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
Culturally-Aware Artificial Intelligence: Personal Values and Technology Acceptance among AI Researchers in China and Germany
INTRODUCTION: AI development is driven by innovation and cultural contexts of collaboration. As AI and IoT systems shape global interaction, understanding cultural influences on technology perception is key for adaptive design and governance. This study compares personal values and AI acceptance among researchers in China and Germany – two leading yet culturally distinct ecosystems. OBJECTIVES: To identify value patterns supporting trustworthy AI and effective cross-cultural collaboration in research and IoT contexts. METHODS: A cross-national survey (n = 200) using the Portraits Value Questionnaire (PVQ) and the Digital Technology Acceptance Scale (DTAS) examined factors shaping AI perception. RESULTS: Chinese participants show higher AI acceptance and stress self-enhancement and conservation; Germans emphasize self-transcendence and greater caution. CONCLUSION: Findings inform culture-aware AI design, value-aligned governance, and intercultural collaboration.
Copyright (c) 2026 Dominic Lammert, Ming Liu, Stefanie Betz, Jacqueline Lammert, Jürgen Pfeffer 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
The Statistical Analytical Review of IoT Threat Detection Mechanisms: Quantitative Evaluation and Multidimensional Performance Assessment Analysis
Increased deployment of IoT systems in industrial, healthcare, smart city, and home environments has expanded the attack surface and complexity of cyber threats. Though a plethora of detection techniques have emerged in literature in the last decade, an unforgivable absence of statistical rigors and compare-and-contrast analysis on the operational characteristics is apparent in the literature sets. This paper presents a statistical analytical review of various contemporary IoT threat detection methods across a wide array of architectures: classical machine learning, deep learning, federated learning, blockchain-based systems, quantum-enhanced frameworks, and hybrid models. The review employs a multidimensional evaluation strategy, extracting both qualitative and quantitative metrics from each study; these enable an objective comparison across heterogeneous systems, Standard performance parameters—Scalability, Delay, Time Complexity, Memory Complexity, Make span, and Analysis Efficiency—are tabulated; thus, an unfolding of a universal analytical framework with almost 300 data points exposes trade-offs, bottlenecks to efficiency, and constraints to deployment. Furthermore, evaluation of the application-specific techniques for healthcare, agriculture, and smart grids were conducted in relation to adaptability and domain specifications. The work identifies that hybrid deep networks (e.g., CNN-LSTM) provide better accuracy at higher computation cost, while TinyML and ensemble models present a trade-off factor for both detection accuracy versus hardware efficiency. In addition, whereas quantum and blockchain Integrated systems have shown to be solid in theory, they face practical impairments. Research gaps identified here lead the discussion on future directions, toward explainability, energy-aware design, and adversarial resilience, thus providing a tangible roadmap toward the next generation of secure IoT frameworks.
Copyright (c) 2026 Lanka Kavitha, Kareemulla Shaik https://creativecommons.org/licenses/by-nc-sa/4.0
Multi-Agent Soft Actor-Critic with Graph Attention Networks for Adaptive Traffic Signal Optimization (MASAC-GAT)
INTRODUCTION: Adaptive Traffic Signal Optimisation (ATSO) is a challenging problem for urban traffic networks, having important implications for congestion reduction, traffic efficiency, and environmental conservation. Conventional traffic signal control techniques, i.e., fixed-time and rule-based control, fail to respond to dynamic traffic behaviour efficiently. OBJECTIVES: Recent developments in Reinforcement Learning (RL) have been promising for ATSO but are plagued by poor scalability, lack of coordination in multi-intersection networks, and inefficiency in dealing with continuous action spaces. METHODS: Furthermore, most RL-based solutions are based on simplistic state representation and fail to incorporate complex interdependencies between traffic signals. Considering these limitations, this paper introduces a new framework, Multi-Agent Soft Actor-Critic with Graph Attention Networks (MASAC-GAT), which unites the sample efficiency and stability of Soft Actor-Critic (SAC) with the relational modelling ability of Graph Attention Networks (GATs). RESULTS: The proposed method exhibited significant performance gains on three important traffic metrics: Signal Adjustment Efficiency (92%), Average Waiting Time (20–35 seconds), and Congestion Prediction Accuracy (93%), outperforming DQL, PPO, A2C, GNN-based variants, and knowledge sharing DDPG (KS-DDPG). Through minimised redundant signal changes and reduced vehicle delays, the method ushers in the next generation of smart transportation systems. CONCLUSION: The proposed method facilitates decentralised yet coordinated control of traffic signals by utilising local observations and global context. The proposed method unites real-time traffic observations, e.g., traffic volume, vehicle speeds, weather, accident reports, and signal status, into a customised OpenAI Gym environment for training and evaluation.
