EAI Endorsed Transactions on Internet of Things

Performance Evaluation of Various Path Planning Methods for Robotics and Computational Geometry

Appears in:

IoT 24(1):

Authors:

Gokuldas Vedant Sarvesh Raikar, Gururaj HL, Vinayakumar Ravi, Wael Suliman

Abstract:

INTRODUCTION: In integrating Spiral Coverage into Cellular Decomposition, which combines structured grid-based techniques with flexible, quick spiral traversal, time efficiency is increased. OBJECTIVES: In the field of robotics and computational geometry, the study proposes a comparative explorati…INTRODUCTION: In integrating Spiral Coverage into Cellular Decomposition, which combines structured grid-based techniques with flexible, quick spiral traversal, time efficiency is increased.
OBJECTIVES: In the field of robotics and computational geometry, the study proposes a comparative exploration of two prominent path planning methodologies—Boustrophedon Cellular Decomposition and the innovative Spiral Coverage. Boustrophedon coverage has limitations in time efficiency due to its back-and-forth motion pattern, which can lead to lengthier coverage periods, especially in congested areas. Nevertheless, it is useful in some situations. It is critical to address these time-related issues to make Boustrophedon algorithms more useful in practical settings.
METHODS: The research centres on achieving comprehensive cell coverage, addressing the complexities arising from confined spaces and intricate geometries. While conventional methods emphasise route optimization between points, the coverage path planning approach seeks optimal paths that maximize coverage and minimize associated costs. This study delves into the theory, practical implementation, and application of Spiral Coverage integrated with established cellular decomposition techniques.
RESULTS: Through comparative analysis, it illustrates the advantages of spiral coverage over boustrophedon coverage in diverse robotics and computational applications. The research highlights Spiral Coverage's superiority in terms of path optimization, computational efficiency, and adaptability, proposing a novel perspective into cell decomposition. The methodology integrates the Spiral Coverage concept, transcending traditional techniques reliant on grids or Voronoi diagrams. Rigorous evaluation validates its potential to enhance path planning, exemplifying a substantial advancement in robotics and computational geometry.
CONCLUSION: Our findings show that spiral coverage is on an average 45% more efficient than conventional Boustrophedon coverage. This paper set the basis for the future work on how different algorithms can traverse different shapes more efficiently.
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GTBTL-IoT: An Approach of Curtailing Task Offloading Time for Improved Responsiveness in IoT-MEC Model

Appears in:

IoT 24(1):

Authors:

Eram Fatima Siddiqui, Tasneem Ahmed

Abstract:

INTRODUCTION: The Internet of Things (IoT) has transformed daily life by interconnecting digital devices via integrated sensors, software, and connectivity. Although IoT devices excel at real-time data collection and decision-making, their performance on complex tasks is hindered by limited power, …INTRODUCTION: The Internet of Things (IoT) has transformed daily life by interconnecting digital devices via integrated sensors, software, and connectivity. Although IoT devices excel at real-time data collection and decision-making, their performance on complex tasks is hindered by limited power, resources, and time. To address this, IoT is often combined with cloud computing (CC) to meet time-sensitive demands. However, the distance between IoT devices and cloud servers can result in latency issues.
OBJECTIVES: To mitigate latency challenges, Mobile Edge Computing (MEC) is integrated with IoT. MEC offers cloud-like services through servers located near network edges and IoT devices, enhancing device responsiveness by reducing transmission and processing latency. This study aims to develop a solution to optimize task offloading in IoT-MEC environments, addressing challenges like latency, uneven workloads, and network congestion.
METHODS: This research introduces the Game Theory-Based Task Latency (GTBTL-IoT) algorithm, a two-way task offloading approach employing Game Matching Theory and Data Partitioning Theory. Initially, the algorithm matches IoT devices with the nearest MEC server using game-matching theory. Subsequently, it splits the entire task into two halves and allocates them to both local and MEC servers for parallel computation, optimizing resource usage and workload balance.
RESULTS: GTBTL-IoT outperforms existing algorithms, such as the Delay-Aware Online Workload Allocation (DAOWA) Algorithm, Fuzzy Algorithm (FA), and Dynamic Task Scheduling (DTS), by an average of 143.75 ms with a 5.5 s system deadline. Additionally, it significantly reduces task transmission, computation latency, and overall job offloading time by 59%. Evaluated in an ENIGMA-based simulation environment, GTBTL-IoT demonstrates its ability to compute requests in real-time with optimal resource usage, ensuring efficient and balanced task execution in the IoT-MEC paradigm.
CONCLUSION: The Game Theory-Based Task Latency (GTBTL-IoT) algorithm presents a novel approach to optimize task offloading in IoT-MEC environments. By leveraging Game Matching Theory and Data Partitioning Theory, GTBTL-IoT effectively reduces latency, balances workloads, and optimizes resource usage. The algorithm's superior performance compared to existing methods underscores its potential to enhance the responsiveness and efficiency of IoT devices in real-world applications, ensuring seamless task execution in IoT-MEC systems.
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Heart Disease Diagnosis and Diet Recommendation System Using Ayurvedic Dosha Analysis

Appears in:

IoT 24(1):

Authors:

Kuldeep Vayadande, Chudaman D. Sukte, Yogesh Bodhe, Tanishka Jagtap, Atharv Joshi, Palash Joshi, Arushi Kadam, Sai Kadam

Abstract:

