EAI Endorsed Transactions on Internet of Things
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
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System of digitizing and paying of civil status documents via an RFID card
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.
Copyright (c) 2025 Mohamed E.A. Ouesse, Diatta Baboucar, Basse Adrien, Bouallegue Ridha https://creativecommons.org/licenses/by-nc-sa/4.0
Improved PIC16f877 microcontroller timer register to delimit the execution time of an operation
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.
Copyright (c) 2025 Souleymane Mbathie, Mohamed Ouesse, Baboucar Diatta, Adrien Basse, Ridha Bouallegue https://creativecommons.org/licenses/by-nc-sa/4.0
Trust-Aware Federated Learning with Differential Privacy for Secure AIoT in Critical Infrastructures
Federated learning offers a scalable solution for distributed intelligence in Artificial Intelligence of Things (AIoT) systems, yet privacy leakage, adversarial attacks, and system heterogeneity remain persistent challenges in critical infrastructures such as smart cities, agriculture, and forestry. This paper proposes PriSec- FedGuardNet, a trust-aware federated learning framework that integrates differential privacy, homomorphic secure aggregation, and graph neural network–based trust evaluation to safeguard both data and model updates. The framework preserves sensitive information by perturbing gradients with calibrated noise, encrypts local updates for aggregation without decryption, and assigns trust scores to filter unreliable participants. Experimental validation on ToN-IoT, Bot-IoT, and real-world sensor datasets demonstrates that PriSec-FedGuardNet maintains above 97.3% relative utility under strict privacy budgets, improves anomaly detection F1-scores by up to 18% under poisoning attacks, and reduces device-level energy overheads to less than 12%. Domain-specific evaluations across Indian smart city, agricultural, and forestry deployments further highlight the adaptability and efficiency of the framework. By balancing privacy, security, and utility, PriSec-FedGuardNet establishes a robust paradigm for secure federated learning in AIoT-driven critical infrastructures.
Copyright (c) 2025 Ramana Kadiyala, C. V. Lakshmi Narayana, S. China Ramu, Narsaiah Putta, Shyam Sunder Pabboju, B. Ramana Reddy https://creativecommons.org/licenses/by-nc-sa/4.0
Research and Design a Low Power Electronic Shelf Label Based on E-paper Display and LoRa Technology
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%.
Copyright (c) 2025 Le Huy Trinh, Thanh-Tuan Phan, Fabien Ferrero, Nguyen Vu Truong https://creativecommons.org/licenses/by-nc-sa/4.0
A Comprehensive Survey on Objective Functions in RPL Routing with Various Networking and Application Scenarios
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.
Copyright (c) 2025 Ibrahim Ali Alnajjar https://creativecommons.org/licenses/by-nc-sa/4.0
A Trust Based Energy Efficient Routing Design Based on Hybrid Particle Swarm Optimization (HPSO) for Wireless Sensor Networks
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.
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
A Scalable Hybrid RF-BiLSTM Framework for Reliable IoT Traffic Threat Detection via Feature Selection and Temporal Pattern Recognition
In this research, we addressed the recurring challenges of securing IoT networks against emerging cyber security threats. Taking advantage of the complementary strengths of Random Forest (RF) for feature selection and Bidirectional Long Short-Term Memory (BiLSTM) networks for sequential learning; we developed a novel Hybrid RF-BiLSTM model that combines feature level insights with temporal pattern recognition to provide a reliable solution for IoT traffic threats. We conducted extensive experiments with Aposemat IoT-23 dataset, where we used equal volumes of benign and malicious traffic samples leading to balanced evaluation. Furthermore, the Hybrid RF-BiLSTM model achieved a performance of 99.87%, while the Random Forest and BiLSTM performance were 99.37% and 93.32%, respectively, demonstrating the power of the hybrid approach over individual ones. The analysis gave more details about the model's performance, showing the confusion matrix and calculating the performance metrics that substantiates the model's reliability to minimize false positive and false negatives while also achieving high precision and recall. It shows that how well the integration of feature selection and sequential learning works for IoT cyber security. This Hybrid RF-BiLSTM approach lays a scalable and practical framework for real-world IoT security problems and a stepping stone for future studies in hybrid ML models for anomaly detection and threat analysis.
Copyright (c) 2025 Nadia Ansar, Suraiya Parveen, Ihtiram Raza Khan, Bhavya Alankar https://creativecommons.org/licenses/by-nc-sa/4.0
A Quantitative Framework for the Selection of Hybrid Consensus Mechanisms in Blockchain-IoT Systems
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.
