. Our future work will focus on identifying and repairing quantum circuits, as well as exploring hybrid quantum classical models to improve model accuracy and interpretation. The findings open the door to new ways to predict the simplest solutions and the most powerful and accurate way to date of estimating the dan…
subject: Research article
  topic: section
subject: Quantum machine learning
  topic: keywords
subject: Quantum computing
  topic: keywords
subject: Potentially Hazardous Asteroids (PHAs)
  topic: keywords
subject: Quantum Convolutional Neural Networks (QCNN)
  topic: keywords
subject: Space Hazard Assessment
  topic: keywords
subject: quantum algorithm optimization
  topic: keywords
subject: Recursive Feature Elimination(RFE)
  topic: keywords
creator: Kuldeep Vayadande
creator: Yogesh Bodhe
creator: Amol A. Bhosle
creator: Kavita Sultanpure
creator: Geetanjali Yadav
creator: Ajit R. Patil
creator: Jyoti Jayesh Chavhan
creator: Amolkumar N. Jadhav
creator: Preeti Bailke
rights: Copyright (c) 2025 Kuldeep Vayadande, Yogesh Bodhe, Amol A. Bhosle, Kavita Sultanpure, Geetanjali Yadav, Ajit R. Patil, Jyoti Jayesh Chavhan, Amolkumar N. Jadhav, Preeti Bailke https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-30
publication date: 2025-04-30
doi: 10.4108/eetiot.8170

Variation of Production Line Energy Consumption: Stochastic Process Models for Single and Multiple Machine Systems
type: item
description: The energy consumption of production lines can vary across different machines due to diverse factors such as varying processing rates, machine conditions, product characteristics, and the compatibility between products and machines. Additionally, identical products on the same type of machine may experience temporal variations in energy consumption due to real-time equipment status and operational activities. Accurately understanding these influencing factors can aid in developing more efficient energy management strategies. This study first analyzes the energy consumption of single machines processing single products, establishing a probabilistic model for the stochastic process of single-machine energy consumption over time. Mathematical tools, including convolution, are then employed to develop a stochastic process model for energy consumption across multiple machines and products within the entire plant. The overall stochastic process model is optimized, leading to the creation of a "Peak Energy Consumption Alert" and an "Energy Efficiency Alert" system to mitigate the risks of peak energy consumption and overall energy inefficiency. By utilizing the proposed optimization model for machine component energy consumption and the production scheduling module, the likelihood of exceeding contracted power capacity is significantly reduced. This approach also enhances the energy efficiency of production scheduling, thereby reducing the total energy consumption of the entire plant. Furthermore, while meeting energy planning goals, the model considers non-energy-related production indicators (e.g., completion time, order delivery dates), ultimately improving the operational efficiency of production lines.
subject: Research article
  topic: section
subject: Energy consumption
  topic: keywords
subject: non-energy-related
  topic: keywords
subject: production indicators
  topic: keywords
creator: Po-Jung Lai
creator: Hao-Chu Lin
creator: Hui Hsin Chin
rights: Copyright (c) 2025 Po-Jung Lai, Hao-Chu Lin, Hui Hsin Chin https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-15
publication date: 2025-04-15
doi: 10.4108/eetiot.8011

A Secure and Efficient Blockchain-Based Framework for Smart Cities Using Physics-Informed Neural Networks
type: item
description: The massive scale and extensive implementation of the Internet of Things (IoT) makes it difficult to provide secure and private communications over it. Privacy and decentralisation have been made easier using blockchain technology. Unfortunately, these solutions aren't practical for the majority of IoT uses because of how much time and computing power they require. Secure and private IoT that makes efficient use of available resources is proposed in this study. With the use of Physics Informed Neural Networks, the technique takes advantage of the computing power available in IoT settings like smart cities. This solution examines the reliability of the blockchain-based Smart Cities Architecture with respect to accessibility, privacy, and integrity. When weighed against the security and privacy advantages our system offers, our simulation findings reveal that the overheads (distribution, processing time, and energy usage) are negligible.
subject: Blockchain-Based Digital Trust in IoT for Edge Computing
  topic: section
subject: Physics Informed Neural Networks
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: SC
  topic: keywords
subject: Block Chain
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Kernal Principal Component Analysis
  topic: keywords
subject: KPCA
  topic: keywords
subject: Feature Selection
  topic: keywords
subject: Normalization
  topic: keywords
subject: Privacy-Preserving and Secure Framework
  topic: keywords
subject: PPSF
  topic: keywords
creator: Mohd. Asif Gandhi
creator: Atul D Narkhede
creator: N. Noor Alleema
creator: M.P. Indumathi
creator: Deepak Sundrani
creator: R. Sasikala
rights: Copyright (c) 2025 Mohd. Asif Gandhi, Atul D Narkhede, N. Noor Alleema, M.P. Indumathi, Deepak Sundrani, R. Sasikala https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-14
publication date: 2025-04-14
doi: 10.4108/eetiot.7740