The current healthcare system often fails to account for individual health needs, leading to ineffective preventive measures and dietary guidance. Ayurvedic principles, which focus on the Dosha, offer a profound understanding of an individual's constitution, influencing their health, vulnerability …The current healthcare system often fails to account for individual health needs, leading to ineffective preventive measures and dietary guidance. Ayurvedic principles, which focus on the Dosha, offer a profound understanding of an individual's constitution, influencing their health, vulnerability to specific diseases, and ideal dietary choices. This paper explores the evolving intersection of ancient Ayurvedic wisdom and modern technology in the realm of disease diagnosis. Ayurveda, with its emphasis on personalized well-being, has long been a source of holistic health practices. In this context, the study delves into the intricate system of Ayurvedic Dosha analysis and its potential applications in contemporary healthcare. The research introduces an innovative way that seamlessly integrates traditional Ayurvedic pulse examination with state-of-the-art technology. By employing pulse sensors and advanced algorithms, the system not only identifies specific ailments but also classifies patients into Ayurvedic Prakriti types. Going beyond conventional diagnosis, this holistic approach extends to personalized recommendations, encompassing diet, lifestyle, Ayurvedic treatments, exercise, and daily routines. While addressing the challenges of harmonizing ancient principles with modern technology, the paper also presents the performance metrics of the model. The accuracy rates are as follows: Logistic Regression (LR) - 85.94%, Random Forest - 89.21%, Decision Tree - 99.70%, and k-Nearest Neighbors (KNN) - 86.43%. These metrics underscore the robustness of the system. In addition to outlining core concepts, methodologies, and model accuracies, the study explores current trends and recent developments in the field, offering readers a comprehensive understanding of Ayurvedic Dosha-based disease diagnosis. The research contributes to the broader discourse on healthcare by paving the way for early detection and individualized, holistic well-being for patients.
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Comparative Study on Anomaly based Intrusion Detection using Deep Learning Techniques

Appears in:

IoT 24(1):

Authors:

Sabeena S, Chitra S

Abstract:

With an array of applications, Wireless Sensor Networks (WSNs) have the potential to transform the world into a smart planet. WSNs consist of a collection of resource-constrained sensors that gather data, which is then utilized for decision-making and analysis, leading to improvements in quality of…With an array of applications, Wireless Sensor Networks (WSNs) have the potential to transform the world into a smart planet. WSNs consist of a collection of resource-constrained sensors that gather data, which is then utilized for decision-making and analysis, leading to improvements in quality of service, management, and efficiency. However, the open nature of WSNs exposes them to numerous vulnerabilities and threats. Operating in potentially hostile and unattended environments makes these networks attractive targets for adversaries. Therefore, it is essential to detect the presence of malicious attacks within the networks and implement robust security systems to address these challenges. While traditional security mechanisms such as authentication and cryptographic methods are commonly employed, they often fall short in effectively countering the dynamic nature of modern attacks. Hence, IDS (Intrusion Detection System) tends to continuously monitor the network and detect potential threats in real-time scenarios. This method possess the ability of identifying, responding promptly, preventing and thus ensures resilience of the network. Therefore, the present study reviews the various intrusion detection techniques and data collection methods. The main aim of the study is to investigate the design challenges of deploying IDS in a WSN environment. So, the study analysed the AI (Artificial Intelligence) based techniques involved in intrusion detection and how these techniques could be adopted in WSN. In addition, the comparative analysis of several ML (Machine Learning) and DL (Deep Learning) algorithms are also deliberated to portray the different deployment technique with corresponding outcomes. Further, the main challenges faced by each studies with their limitations are specified for supporting future researchers in developing new trends in intrusion detection for WSN.
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Assessment of CatBoost for Diabetes Prevention in Comparison to XGBoost: AI model capable of predicting the onset of diabetes

Appears in:

IoT 24(1):

Authors:

Jagadeesh Selvaraj, G. Gifta Jerith, Karthikeyan R, Senthil K

Abstract:

The metabolic disease known as diabetes is defined by consistently elevated blood sugar levels. An increase in hunger, thirst, and urine production are symptoms of high blood sugar. Untreated diabetes may lead to a variety of complications. Acute complications of diabetes include hyperosmolarity, h…The metabolic disease known as diabetes is defined by consistently elevated blood sugar levels. An increase in hunger, thirst, and urine production are symptoms of high blood sugar. Untreated diabetes may lead to a variety of complications. Acute complications of diabetes include hyperosmolarity, hyperglycemia, diabetic ketoacidosis, and perhaps death. The most devastating long-term effects are cardiovascular disease, cerebrovascular accident, chronic kidney disease, foot ulcers, and vision loss. The World Diabetes Organization estimates that 463 million people were diagnosed with diabetes in 2019. This population will increase by 578 million by 2030 and by 700 million by 2045, if forecasts pan out. The ability to quickly and accurately diagnose sickness is one of its current medical uses. Therefore, we might potentially reduce death rates via the use of machine learning by creating an AI model that can anticipate when diabetes will start. We will compare the CatBoost and XGBoost algorithms to find the one that is most suited for this purpose. Finally, using a number of health markers from the dataset, the study contrasted XGBoost and CatBoost, two models that may predict diabetes. We train and build our recommended system using Python on a real-world dataset taken from Kaggle. We evaluate our algorithms using precision, recall, F1score, and accuracy metrics, among other performance evaluation parameters. While XGBoost achieved an F1 Score of 91.75, an accuracy rate of 93.33%, a precision of 90.38%, and a recall of 90.63%. The accuracy, precision, recall, and F1 score for CatBoost are 96.09%, 93.38%, 91.38% and 92.13%, respectively. It's the most effective ensemble method, according to CatBoost.
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Exploring the Educational Transformations: A Systematic Literature Review on the Influence of the Internet of Things in Higher Education

Appears in:

IoT 24(1):

Authors:

Vasileios Paliktzoglou, Olympia Vlachopoulou

Abstract:

The rapid growth of the Internet of Things innovations stimulates higher educational institutions to invest in and adopt these technologies to support and enhance their learning and teaching strategies. This study aims to investigate the influence of the Internet of Things on teaching and learning …The rapid growth of the Internet of Things innovations stimulates higher educational institutions to invest in and adopt these technologies to support and enhance their learning and teaching strategies. This study aims to investigate the influence of the Internet of Things on teaching and learning in higher education, performing a systematic literature review. Therefore, this research focuses on the following research questions: R-Q1: What are the benefits of the Internet of Things for teaching and learning in higher education? R-Q2: What are the limitations of the Internet of Things for teaching and learning in higher education? The systematic literature review, including the search strategy, the inclusion and exclusion criteria, and the review of the titles, keywords, and abstracts, identified a total of 31 results, mainly journal and conference articles. The findings from the extracted articles in this review were grouped into eleven themes: adoption, personalized learning, learning efficiency, intelligent teaching, collaboration and connectivity, creativity, health and safety monitoring, latency time, security and privacy, quality and ethics, and financing issues. The findings suggest that the Internet of Things can enhance the learning quality, improve the gained knowledge, and reduce costs in higher education. Therefore, adopting a consistent Internet of Things implementation strategy is essential to address identified limitations in higher education.
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Digital Literacy: Comparative Review on Machine Learning Based Performance Assessment of Students

Appears in:

IoT 24(1):

Authors:

K. Shwetha, S. Shahar Banu

Abstract:

The E-learning system paved an opportunity to make drastic changes in the educational system all over the world. Several institutions began to implement online learning to offer internet based courses contrary to the traditional classroom teaching. These online courses tends to provide several pote…The E-learning system paved an opportunity to make drastic changes in the educational system all over the world. Several institutions began to implement online learning to offer internet based courses contrary to the traditional classroom teaching. These online courses tends to provide several potential benefits such as flexibility and opportunities, to discover knowledge of the students. It also offers innovations in learning strategies of the students and resolve several complexities by accessing information from internet. Though e-learning based systems produces certain advantages, they also possess limitations of co-operative learning, active learning and performance mitigations. To address these issues, the present study focused on the different AI based techniques used in the prediction of student’s academic performance. The main objective of the study is to analyze the primary factors that affects the learning through online and analyze the performance using different intelligent approaches. A comparative study of the AI based techniques is performed to analyze the different methods involved in the assessment of academic performance. Further, the present issues and future works of the studies is deliberated to produce optimized analysis systems. This tends to support several researchers to overcome the disputes and provide effective e-learning assessment systems.
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Brackish water parameters monitoring dashboard using Internet of things and industry 4.0

Appears in:

IoT 24(1):

Authors:

V. Sowmiya, G. R. Kanagachidambaresan

Abstract:

INTRODUCTION: Brackish water aquaculture plays a crucial role in meeting the growing global demand for seafood. It offers an opportunity to diversify aquaculture production and reduce pressure on overexploited marine resources. OBJECTIVES: By harnessing the unique properties of brackish ecosystems…INTRODUCTION: Brackish water aquaculture plays a crucial role in meeting the growing global demand for seafood. It offers an opportunity to diversify aquaculture production and reduce pressure on overexploited marine resources.
OBJECTIVES: By harnessing the unique properties of brackish ecosystems, this practice contributes to food security, economic growth, and sustainable resource management, while also promoting the conservation of valuable marine habitats. The development of a cutting-edge Indigenous Water Quality Monitoring Prototype named "Aqua BuoySis" for precision brackish water aquaculture utilizing machine intelligence.
METHODS: The prototype integrates sensors for Dissolved Oxygen (DO), pH, Temperature, Turbidity, and Total Dissolved Solids (TDS). These sensors are calibrated using a dynamic temperature-based machine-learning approach to ensure accuracy in real-time environments. Sensor calibration constants are uploaded to a server for comprehensive data calibration.
RESULTS: The system collects data at 20-second intervals, associating it with specific pond IDs. Data refinement is achieved through Long Short-Term Memory (LSTM) processing. An Android and Web application, available in native languages such as Tamil and Telugu, has been developed to provide live updates to aqua farmers, facilitating informed decision-making.
CONCLUSION: This technology represents a significant step towards enhancing precision in brackish water aquaculture through the fusion of machine intelligence and water quality management.
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A Comparative Study on Machine Learning Classifiers for Cervical Cancer Prediction: A Predictive Analytic Approach

Appears in:

IoT 24(1):

Authors:

Khandaker Mohammad Mohi Uddin, Iftikhar Ahammad Sikder, Md. Nahid Hasan

Abstract:

INTRODUCTION: Cervical cancer is a significant global health concern, particularly in underdeveloped nations where preventive healthcare measures are limited. Early identification of the risks associated with cervical cancer is essential for both prevention and treatment. OBJECTIVES: In recent yea…INTRODUCTION: Cervical cancer is a significant global health concern, particularly in underdeveloped nations where preventive healthcare measures are limited. Early identification of the risks associated with cervical cancer is essential for both prevention and treatment.
OBJECTIVES: In recent years, machine-learning algorithms have gained popularity as potential techniques for determining a person's risk of developing cancer based on demographic and medical information. This study uses a dataset that contains patient demographics, clinical history, and results from diagnostic tests to examine how machine learning-based algorithms can be used to predict the risks of cervical cancer.
METHODS: Various machine learning approaches are used to create predictive systems, including Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN),  Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), Nearest Centroid (NC), Multilayer Perceptron(MP), and AdaBoost (AB).
RESULTS: The prediction capability of these models is assessed using performance metrics such as accuracy, sensitivity, specificity, f-measure, precision, and area under the receiver operating characteristic curve (AUC-ROC). Our results show that the decision tree has the highest accuracy, precision, and f1-score (98.91%, 97.81%, and 0.9889). Additionally, model performance was optimized by the use of hyperparameter tuning. After hyperparameter adjustment, the Support Vector Machine (SVM) showed superior accuracy of 99.64%, precision of 99.26%, and an F1-score of 0.9963, thereby indicating its potential in cervical cancer probability prediction. We also created a web application that uses a machine-learning model to estimate the risk of cervical cancer.
CONCLUSION: The findings of this study highlight the significance of SVM and demonstrate the potential and capabilities of machine learning techniques to enhance accurate prediction and patient outcomes for cervical cancer screening.
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Design Of Intelligent Road Eye Using AI And Machine Learning For Automobiles