Practical Byzantine Fault Tolerance (PBFT)
Copyright (c) 2025 N. A. Natraj, J. J. Midhunchakkaravarthy, Brojo Kishore Mishra https://creativecommons.org/licenses/by-nc-sa/4.0
Enhanced Beamspace Channel Recovery in mmWave MIMO Using Deep Neural Networks
Millimeter-wave (mmWave) massive MIMO systems use many antennas. These systems offer high data rates. But using many radio frequency (RF) chains increases cost and power use. To solve this, lens antenna arrays are used. Energy is focused, allowing the use of fewer RF chains. However, this creates a new challenge. With fewer RF chains, it is hard to estimate the wireless channel. Accurate channel estimation is needed for good system performance. In beamspace, the channel is sparse. This shows that only a few values are large. The rest are close to zero. Because of this, the problem is seen as sparse signal recovery. AMP (Approximate Message Passing) is one popular algorithm used for this. A better version named LAMP (Learned AMP) uses deep learning. But it still does not give the best results. This paper proposes a new method GM-LAMP. It improves the channel estimation accuracy. It uses prior knowledge about the channel. It assumes that the beamspace channel follows a Gaussian mixture distribution. First, a new shrinkage function is created based on this distribution. Then, the original function in the LAMP network is replaced with the new one. As a result, a better deep learning model is developed. The final GM-LAMP network estimates the beamspace channel more precisely. It works well with both theoretical models and real-world data. Simulations show that GM-LAMP performs better than earlier methods. This approach combines math knowledge and deep learning. It shows that using prior information helps deep networks make smarter predictions. The proposed method offers better accuracy and is useful for future mmWave systems.
Copyright (c) 2025 V. Saraswathi, Vatsala Anand, Yaddanapudi Venkata Bhaskara Lakshmi, Samana Vinaya Kumar, Vijaya Babu Burra, U.S.B.K. Mahalaxmi, Suneetha Jalli, V. Vijayasri Bolisetty, Sarala Patchala https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing 5G Traffic Management with Programmable Intelligence and Open RAN Integration
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.
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
Glaucoma Retinal Image Detection through the Segmentation of OD Using Modified Deep Learning Method
Classifiers are the important processing module in any type of classification systems. This paper uses the proposed Modified LeNET (MLNET) classification architecture along with the standard LeNET to classify the retinal pictures into healthy cases and cases of glaucoma. This research work develops an automated computer aided system which has the following modules as preprocessing, Optic Disk (OD) segmentation, Feature computations and MLNET classification. The Glaucoma classification system has been functioned in two processing phases as training and testing. The training processing phase trains both healthy and Glaucoma retinal images from the known dataset using preprocessing, OD segmentation and feature computations from the segmented OD region. These features from the OD region have been further trained by the proposed MLNET classifier. The testing processing phase tests the unknown retinal image into either Glaucoma or healthy class through the sub processing modules of preprocessing, OD region segmentation and feature computations. The features from the OD region in the unknown test retinal image have been fed into the proposed MLNET classifier with respect to the previous training results.
Copyright (c) 2025 J. Ruby Elizabeth, D. Kesavaraja, S. Ebenezer Juliet, S. Jagadeesh, S. Samsudeen Shaffi, R. Umanesan https://creativecommons.org/licenses/by-nc-sa/4.0
Migration of Microservices Execution Contexts Between Processing Zones in Software-Defined Vehicular Fog Networks
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.
Mobility-Responsive Microservice Orchestration
Copyright (c) 2025 Leonel D.C. Alvarenga, Pedro Sousa, António Costa https://creativecommons.org/licenses/by-nc-sa/4.0
Exploration of the application of wireless communication Internet in the child-friendly public space
In the design and establishment of child-oriented public spaces, the pervasive use of wireless communication technologies via the internet is a cornerstone in fostering a safe and educational milieu. The convergence of high-speed Wi-Fi, mobile apps, IoT devices, and precise location tracking builds a holistic platform for children's enjoyment and development. Real-time wireless monitoring gives parents unparalleled assurance of their children's safety. Mobile apps integrated with public amenities offer interactive games and educational content, fostering an enriching learning environment. IoT devices, such as intelligent cameras and emergency response buttons, further bolster security measures, providing additional protection for children. Wireless tech in public spaces gathers behavioral data, deepening understanding of kids' needs. This data optimizes spaces, enhancing their experience. Wireless tech in child-friendly spaces boosts security, engagement, and blends education with entertainment. As tech evolves, future spaces will prioritize kids' growth, transforming into havens for holistic development.