Interlink Platform for School, Higher and Technical Education in India: Design Platform
type: item
description: This research paper aims to fill the knowledge gap in understanding the factors that contribute to academic performance and dropout rates in the Indian education system. The study proposes a “Interlinked platform for school, higher and technical education in India”, a unified digital space for students, educators, administrators and government agencies. The platform is designed to track students' academic performance across different educational levels and visualize dropout rates. The research uses a comprehensive methodology that integrates data analysis techniques and visualization frameworks and uses Python libraries such as NumPy, Pandas, Matplotlib and Scikit-Learn. Student academic outcomes are analyzed using linear regression and K-means clustering, and dropout rates are predicted using logistic regression. The aim of the research is to provide institutions with valuable insights to understand the factors that contribute to dropout rates and to develop targeted interventions to address potential triggers of dropout.
subject: IoT, Machine Learning and Data Analytics for Smart Environment
  topic: section
subject: Performance Analysis
  topic: keywords
subject: Interlinked Platform
  topic: keywords
subject: Dropout Prediction
  topic: keywords
subject: Educational
  topic: keywords
creator: Nitesh Ghodichor
creator: Pratham Chopde
creator: Mansi Choudhari
creator: Saloni Rangari
creator: Pratham Badge
rights: Copyright (c) 2025 Nitesh Ghodichor, Pratham Chopde, Mansi Choudhari, Saloni Rangari, Pratham Badge https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-13
publication date: 2025-04-13
doi: 10.4108/eetiot.6152

All-in-Net: Scorekeeping in Basketball Training using a Mobile Phone Camera and Image
type: item
description: As basketball is one of the crowd's favorite invasion games, methods are being developed to enhance players' performance as technology improves. Recording large parts of training significantly reduces subjective training assessment compared to objective aspects. In this paper, basketball players and coaches use a mobile application based on image processing for achievements, measuring shooting the basket from different positions on the court and displaying feedback. The shooting technique and the percentage of success are measured using an algorithm that identifies the angle of the shot to the basket. The system monitors players' knowledge of results during training using one mobile phone camera. The paper describes the architecture and design of the mobile computing and application: Pre-training (define goals based on past performance and level of training difficulty), during training (data collection and compatible camera), and post-training (analysis and visualization of results). Finally, the paper discusses validation and implications.
subject: Research article
  topic: section
subject: Mobile Computing Systems and Applications
  topic: keywords
subject: Smart training systems
  topic: keywords
subject: Image recognition
  topic: keywords
creator: Yonit Rusho
creator: Marcelo Sihman
creator: Yeshayahu Huzler
rights: Copyright (c) 2025 Yonit Rusho, Marcelo Sihman, Yeshayahu Huzler https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-09
publication date: 2025-04-09
doi: 10.4108/eetiot.9061

Spellbound Kannada: Harnessing Conditional Generative Adversarial Networks for Transformative Word Suggestion Systems in Kannada Language Processing
type: item
description: INTRODUCTION: The advancement of a word suggestion system model is driven by the need to enhance user interaction and efficiency in digital communication. Hence, the word suggestion system helps minimize typographical errors and spelling mistakes. Therefore, various traditional methods are used to suggest words to sentences; however, these traditional models are extremely time consuming, prone to errors and tedious. METHODS: Owing to these factors, the present paper focuses on developing a Kannada word suggestion system using cGAN (Conditional Generative Adversarial Networks), as this system is designed to significantly enhance user interaction by offering predictive text suggestions in the Kannada language. RESULTS: The training dataset, which resides on AWS S3, comprises a comprehensive collection of Kannada texts utilized for both training and validation purposes. Furthermore, the implementation of the model leverages the TensorFlow and keras framework, specifically employing long short-term memory (LSTM) networks for effective sequence prediction and generation. LSTMs are particularly advantageous in NLP processing because they can capture long-term dependencies within sequential data. To facilitate user interaction, a web-based interface has been developed using Flask, enabling users to input initial characters and receive dynamically generated Kannada word suggestions. CONCLUSION: This paper not only delves into the application of cGANs within the realm of NLP but also illustrates practical deployment strategies utilizing cloud services and modern web technologies. Overall, the proposed approaches demonstrate the potential of the cGAN in enhancing the user experience through intelligent text prediction systems tailored for the Kannada language.
subject: Research article
  topic: section
subject: Natural Language Processing
  topic: keywords
subject: Word Suggestion System
  topic: keywords
subject: Conditional Generative Adversarial Network
  topic: keywords
subject: Kannada
  topic: keywords
subject: LSTM
  topic: keywords
creator: Prathibha R J
creator: Anupama N
creator: Sudarshan A S
rights: Copyright (c) 2025 Prathibha R J, Anupama N, Sudarshan A S https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-01
publication date: 2025-04-01
doi: 10.4108/eetiot.7792