Appears in:

IoT 24(1):

Authors:

Chandrasekharan Nataraj, Lawrence Wong Ming Wei, Mukil Alagirisamy, Sathish Kumar Selvaperumal

Abstract:

The project aims to design an intelligent road eye by using AI and a machine learning approach to detect speedbumps and potholes on the road. The system design utilizes a YOLOv5 custom-trained model and COCO dataset in detecting the objects on the road. The system is integrated with lane detection …The project aims to design an intelligent road eye by using AI and a machine learning approach to detect speedbumps and potholes on the road. The system design utilizes a YOLOv5 custom-trained model and COCO dataset in detecting the objects on the road. The system is integrated with lane detection algorithms to achieve active steering feedback and pothole avoidance. Based on the detection results, feedback will be given in the form of visual, audio, and steering angles, allowing the driver to have sufficient response time to perform braking or steering adjustments where applicable. The trained model can achieve a mean average precision value (mAP) of up to 0.995 for all classes, and a maximum detection range of 5.77m and 34.8m for potholes and speedbump respectively. The future works of the project include integrating the algorithm into the vehicle to achieve autonomous braking and active pothole avoidance with the help of sensors and cameras on the vehicle, as well as adopting augmented reality (AR) to project the visual feedback on the vehicle windscreen.
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Blockchain Based Cryptographic Algorithm for Data Protection in Electronic Voting System

Appears in:

IoT 24(1):

Authors:

Vinayachandra , Krishna Prasad K

Abstract:

The development of digital world created advancement of technology, by which the data services are electronically transacted. Electronic voting is used to improve the election system in several ways. But technical issues associated with protection of data, is considered as a major concern leading t…The development of digital world created advancement of technology, by which the data services are electronically transacted. Electronic voting is used to improve the election system in several ways. But technical issues associated with protection of data, is considered as a major concern leading to illegal activities and threats. Some of the conventional methods used in securing e-votes are matching finger prints of individual, control unit accessibility, which provides decreased capability of attaining proper authenticity and security. Even several cryptographic methods are used for data security, but produced limited accuracy. So, to improve the reliability and accuracy of data protection in e-voting, combination of AES (Advanced Encryption System) and RSA (Rivest, Shamir, Adleman) combined with blockchain technology is implemented. AES uses larger key size which makes the data privacy robust. But encryption and decryption time produces decreases computational speed. So, AES is combined with RSA, as it uses shorter key length and easy to implement with block-chain algorithm, it is deployed to decrease encryption and decryption time, thus producing highly sensitive data security. The performance of the system is validated by calculating the encryption and decryption time, block size and verification time of original data with output data.
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Spellbound Kannada: Harnessing Conditional Generative Adversarial Networks for Transformative Word Suggestion Systems in Kannada Language Processing

Appears in:

IoT 24(1):

Authors:

Prathibha R J, Anupama N, Sudarshan A S

Abstract:

INTRODUCTION: The advancement of a word suggestion system model is driven by the need to enhance user interaction and efficiency in digital communication. Hence, the word suggestion system helps minimize typographical errors and spelling mistakes. Therefore, various traditional methods are used to …INTRODUCTION: The advancement of a word suggestion system model is driven by the need to enhance user interaction and efficiency in digital communication. Hence, the word suggestion system helps minimize typographical errors and spelling mistakes. Therefore, various traditional methods are used to suggest words to sentences; however, these traditional models are extremely time consuming, prone to errors and tedious. METHODS: Owing to these factors, the present paper focuses on developing a Kannada word suggestion system using cGAN (Conditional Generative Adversarial Networks), as this system is designed to significantly enhance user interaction by offering predictive text suggestions in the Kannada language. RESULTS: The training dataset, which resides on AWS S3, comprises a comprehensive collection of Kannada texts utilized for both training and validation purposes. Furthermore, the implementation of the model leverages the TensorFlow and keras framework, specifically employing long short-term memory (LSTM) networks for effective sequence prediction and generation. LSTMs are particularly advantageous in NLP processing because they can capture long-term dependencies within sequential data. To facilitate user interaction, a web-based interface has been developed using Flask, enabling users to input initial characters and receive dynamically generated Kannada word suggestions. CONCLUSION: This paper not only delves into the application of cGANs within the realm of NLP but also illustrates practical deployment strategies utilizing cloud services and modern web technologies. Overall, the proposed approaches demonstrate the potential of the cGAN in enhancing the user experience through intelligent text prediction systems tailored for the Kannada language.
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Innovative Human Interaction System to Predict College Student Emotions Using the Extended MASK-R-CNN Algorithm

Appears in:

IoT 24(1):

Authors:

Dinesh P, Thailambal G

Abstract:

There is a rising demand for emerging machines that can be self-decisive and intelligent. Machines can capture the emotions and gestures of college students to mechanise tasks and handle interactions better. Facial expressions based on emotion recognition are practices that play a substantial role …There is a rising demand for emerging machines that can be self-decisive and intelligent. Machines can capture the emotions and gestures of college students to mechanise tasks and handle interactions better. Facial expressions based on emotion recognition are practices that play a substantial role in the modern fields of artificial intelligence and computer vision. Numerous manual methods for detecting emotions are focused on few basic emotions. Additionally, significant time is needed for appropriate detection. Nonetheless, these techniques are time-consuming and inefficient for obtaining better results. Therefore, an effective object detection model is needed to address such issues. To overcome these challenges, several studies have focused on object detection systems to provide effective emotion prediction. Conversely, it results in a lack of speed, precision and computational complexity. To improve object detection performance, the proposed model employs deep learning (DL)-based adaptive feature spatial anchor refinement with a mask region-based convolutional neural network (Mask RCNN). It uses the Facial Expression Recognition (FER) 2013 dataset for the evaluation process. Correspondingly, the efficacy of the projected model is calculated via various evaluation metrics, such as the recall, precision and mean average precision (mAP), to estimate the performance of the proposed DL method. It achieves 0.75298 for MAP@50, 0.70252 for precision and 0.66606 for recall. Furthermore, a comparison of existing models reveals the efficiency of the proposed DL method. The present research is intended to contribute to emerging object detection methods for enhancing real-time analysis of student emotions in various environments, such as classrooms and online education.
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All-in-Net: Scorekeeping in Basketball Training using a Mobile Phone Camera and Image

Appears in:

IoT 24(1):

Authors:

Yonit Rusho, Marcelo Sihman, Yeshayahu Huzler

Abstract:

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 an…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.
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Synergistic Integration of Quantum and Classical Machine Learning Models for High-Fidelity Asteroid Hazard Detection

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IoT 24(1):

Authors:

Kuldeep Vayadande, Yogesh Bodhe, Amol A. Bhosle, Kavita Sultanpure, Geetanjali Yadav, Ajit R. Patil, Jyoti Jayesh Chavhan, Amolkumar N. Jadhav, Preeti Bailke

Abstract:

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 trad…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.
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Navigating the Future of Intelligent Transportation: Challenges and Solutions in 6G V2X and V2V Networks

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IoT 24(1):

Authors:

Spandana Mande, Shaik Salma Asiya Begum, Nandhakumar Ramachandran

Abstract:

Recent advancements in Information and Communication Technologies (ICT) have transformed vehicular communication, facilitating Vehicle-to-Vehicle (V2V) and Vehicle-to-Everything (V2X) connectivity. These technologies enhance travel safety and efficiency while also reducing pollution and accident ra…Recent advancements in Information and Communication Technologies (ICT) have transformed vehicular communication, facilitating Vehicle-to-Vehicle (V2V) and Vehicle-to-Everything (V2X) connectivity. These technologies enhance travel safety and efficiency while also reducing pollution and accident rates through optimised resource utilisation. V2X applications encounter significant challenges concerning traffic safety, data security, and scalability, necessitating thorough evaluation prior to extensive implementation. The rising prevalence of connected vehicles presents challenges, including network instability, heterogeneity, and extensive data management, which require novel solutions for effective communication. This study gives a new view on how to combine 6G technologies with V2X and V2V communication systems. It addresses important issues like security, resource allocation, and making sure that communication is very reliable and has low latency. Our study is different from others because it looks at all the technologies that make this possible, including Proportional Fairness Algorithms, machine learning, blockchain, and Reconfigurable Intelligent Surfaces (RIS). We emphasise the significance of collective perception (CP) in improving traffic safety and resource efficiency through the sharing of sensor data among V2X-enabled entities, including vehicles, roadside units (RSUs), and vulnerable road users.This research provides a thorough analysis of advanced methodologies and their limitations, making it a valuable resource for scholars, academics, and industry professionals. It offers a framework for developing future intelligent transportation systems that utilise 6G capabilities to achieve high reliability, minimal latency, and optimal spectrum efficiency in vehicular networks.
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Optimization of Deep Learning Technique for OFDM Receivers in 6G Wireless Communications

Appears in:

IoT 24(1):

Authors:

Kasetty Lakshmi Narasimha, V. Saraswathi, Mummidi SubbaRaju, M. Koteswara Rao, Kapula Kalyani Kalyani, Anil Kumar R

Abstract:

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) archi…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.
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Advancing IoT Security with an Innovative Machine Learning Paradigm for Botnet Attack Detection

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IoT 24(1):

Authors:

Punitha P, Dinesh Kumar V. K, Lakshmana Kumar R

Abstract:

INTRODUCTION: In contemporary society, everyday operations are greatly improved by the Internet of Things (IoT), which connects physical devices to provide digital services. IoT technology offers unified services and streamlines activities across various domains, ranging from remote monitoring to s…INTRODUCTION: In contemporary society, everyday operations are greatly improved by the Internet of Things (IoT), which connects physical devices to provide digital services. IoT technology offers unified services and streamlines activities across various domains, ranging from remote monitoring to sophisticated welfare systems. However, the growing number of IoT devices presents a security concern. Many of these devices are susceptible to exploitation, leading to diverse vulnerabilities.
OBJECTIVES: Resource-constrained IoT devices become prime targets for botnet attacks, manifesting in various forms and penetration methods. Despite numerous research efforts introducing multiple approaches for detecting botnet attacks in IoT, existing methods often fail to achieve satisfactory detection rates.
METHODS: Additionally, these approaches struggle to comprehensively analyze the diverse communication networks within the expansive realm of IoT devices. This study proposes an innovative machine-learning framework for detecting IoT botnet threats to address these limitations.
RESULTS: This conceptual framework exhibits a remarkable capability to identify a spectrum of botnet attacks, showcasing a detection accuracy of 99.5 per cent, significantly surpassing the performance of other prevalent machine-learning approaches.
CONCLUSION: Through this research, we aim to enhance the security paradigm of IoT networks, ensuring robust protection against evolving botnet threats in the dynamic landscape of interconnected devices.
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Major vulnerabilities in Ethereum smart contracts: Investigation and statistical analysis