Copyright (c) 2025 Gaoyang Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Heterogeneous Multi-Model Ensemble for PPE Detection in Construction Environments
The construction industry remains one of the most hazardous work environments, with a fatality rate of 25.6 per 100,000 workers, significantly exceeding the average across all industries. PPE compliance is crucial for worker safety, yet monitoring adherence remains challenging in dynamic construction environments. This paper presents an automated PPE detection system utilizing an ensemble deep learning models to enhance workplace safety monitoring. Our approach combines three advanced architectures with Yolov11, RTDETRv2, and HyperYolo. The individual model predictions are integrated using WBF to improve detection robustness. We evaluate our system on a comprehensive dataset of 4,135 professionally annotated images encompassing critical PPE categories including hard hats, safety vests, protective gloves, and safety boots, along with their corresponding absence classes. The proposed ensemble achieves superior performance with a precision of 0.765, recall of 0.735, mAP@50 of 0.760, and mAP@50:95 of 0.440, outperforming individual models across all evaluation metrics. The results demonstrate the effectiveness of multi-model fusion for automated PPE detection. This research contributes to the advancement of intelligent safety systems that can significantly reduce workplace injuries and fatalities through automated PPE compliance verification.
Copyright (c) 2025 Thi-Nguyen Nguyen, Quang-Anh Nguyen-Duc, Dinh-Thai Kim, Duy Tung Doan, Minh-Duc Pham, Duc Cuong Van, Minh-Anh Nguyen https://creativecommons.org/licenses/by-nc-sa/4.0
Conformer network-guided speech recognition for smart home Internet of Things system
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 of the proposed intelligent fusion-based Internet of Things system in terms of speech recognition accuracy, computing power, and the intelligence level of voice interaction has been verified.
Copyright (c) 2025 Shengjun Huang, Dong-hyun Kim https://creativecommons.org/licenses/by-nc-sa/4.0
Edge Computing for Computer Vision in IoT: Feasibility and Directions
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.
Copyright (c) 2025 Panagiotis Savvidis, George A. Papakostas https://creativecommons.org/licenses/by-nc-sa/4.0
Extending JASPER for Jammer-Angle Allocation Evaluation: Testing Algorithm Robustness Under Jammer Failures and Varying Resource Conditions
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.
Copyright (c) 2025 Ahmet Burak Barakli, Okan Topçu, Halit Oğuztüzün https://creativecommons.org/licenses/by-nc-sa/4.0
SRNCDSA: A Novel Enhancement of ECDSA Using a Single Random Number and Counter for Improved Security
INTRODUCTION: The Elliptic Curve Digital Signature Algorithm (ECDSA) is widely used to secure communications in resource-constrained systems, including IoT devices, UAVs, and blockchain platforms. Despite its efficiency, ECDSA relies heavily on the generation of secure random keys, which makes it vulnerable to key leakage if random values are reused or derived from weak entropy sources. OBJECTIVES: This study introduces the Single Random Number Counter-based Digital Signature Algorithm (SRNCDSA), an enhanced variant of ECDSA designed to address vulnerabilities arising from random key reuse while preserving high performance in resource-constrained environments. METHODS: SRNCDSA generates nonces by combining a static random number with an incrementing counter, ensuring deterministic uniqueness and maintaining high entropy without requiring fresh randomness for each signature. The proposed scheme was implemented and evaluated on a constrained hardware platform representative of UAV and IoT environments. RESULTS: SRNCDSA achieved an average computational cost of 0.002946 seconds per signature and supported 20,366.62 signatures per minute, with moderate CPU utilization (7.45%) and relatively high memory consumption (73.02%). The nonce entropy reached 7.6438566 bits, approaching the theoretical maximum of 8 bits at the byte level. CONCLUSION: SRNCDSA provides a practical and efficient countermeasure to nonce reuse in ECDSA, combining robust security guarantees with performance characteristics suitable for real-time embedded systems.
Copyright (c) 2025 Youcef Benabderrezak, Mohamed Amine Riahla, Samiya Hamadouche https://creativecommons.org/licenses/by-nc-sa/4.0
An Improved & Smart Clock Synchronisation Model for Emblematic IoT Applications
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.