Innovative Human Interaction System to Predict College Student Emotions Using the Extended MASK-R-CNN Algorithm
type: item
description: There is a rising demand for emerging machines that can be self-decisive and intelligent. Machines can capture the emotions and gestures of college students to mechanise tasks and handle interactions better. Facial expressions based on emotion recognition are practices that play a substantial role in the modern fields of artificial intelligence and computer vision. Numerous manual methods for detecting emotions are focused on few basic emotions. Additionally, significant time is needed for appropriate detection. Nonetheless, these techniques are time-consuming and inefficient for obtaining better results. Therefore, an effective object detection model is needed to address such issues. To overcome these challenges, several studies have focused on object detection systems to provide effective emotion prediction. Conversely, it results in a lack of speed, precision and computational complexity. To improve object detection performance, the proposed model employs deep learning (DL)-based adaptive feature spatial anchor refinement with a mask region-based convolutional neural network (Mask RCNN). It uses the Facial Expression Recognition (FER) 2013 dataset for the evaluation process. Correspondingly, the efficacy of the projected model is calculated via various evaluation metrics, such as the recall, precision and mean average precision (mAP), to estimate the performance of the proposed DL method. It achieves 0.75298 for MAP@50, 0.70252 for precision and 0.66606 for recall. Furthermore, a comparison of existing models reveals the efficiency of the proposed DL method. The present research is intended to contribute to emerging object detection methods for enhancing real-time analysis of student emotions in various environments, such as classrooms and online education.
subject: Research article
  topic: section
subject: Object detection
  topic: keywords
subject: emotions prediction
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Mask RCNN
  topic: keywords
subject: Facial expression recognition
  topic: keywords
creator: Dinesh P
creator: Thailambal G
rights: Copyright (c) 2025 Dinesh P, Thailambal G https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-03-10
publication date: 2025-03-10
doi: 10.4108/eetiot.7874

A New Approach for Solving Fractional Differential Equations Incorporating Ramadan Group Transform and Machine Learning
type: item
description: This paper examines various types of fractional differential equations using fractional calculus methods. It extends the classical Frobenius method and introduces key theorems that apply the Ramadan Group transform and other techniques. Additionally, the research incorporates machine learning, specifically neural networks, to solve these equations. The paper demonstrates that machine learning can enhance the solution process through data generation, model design, and optimization. Examples provided illustrate how combining traditional methods with machine learning can effectively solve fractional differential equations.
subject: Research article
  topic: section
subject: fractional-order differential equation
  topic: keywords
subject: Mittag-Leffler function
  topic: keywords
subject: gamma function
  topic: keywords
subject: Ramadan Group transform of the fractional derivative
  topic: keywords
creator: Prabakaran Raghavendran
creator: Tharmalingam Gunasekar
creator: Saikat Gochhait
rights: Copyright (c) 2025 Prabakaran Raghavendran, Tharmalingam Gunasekar, Saikat Gochhait https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-03-04
publication date: 2025-03-04
doi: 10.4108/eetiot.7134