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IoT 24(1):

Authors:

Mohammad Pishdar, Mahdi Bahaghighat, Rajeev Kumar, Qin Xin

Abstract:

The general public is becoming increasingly familiar with blockchain technology. Numerous new applications are made possible by this technology's unique features, which include transparency, strong security via cryptography, and distribution. These applications need certain programming tools and in…The general public is becoming increasingly familiar with blockchain technology. Numerous new applications are made possible by this technology's unique features, which include transparency, strong security via cryptography, and distribution. These applications need certain programming tools and interfaces to be implemented. This is made feasible by smart contracts. If the prerequisites are satisfied, smart contracts are carried out automatically. Any mistake in smart contract coding, particularly security-related ones, might have an impact on the project as a whole, available funds, and important data. The current paper discusses the flaws of the Ethereum smart contract in this respect. By examining publically accessible scientific sources, this work aims to present thorough information about vulnerabilities, examples, and current security solutions. Additionally, a substantial collection of current Ethereum (ETH) smart contracts has undergone a static code examination to conduct the vulnerability-finding procedure. The output has undergone assessments and statistical analysis. The study's conclusions demonstrate that smart contracts have several distinct flaws, including arithmetic flaws, that developers should be more aware of. These vulnerabilities and the solutions that can be used to address them are also included.
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Revitalizing Image Retrieval: AI Enhancement and Metaheuristic Algorithm Adaptation

Appears in:

IoT 24(1):

Authors:

Kumaravel Pichaimani, S. Thabasu Kannan

Abstract:

INTRODUCTION: In recent years, development of digital technology has led to number of images, which can be stored in digital format. However, searching and retrieval of images in large image DB (Database) is a mammoth task. Therefore, different image retrieval techniques have been used for retrievi…INTRODUCTION: In recent years, development of digital technology has led to number of images, which can be stored in digital format. However, searching and retrieval of images in large image DB (Database) is a mammoth task. Therefore, different image retrieval techniques have been used for retrieving the suitable images, which includes retrieval of images using keywords or annotations, however, these methods are considered to be time consuming and leads to imprecise outcome.
OBJECTIVES: Therefore Effective and precise retrieval of suitable images from huge DB can thrived by utilizing CBIR (Content Based Image Retrieval) system. However, incorporation of CBIR in most existing studies resulted in low accuracy for IR. So, proposed model incorporates Modified ResNet50 (M-ResNet50) and VGG 16 model for feature extraction in order to extract the best features as M-ResNet50 utilizes extra dense layers which aids in better feature extraction process.
METHODS: After feature extraction, the features are fused using PCA and fed to Modified PSO (M-PSO) model for obtaining optimized features since M-PSO is fast and aids in selecting optimal features after processing from insignificant features that primarily set the preferred number of necessary features.
RESULTS: Moreover, M-PSO require less parameters to tune instead of a huge number of parameters by incorporating K parameters of KNN algorithm in order to find the nearest images to Query Images (QI), thereby making the model appropriate for IR process with better similarity score.
CONCLUSION: The proposed model utilizes 8 different sun images at different intervals for IR process. Finally, the proposed model is evaluated by using several metrics such as accuracy, precision, recall and F1 score, besides the proposed model is compared with various existing models in order to evaluate the efficiency of the proposed model.
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Wireless 5G Network in Edge Computing Based On MIMO with Federated Learning and Clustering Integrated Reinforcement Learning

Appears in:

IoT 24(1):

Authors:

Manikandan Parasuraman, Sivaram Rajeyyagari, Ramesh Sekaran, Suthendran Kannan, Vinayakumar Ravi

Abstract:

Edge Computing (EC) is a revolutionary architecture that brings Cloud Computing (CC) services closer to data sources than ever before. This research proposed novel technique in edge computing network based on wireless 5G technology using MIMO_federated learning integrated with Reinforcement neural …Edge Computing (EC) is a revolutionary architecture that brings Cloud Computing (CC) services closer to data sources than ever before. This research proposed novel technique in edge computing network based on wireless 5G technology using MIMO_federated learning integrated with Reinforcement neural network. Here the aim is to enhance the resource allocation by Decentralized Federated learning in multiple user based MIMO (De_Fed_L- MIMO) networks. Then the energy efficiency and channel optimization of the network is carried out using K-means clustering integrated with Reinforcement learning (K-means_RL). Here the experimental analysis is carried out in terms of number of users of network as well as number of edge server by DoF of 92%, Spectral efficiency of 92%, Energy efficiency of 96%, Signal to noise ratio (SNR) of 85%, Coverage area of 92%, RL training accuracy of 95%, FL training accuracy of 98%.
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Optimizing Healthcare in the Digital Era: Fusion of IoT with other Techniques

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IoT 24(1):

Authors:

Rashmi Singh, Aryan Chaudhary, Vidyapati Kumar

Abstract:

The Internet of Things (IoT) has helped explore the healthcare industry. The present paper discusses the benefits and challenges associated with IoT in healthcare, highlights notable use cases, and presents the future prospects and considerations for successful implementation. Through a comprehensi…The Internet of Things (IoT) has helped explore the healthcare industry. The present paper discusses the benefits and challenges associated with IoT in healthcare, highlights notable use cases, and presents the future prospects and considerations for successful implementation. Through a comprehensive examination of the topic, this paper aims to provide insights into the role of IoT in enhancing healthcare delivery, improving patient outcomes,  and transforming the healthcare domain. A case study of brain tumor classification is investigated to explore IoT's applicability in healthcare.  The VGG 16 model improved more consistently over the epoch, achieving higher validation accuracy than other models. In contrast, the discrepancies in validation accuracy and loss indicate the degree of variability of these models. The concept is augmented with fuzzy logic, nearness monitoring, and IoT in healthcare to understand future applicability, promising a better perspective on their transformational prowess.
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A New Approach for Solving Fractional Differential Equations Incorporating Ramadan Group Transform and Machine Learning