Copyright (c) 2025 Divya Upadhyay, Ashwani Kumar Dubey https://creativecommons.org/licenses/by-nc-sa/4.0
Effective Object detection and Tracking using Attention-Driven YOLO v9 Model with Multi-Stage Cascaded Convolutional Model
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.
Copyright (c) 2025 Krishna Mohan A, P. V. N. Reddy, K. Satyma Prasad https://creativecommons.org/licenses/by-nc-sa/4.0
Blockchain-Assisted Authentication and Energy-Efficient Clustering Framework for Secure IoT Communication
Securing sensitive information is a challenging task in an IoT environment due to the resource constraints of edge devices. Simultaneously, ensuring energy-efficient communication is equally important in clustered IoT networks. Resource depletion impacts the overall network lifetime. Existing methodologies treat authentication and routing as separate tasks, which leads to security vulnerabilities. Many existing authentication techniques use centralized control and static key management and are therefore vulnerable to various kinds of attacks including impersonation and brute force attacks. To address these issues, a blockchain based decentralized authentication and privacy-preserving framework for clustered IoT networks is proposed in this paper. A Trusted Domain Authority (TDA) enforces authentication among IoT devices, users, and gateways. It securely stores the authentication credentials of these entities in the blockchain and creates dynamic session keys for the Cluster Heads(CHs). The clusters are formed using a ranking-based K-Nearest Neighbour (KNN) algorithm. Reward-based Deep Q-Learning (OR-DQL) selects optimal CHs by using energy, vicinity, and trust as parameters. Additionally, the proposed framework safeguards packet headers from traffic analysis using a bounded Laplace differential privacy mechanism. The TDA generates dynamic session keys using Chinese Remainder Theorem (CRT) and securely distributes them to the CHs using the lightweight PRESENT cipher. The proposed system is implemented on the NS-3 network simulator. The simulation results demonstrate an improvement in throughput by 30.7%, a reduction in energy consumption by 24%, and a reduction in end-to-end delay by 27.7% compared to protocols such as ESMR and PBA. These results confirm that the suggested system can provide energy efficient secure communication in IoT networks.
Copyright (c) 2025 G.C. Madhu, Vijayakumar Peroumal https://creativecommons.org/licenses/by-nc-sa/4.0
Optimization of Deep Learning Technique for OFDM Receivers in 6G Wireless Communications
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.
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
Facial Recognition Enabled Smart Security Lock System Using Machine Learning Approach
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.
Advances in Internet of Things and its cybersecurity applications
Copyright (c) 2025 M. Marimuthu , G. Mohanraj, J. Akilandeswari, V. Sathiyapriya https://creativecommons.org/licenses/by-nc-sa/4.0
Enhanced security based on Context Aware Proactive Trust Aware Routing Protocol using secure Preemptive Verifiable Key Handover Policy for VANET Environment
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.
Blockchain-Based Digital Trust in IoT for Edge Computing
Copyright (c) 2025 K N Anupama, R Nagaraj https://creativecommons.org/licenses/by-nc-sa/4.0
Advanced Attention-Enhanced BiLSTM-GRU Model for Real vs. Fake News Detection
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.
Copyright (c) 2025 M. Arunkrishna, B. Senthilkumaran https://creativecommons.org/licenses/by-nc-sa/4.0
Synergistic Integration of Quantum and Classical Machine Learning Models for High-Fidelity Asteroid Hazard Detection
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 danger zone.
Quantum Convolutional Neural Networks (QCNN)
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
Variation of Production Line Energy Consumption: Stochastic Process Models for Single and Multiple Machine Systems
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.
Copyright (c) 2025 Po-Jung Lai, Hao-Chu Lin, Hui Hsin Chin https://creativecommons.org/licenses/by-nc-sa/4.0
A Secure and Efficient Blockchain-Based Framework for Smart Cities Using Physics-Informed Neural Networks
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.
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
Interlink Platform for School, Higher and Technical Education in India: Design Platform
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.
IoT, Machine Learning and Data Analytics for Smart Environment
Copyright (c) 2025 Nitesh Ghodichor, Pratham Chopde, Mansi Choudhari, Saloni Rangari, Pratham Badge https://creativecommons.org/licenses/by-nc-sa/4.0
All-in-Net: Scorekeeping in Basketball Training using a Mobile Phone Camera and Image
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.
Mobile Computing Systems and Applications
Copyright (c) 2025 Yonit Rusho, Marcelo Sihman, Yeshayahu Huzler https://creativecommons.org/licenses/by-nc-sa/4.0