Copyright (c) 2026 R. M. Bommi, E. Bhuvaneswari, M. Rohini, G. Uganya https://creativecommons.org/licenses/by-nc-sa/4.0
Metaheuristic Approaches for Energy Optimization in Wireless Sensor Networks: A Systematic Review of Trends, Challenges, and Future Directions
Wireless Sensor Networks (WSNs) have become a foundational technology across diverse domains, ranging from critical healthcare monitoring to large-scale environmental management. However, the severe energy constraints of sensor nodes remain a persistent bottleneck, threatening both operational efficiency and network longevity. While metaheuristic algorithms offer promising solutions, existing reviews often focus on isolated network layers or rely on outdated datasets. Addressing this gap, this Systematic Literature Review (SLR) analyzes 48 primary studies published between 2019 and 2024, offering a holistic taxonomy that integrates routing and clustering optimizations. The findings reveal that Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) continue to dominate the field, each appearing in 23.5% of studies. However, a decisive shift is observed toward hybrid techniques such as Firefly–PSO and Grey Wolf Optimization variants which demonstrate enhanced adaptability in avoiding local optima, albeit at higher computational costs. Performance evaluations remain heavily simulation-driven, primarily focusing on energy consumption (31.2%), network lifetime (29.8%), and throughput (19.9%), while real-world validations in domains like Industrial IoT remain scarce. Furthermore, the review identifies emerging trends integrating Machine Learning, Edge Computing, and UAV-assisted routing into metaheuristic frameworks, signaling a transition toward more secure and multi-objective optimization strategies. This study concludes by highlighting critical open issues in fault tolerance, heterogeneous node management, and security-aware routing, providing a strategic roadmap for developing resilient, deployment-ready WSN solutions.
Copyright (c) 2026 Rudi Hartono, I Wayan Mustika, Selo Sulistyo 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
Enhancing Crop Yield Prediction with IoT and Agricultural UAVs: A Comprehensive Review
INTRODUCTION: Rapid development in the field of the Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) is allowing them to be utilized across multiple sectors like industrial manufacturing, healthcare, defense, etc. In the agricultural industry, IoT and UAVs are also proving themselves as one of the most promising technologies. These technologies have opened the door to numerous innovative opportunities in precision agriculture, particularly in predicting crop yields more accurately and efficiently. Traditional methods for crop yield prediction were based on manual sampling and statistical models, which proved to be time-consuming and less accurate. OBJECTIVES: This paper mainly contributes to the comprehensive study of IoT and UAVs in crop yield prediction. It highlights how data-driven methods, sensor technologies, and remote sensing enhance decision-making in precision agriculture. METHODS: The paper discusses traditional practices for crop yield prediction and their limitations. It explains the architecture of IoT and its various layers, including a detailed study and comparison of different IoT sensors, microcontrollers, and communication standards. The paper further focuses on the potential of UAVs for yield prediction, including details of different types of UAV platforms, control strategies, and communication standards. Additionally, the paper explains the benefits and limitations of integrating IoT and UAVs for more accurate crop yield prediction. RESULTS: The study demonstrated that IoT-enabled monitoring and UAV-based remote sensing improve crop prediction accuracy. CONCLUSION: Overall, this paper presents the transformative capability of integrating IoT and UAV in modernizing the process of crop yield prediction and other precision agriculture practices. As a future scope, the paper focuses on the use of edge/fog computing, mobile apps, and AI chatbots to enhance the power of IoT and UAVs in crop yield prediction.
Copyright (c) 2025 Purnima Awasthi, Sumita Mishra, Nishu Gupta 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
Fortifying RPL-Based 6LoWPAN in IoT: A Comprehensive Review of Emerging Attack Vectors and Defense Mechanisms using Machine Learning
Internet of Things (IoT) is a system of interconnected digital tools, including sensing elements and communication modules, that enables seamless data transfer over the Internet. These devices, though, run with resource limitations in terms of power, memory, and computational capabilities. Therefore, scientists created the IPv6 Over Low-power Wireless Personal Area Network (6LoWPAN) protocol, which allows for wireless communication among IoT devices while ensuring efficient use of resources. The Internet Engineering Task Force (IETF) has formally ratified 6LoWPAN, and its ROLL working group presented the Routing Protocol for Low-power and Lossy Networks (RPL), standardized as IETF RFC 6550, as a fundamental part of the 6LoWPAN stack. Although beneficial, RPL-based routing within IoT networks is particularly exposed to various security risks. This survey offers an in-depth overview of RPL-specific attacks and their defense mechanisms, as published in top-tier journals between 2015 and 2025. Based on a conceptual analysis of routing-based attacks in RPL, we introduce a new attack taxonomy that categorizes these attacks into 12 fundamental categories based on intrinsic features and behavior. Furthermore, we discuss the impact of every attack on network performance and describe actual cases in which these vulnerabilities have been targeted. Aside from attack classification, this survey proposes a novel taxonomy of defense mechanisms that categorizes them into 8 fundamental categories depending on their strategic method of routing attack mitigation. Every defense method is extensively studied concerning its applicability in actual IoT implementations. Furthermore, we critically study and evaluate different evaluation platforms, such as testbeds and simulators, used in investigating RPL-based security attacks and countermeasures, highlighting their applicability and usefulness in real-world environments. Lastly, we identify open research challenges by examining current literature gaps and outline future research opportunities for both researchers and practitioners. In addition, the survey notes a clear shift toward sequence-based and graph-driven learning models, supported by optimization with meta-heuristic techniques, which increasingly guide the design of modern IDS frameworks for RPL-enabled IoT networks. Our research is intended to offer valuable understanding and a strong platform for investigators to create more efficient security measures in response to evolving RPL-based attacks in the IoT environment.
Routing Protocol for Low-power and Lossy Networks (RPL)
IPv6 Over Low-power Wireless Personal Area Network (6LoWPAN) protocol
Copyright (c) 2025 Sudha Rani Unnam, Kareemulla Shaik 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