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IoT 24(1):

Authors:

Prabakaran Raghavendran, Tharmalingam Gunasekar, Saikat Gochhait

Abstract:

This paper examines various types of fractional differential equations using fractional calculus methods. It extends the classical Frobenius method and introduces key theorems that apply the Ramadan Group transform and other techniques. Additionally, the research incorporates machine learning, spec…This paper examines various types of fractional differential equations using fractional calculus methods. It extends the classical Frobenius method and introduces key theorems that apply the Ramadan Group transform and other techniques. Additionally, the research incorporates machine learning, specifically neural networks, to solve these equations. The paper demonstrates that machine learning can enhance the solution process through data generation, model design, and optimization. Examples provided illustrate how combining traditional methods with machine learning can effectively solve fractional differential equations.
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Integration of Blockchain and IoT for Enhanced Transparency in Diamond Supply Chain

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IoT 24(1):

Authors:

Amit Sharma, L. Ganesh Babu, Mrunalini U. Buradkar, M. Shanmathi, J. Vinisha, M. Udhayamoorthi

Abstract:

PURPOSE: With a focus on enhancing transparency, lowering the risk of fraud, and ensuring ethical sourcing practices, this research aims to investigate how blockchain and IoT technologies can be incorporated into the diamond supply chain. This study addresses the complexities and challenges of impl…PURPOSE: With a focus on enhancing transparency, lowering the risk of fraud, and ensuring ethical sourcing practices, this research aims to investigate how blockchain and IoT technologies can be incorporated into the diamond supply chain. This study addresses the complexities and challenges of implementing these technologies in an industry characterized by fragmented information sharing and centralized data storage.
DESIGN/METHODOLOGY/APPROACH: Using both qualitative and quantitative analysis, the research uses a mixed-methods approach. While secondary data was obtained from previously published works, industry reports, and case studies, primary data was gathered through semi-structured interviews with professionals in the field. The implementation of the prototype system was carried out in three phases: Define, Operate, and Test. Ethereum was chosen for its smart contract capabilities, and various IoT sensors were deployed to monitor environmental conditions and track the real-time location of diamonds.
FINDINGS: The integration of blockchain and IoT technologies significantly enhanced transparency within the diamond supply chain. The immutable nature of blockchain ensured tamper-proof records of transactions, while IoT sensors provided continuous real-time data, reinforcing transparency. The study observed a notable reduction in fraud due to the robust mechanisms of the system, which detected and prevented unauthorized alterations to the recorded data. Smart contracts automated compliance checks, ensuring adherence to ethical standards. Quantitative analysis revealed improvements in key metrics such as fraud reduction rates, transparency enhancements, and adherence to ethical sourcing standards.
ORIGINALITY/VALUE: This study bridges a notable gap in existing research by focusing on the diamond supply chain. It provides comprehensive, data-driven insights and practical recommendations for industry stakeholders and policymakers. The results highlight how combining blockchain and IoT technology can improve operational efficiency, transparency, and ethical practices in the diamond business. It is also feasible and scalable. The study's methods and findings add a great deal to the body of information already in existence and provide a framework for further investigation and application in related situations.
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Interlink Platform for School, Higher and Technical Education in India: Design Platform

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IoT 24(1):

Authors:

Nitesh Ghodichor, Pratham Chopde, Mansi Choudhari, Saloni Rangari, Pratham Badge

Abstract:

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 stud…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.
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A Secure and Efficient Blockchain-Based Framework for Smart Cities Using Physics-Informed Neural Networks

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IoT 24(1):

Authors:

Mohd. Asif Gandhi , Atul D Narkhede , N. Noor Alleema , M.P. Indumathi , Deepak Sundrani , R. Sasikala

Abstract:

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…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.
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Variation of Production Line Energy Consumption: Stochastic Process Models for Single and Multiple Machine Systems

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IoT 24(1):

Authors:

Po-Jung Lai, Hao-Chu Lin, Hui Hsin Chin

Abstract:

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 e…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.
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Advanced Attention-Enhanced BiLSTM-GRU Model for Real vs. Fake News Detection

Appears in:

IoT 24(1):

Authors:

M. Arunkrishna , B. Senthilkumaran

Abstract:

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 …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.
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Enhanced security based on Context Aware Proactive Trust Aware Routing Protocol using secure Preemptive Verifiable Key Handover Policy for VANET Environment

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IoT 24(1):

Authors:

K N Anupama , R Nagaraj

Abstract:

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 implemen…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.
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Facial Recognition Enabled Smart Security Lock System Using Machine Learning Approach

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IoT 24(1):

Authors:

M. Marimuthu, G. Mohanraj, J. Akilandeswari, V. Sathiyapriya

Abstract:

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 l…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.
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Effective Object detection and Tracking using Attention-Driven YOLO v9 Model with Multi-Stage Cascaded Convolutional Model

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IoT 24(1):

Authors:

Krishna Mohan A, P. V. N. Reddy, K. Satyma Prasad

Abstract:

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 st…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.
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An Improved & Smart Clock Synchronisation Model for Emblematic IoT Applications

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IoT 24(1):

Authors:

Divya Upadhyay, Ashwani Kumar Dubey

Abstract:

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 …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.
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Extending JASPER for Jammer-Angle Allocation Evaluation: Testing Algorithm Robustness Under Jammer Failures and Varying Resource Conditions