Assessment of CatBoost for Diabetes Prevention in Comparison to XGBoost: AI model capable of predicting the onset of diabetes
type: item
description: The metabolic disease known as diabetes is defined by consistently elevated blood sugar levels. An increase in hunger, thirst, and urine production are symptoms of high blood sugar. Untreated diabetes may lead to a variety of complications. Acute complications of diabetes include hyperosmolarity, hyperglycemia, diabetic ketoacidosis, and perhaps death. The most devastating long-term effects are cardiovascular disease, cerebrovascular accident, chronic kidney disease, foot ulcers, and vision loss. The World Diabetes Organization estimates that 463 million people were diagnosed with diabetes in 2019. This population will increase by 578 million by 2030 and by 700 million by 2045, if forecasts pan out. The ability to quickly and accurately diagnose sickness is one of its current medical uses. Therefore, we might potentially reduce death rates via the use of machine learning by creating an AI model that can anticipate when diabetes will start. We will compare the CatBoost and XGBoost algorithms to find the one that is most suited for this purpose. Finally, using a number of health markers from the dataset, the study contrasted XGBoost and CatBoost, two models that may predict diabetes. We train and build our recommended system using Python on a real-world dataset taken from Kaggle. We evaluate our algorithms using precision, recall, F1score, and accuracy metrics, among other performance evaluation parameters. While XGBoost achieved an F1 Score of 91.75, an accuracy rate of 93.33%, a precision of 90.38%, and a recall of 90.63%. The accuracy, precision, recall, and F1 score for CatBoost are 96.09%, 93.38%, 91.38% and 92.13%, respectively. It's the most effective ensemble method, according to CatBoost.
subject: Research article
  topic: section
creator: Jagadeesh Selvaraj
creator: G. Gifta Jerith
creator: Karthikeyan R
creator: Senthil K
rights: Copyright (c) 2025 Jagadeesh Selvaraj, G. Gifta Jerith, Karthikeyan R, Senthil K https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-02-10
publication date: 2025-02-10
doi: 10.4108/eetiot.5880

Navigating the Future of Intelligent Transportation: Challenges and Solutions in 6G V2X and V2V Networks
type: item
description: Recent advancements in Information and Communication Technologies (ICT) have transformed vehicular communication, facilitating Vehicle-to-Vehicle (V2V) and Vehicle-to-Everything (V2X) connectivity. These technologies enhance travel safety and efficiency while also reducing pollution and accident rates through optimised resource utilisation. V2X applications encounter significant challenges concerning traffic safety, data security, and scalability, necessitating thorough evaluation prior to extensive implementation. The rising prevalence of connected vehicles presents challenges, including network instability, heterogeneity, and extensive data management, which require novel solutions for effective communication. This study gives a new view on how to combine 6G technologies with V2X and V2V communication systems. It addresses important issues like security, resource allocation, and making sure that communication is very reliable and has low latency. Our study is different from others because it looks at all the technologies that make this possible, including Proportional Fairness Algorithms, machine learning, blockchain, and Reconfigurable Intelligent Surfaces (RIS). We emphasise the significance of collective perception (CP) in improving traffic safety and resource efficiency through the sharing of sensor data among V2X-enabled entities, including vehicles, roadside units (RSUs), and vulnerable road users.This research provides a thorough analysis of advanced methodologies and their limitations, making it a valuable resource for scholars, academics, and industry professionals. It offers a framework for developing future intelligent transportation systems that utilise 6G capabilities to achieve high reliability, minimal latency, and optimal spectrum efficiency in vehicular networks.
subject: Review article
  topic: section
subject: Vehicular Ad hoc Networks
  topic: keywords
subject: Vehicle to Vehicle
  topic: keywords
subject: Vehicle to Everything
  topic: keywords
subject: Collective Perception
  topic: keywords
subject: Information and Communication Technologies
  topic: keywords
subject: Road side Units
  topic: keywords
subject: Data Transmission
  topic: keywords
creator: Spandana Mande
creator: Shaik Salma Asiya Begum
creator: Nandhakumar Ramachandran
rights: Copyright (c) 2025 Spandana Mande, Nandhakumar Ramachandran, Shaik Salma Asiya Begum https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-02-06
publication date: 2025-02-06
doi: 10.4108/eetiot.5114

Wireless 5G Network in Edge Computing Based On MIMO with Federated Learning and Clustering Integrated Reinforcement Learning
type: item
description: Edge Computing (EC) is a revolutionary architecture that brings Cloud Computing (CC) services closer to data sources than ever before. This research proposed novel technique in edge computing network based on wireless 5G technology using MIMO_federated learning integrated with Reinforcement neural network. Here the aim is to enhance the resource allocation by Decentralized Federated learning in multiple user based MIMO (De_Fed_L- MIMO) networks. Then the energy efficiency and channel optimization of the network is carried out using K-means clustering integrated with Reinforcement learning (K-means_RL). Here the experimental analysis is carried out in terms of number of users of network as well as number of edge server by DoF of 92%, Spectral efficiency of 92%, Energy efficiency of 96%, Signal to noise ratio (SNR) of 85%, Coverage area of 92%, RL training accuracy of 95%, FL training accuracy of 98%.
subject: Research article
  topic: section
subject: Edge Computing
  topic: keywords
subject: MIMO
  topic: keywords
subject: 5G cellular networks
  topic: keywords
subject: federated learning
  topic: keywords
subject: resource allocation
  topic: keywords
subject: energy efficiency and channel optimization
  topic: keywords
creator: Manikandan Parasuraman
creator: Sivaram Rajeyyagari
creator: Ramesh Sekaran
creator: Suthendran Kannan
creator: Vinayakumar Ravi
rights: Copyright (c) 2025 Manikandan Parasuraman, Sivaram Rajeyyagari, Ramesh Sekaran, Suthendran Kannan, Vinayakumar Ravi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-02-03
publication date: 2025-02-03
doi: 10.4108/eetiot.5910