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IoT 24(1):

Authors:

Ahmet Burak Barakli, Okan Topçu, Halit Oğuztüzün

Abstract:

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 introduc…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.
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Migration of Microservices Execution Contexts Between Processing Zones in Software-Defined Vehicular Fog Networks

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IoT 24(1):

Authors:

Leonel D.C. Alvarenga, Pedro Sousa, António Costa

Abstract:

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 signi…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.
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Enhancing 5G Traffic Management with Programmable Intelligence and Open RAN Integration

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IoT 24(1):

Authors:

U.S.B.K. Mahalaxmi , Jeevana Sujitha Mantena, V.V. Jaya Rama Krishnaiah, Vatsala Anand, B. Rajani, Inakoti Ramesh Raja, Pavuluri Girija Krishna Sirisha

Abstract:

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 combi…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.
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Trust Based Energy Efficient Routing Design Based on Hybrid Particle Swarm Optimization (HPSO) for Wireless Sensor Networks

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IoT 24(1):

Authors:

S. Gnana Selvan , G. Gifta Jerith, C. Mahesh , S. Ravikumar, S. Samsudeen Shaffi, S. Jagadeesh

Abstract:

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 an…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.
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A Comprehensive Survey on Objective Functions in RPL Routing with Various Networking and Application Scenarios

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IoT 24(1):

Author:

Ibrahim Ali Alnajjar

Abstract:

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 gui…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.
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Research and Design a Low Power Electronic Shelf Label Based on E-paper Display and LoRa Technology

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IoT 24(1):

Authors:

Le Huy Trinh, Thanh-Tuan Phan, Fabien Ferrero, Nguyen Vu Truong

Abstract:

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 a…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%.
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Edge Computing for Computer Vision in IoT: Feasibility and Directions

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IoT 24(1):

Authors:

Panagiotis Savvidis, George A. Papakostas

Abstract:

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…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.
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Improved PIC16f877 microcontroller timer register to delimit the execution time of an operation

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IoT 24(1):

Authors:

Souleymane Mbathie, Mohamed Ouesse, Baboucar Diatta, Adrien Basse, Ridha Bouallegue

Abstract:

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 …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.
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A Quantitative Framework for the Selection of Hybrid Consensus Mechanisms in Blockchain-IoT Systems

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IoT 24(1):

Authors:

N. A. Natraj, J. J. Midhunchakkaravarthy, Brojo Kishore Mishra

Abstract:

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 alt…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.
 
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System of digitizing and paying of civil status documents via an RFID card

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IoT 24(1):

Authors:

Mohamed E.A. Ouesse, Diatta Baboucar, Basse Adrien, Bouallegue Ridha

Abstract:

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 …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.
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Conformer network-guided speech recognition for smart home Internet of Things system

Appears in:

IoT 24(1):

Authors:

Shengjun Huang, Dong-hyun Kim

Abstract:

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 thi…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.
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Glaucoma Retinal Image Detection through the Segmentation of OD Using Modified Deep Learning Method

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IoT 24(1):

Authors:

J. Ruby Elizabeth, D. Kesavaraja, S. Ebenezer Juliet, S. Jagadeesh, S. Samsudeen Shaffi, R. Umanesan

Abstract:

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…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.
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Enhanced Beamspace Channel Recovery in mmWave MIMO Using Deep Neural Networks

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IoT 24(1):

Authors:

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

Abstract:

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…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.
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Exploration of the application of wireless communication Internet in the child-friendly public space

Appears in:

IoT 24(1):

Author:

Gaoyang Liu

Abstract:

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…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.
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A Scalable Hybrid RF-BiLSTM Framework for Reliable IoT Traffic Threat Detection via Feature Selection and Temporal Pattern Recognition

Appears in:

IoT 24(1):

Authors:

Nadia Ansar, Suraiya Parveen, Ihtiram Raza Khan, Bhavya Alankar

Abstract:

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; w…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.
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Heterogeneous Multi-Model Ensemble for PPE Detection in Construction Environments

Appears in:

IoT 24(1):

Authors:

Thi-Nguyen Nguyen, Quang-Anh Nguyen-Duc, Dinh-Thai Kim, Duy Tung Doan, Minh-Duc Pham, Duc Cuong Van, Minh-Anh Nguyen

Abstract:

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 constructi…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.
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Trust-Aware Federated Learning with Differential Privacy for Secure AIoT in Critical Infrastructures

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IoT 24(1):

Authors:

Ramana Kadiyala, C. V. Lakshmi Narayana, S. China Ramu, Narsaiah Putta, Shyam Sunder Pabboju, B. Ramana Reddy

Abstract:

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…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.
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Blockchain-Assisted Authentication and Energy-Efficient Clustering Framework for Secure IoT Communication

Appears in:

IoT 24(1):

Authors:

G.C. Madhu, Vijayakumar Peroumal

Abstract:

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 metho…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.
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SRNCDSA: A Novel Enhancement of ECDSA Using a Single Random Number and Counter for Improved Security

Appears in:

IoT 24(1):

Authors:

Youcef Benabderrezak, Mohamed Amine Riahla, Samiya Hamadouche

Abstract:

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 …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.
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Fortifying RPL-Based 6LoWPAN in IoT: A Comprehensive Review of Emerging Attack Vectors and Defense Mechanisms using Machine Learning

Appears in:

IoT 24(1):

Authors:

Sudha Rani Unnam, Kareemulla Shaik

Abstract:

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. Therefor…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.
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Enhancing Crop Yield Prediction with IoT and Agricultural UAVs: A Comprehensive Review

Appears in:

IoT 24(1):

Authors:

Purnima Awasthi, Sumita Mishra, Nishu Gupta

Abstract:

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…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.
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