Advancing IoT Security with an Innovative Machine Learning Paradigm for Botnet Attack Detection
type: item
description: INTRODUCTION: In contemporary society, everyday operations are greatly improved by the Internet of Things (IoT), which connects physical devices to provide digital services. IoT technology offers unified services and streamlines activities across various domains, ranging from remote monitoring to sophisticated welfare systems. However, the growing number of IoT devices presents a security concern. Many of these devices are susceptible to exploitation, leading to diverse vulnerabilities. OBJECTIVES: Resource-constrained IoT devices become prime targets for botnet attacks, manifesting in various forms and penetration methods. Despite numerous research efforts introducing multiple approaches for detecting botnet attacks in IoT, existing methods often fail to achieve satisfactory detection rates. METHODS: Additionally, these approaches struggle to comprehensively analyze the diverse communication networks within the expansive realm of IoT devices. This study proposes an innovative machine-learning framework for detecting IoT botnet threats to address these limitations. RESULTS: This conceptual framework exhibits a remarkable capability to identify a spectrum of botnet attacks, showcasing a detection accuracy of 99.5 per cent, significantly surpassing the performance of other prevalent machine-learning approaches. CONCLUSION: Through this research, we aim to enhance the security paradigm of IoT networks, ensuring robust protection against evolving botnet threats in the dynamic landscape of interconnected devices.
subject: Advances in Internet of Things and its cybersecurity applications
  topic: section
subject: Machine Learning
  topic: keywords
subject: Feature Extraction
  topic: keywords
subject: Botnets
  topic: keywords
subject: Internet of Things
  topic: keywords
creator: Punitha P
creator: Dinesh Kumar V. K
creator: Lakshmana Kumar R
rights: Copyright (c) 2025 Lakshmana Kumar R, Dinesh Kumar V. K, Punitha P https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-02-03
publication date: 2025-02-03
doi: 10.4108/eetiot.4521

Revitalizing Image Retrieval: AI Enhancement and Metaheuristic Algorithm Adaptation
type: item
description: INTRODUCTION: In recent years, development of digital technology has led to number of images, which can be stored in digital format. However, searching and retrieval of images in large image DB (Database) is a mammoth task. Therefore, different image retrieval techniques have been used for retrieving the suitable images, which includes retrieval of images using keywords or annotations, however, these methods are considered to be time consuming and leads to imprecise outcome. OBJECTIVES: Therefore Effective and precise retrieval of suitable images from huge DB can thrived by utilizing CBIR (Content Based Image Retrieval) system. However, incorporation of CBIR in most existing studies resulted in low accuracy for IR. So, proposed model incorporates Modified ResNet50 (M-ResNet50) and VGG 16 model for feature extraction in order to extract the best features as M-ResNet50 utilizes extra dense layers which aids in better feature extraction process. METHODS: After feature extraction, the features are fused using PCA and fed to Modified PSO (M-PSO) model for obtaining optimized features since M-PSO is fast and aids in selecting optimal features after processing from insignificant features that primarily set the preferred number of necessary features. RESULTS: Moreover, M-PSO require less parameters to tune instead of a huge number of parameters by incorporating K parameters of KNN algorithm in order to find the nearest images to Query Images (QI), thereby making the model appropriate for IR process with better similarity score. CONCLUSION: The proposed model utilizes 8 different sun images at different intervals for IR process. Finally, the proposed model is evaluated by using several metrics such as accuracy, precision, recall and F1 score, besides the proposed model is compared with various existing models in order to evaluate the efficiency of the proposed model.
subject: Research article
  topic: section
subject: Content Based Image Retrieval
  topic: keywords
subject: Image Retrieval
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subject: Resnet50
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subject: Particle Swarm Optimization
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subject: VGG-16
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subject: Feature Extraction
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subject: Feature Fusion
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creator: Kumaravel Pichaimani
creator: S. Thabasu Kannan
rights: Copyright (c) 2025 Kumaravel Pichaimani, S. Thabasu Kannan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-01-17
publication date: 2025-01-17
doi: 10.4108/eetiot.5293