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IOT-BASED cyber security identification model through machine learning technique
Manual vulnerability evaluation tools produce erroneous data and lead to difficult analytical thinking. Such security concerns are exacerbated by the variety, imperfection, and redundancies of modern security repositories. These problems were common traits of producers and public vulnerability disclosures, which make it more difficult to identify security flaws through direct analysis through the Internet of Things (IoT). Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting databases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators. This proposed methodology's full potential has yet to be fulfilled, both in terms of its execution and its significance for security evaluation in application software. The study shows that the recommended mental security methodology works better when addressing the above inadequacies and the constraints of variation among cybersecurity alert mechanisms. Intriguing trade-offs are presented by the experimental analysis of our program, in particular the ensemble method that detects tendencies of computational security defects on data sources. 2023 The Authors -
IoT-Based Emergency Vehicle Detection Using YOLOv8
The rapid response of emergency services plays a critical role in saving lives and minimizing the impact of emergencies. However, identifying and locating emergency vehicles in real-Time can be challenging, especially in congested urban areas. This paper focuses on the emergency vehicle identification using the You Only Look Once version 8 (YOLOv8) algorithm and is focused on Internet of Things (IoT). The goal of this research is to develop a real-Time and precise emergency vehicle detection system using You Only Look Once version 8 (YOLOv8) algorithm, trained and tested with a dataset from a camera placed on a busy road, to enhance emergency service response times. The findings demonstrate the suggested system's ability to recognize emergency vehicles at a speed of 31 frames per second and with a 95% accuracy rate. Modern object identification algorithms include the You Only Look Once version 8 (YOLOv8) algorithm, which has shown promising results in various applications. The proposed system is built on a Raspberry Pi, which acts as an edge device and processes the video stream in realtime. The system consists of an Internet of Things (IoT) device with a camera that captures the live video stream, which is then fed into the algorithm for object detection. Once an emergency vehicle is detected, the system sends an email notification to the nearby emergency services, like a police station, using Simple Mail Transfer Protocol (SMTP), who can then take appropriate action. The results of this investigation show that the Internet of Things and You Only Look Once version 8 (YOLOv8) algorithms have great promise for creating effective and dependable emergency vehicle detection systems. The proposed system possesses the capacity to save lives and improve the effectiveness of emergency response by speeding up response times for emergency services. The suggested solution is also inexpensive, simple to implement, and adaptable to existing infrastructure. Through the development of intelligent transportation systems, emergency services can operate more safely and effectively. More sophisticated machine learning algorithms may be incorporated into the proposed system, and further sensors can be added to utilize alternative methods beyond camera-based detection to identify emergency vehicles. Overall, this research shows the potential of Internet of Things (IoT) and machine learning in creating creative emergency services solutions. 2025 Syed Suhana et al.Published by Sciendo. -
IoT-Based Response Time Analysis of Messages for Smart Autonomous Collision Avoidance System Using Controller Area Network
Many accidents and serious problems occur on the road due to the rapid increase in traffic congestion in all sections of the country. Autonomous vehicles provide a solution to successfully and cost-effectively avoid this problem while minimizing user disruption. Currently, more engaging electromechanical elements with an analog interface are used to develop affordable automobiles for efficient and cost-effective operation for a smart driving platform with a semiautonomous automobile, strengthening the vehicle involvement of the driver while increasing safety. As a result, it takes longer for various car elements to respond, which causes more problems during message transmission. This project aims to create a Controller Area Network (CAN) for analyzing message response times by incorporating a few application nodes on the IoT platform, such as an antilock braking system, flexible cruise control, and seat belt section, for some real-time control system applications. These application nodes are car analytical parts that are linked to IoT modules to prevent collisions. An autonomous device for collision avoidance and obstacle detection in a vehicle can impact road accidents if the CAN protocol is implemented. 2022 Anil Kumar Biswal et al. -
IoT-based smart alert system for drowsy driver detection
In current years, drowsy driver detection is the most necessary procedure to prevent any road accidents, probably worldwide. The aim of this study was to construct a smart alert technique for building intelligent vehicles that can automatically avoid drowsy driver impairment. But drowsiness is a natural phenomenon in the human body that happens due to different factors. Hence, it is required to design a robust alert system to avoid the cause of the mishap. In this proposed paper, we address a drowsy driver alert system that has been developed using such a technique in which the Video Stream Processing (VSP) is analyzed by eye blink concept through an Eye Aspect Ratio (EAR) and Euclidean distance of the eye. Face landmark algorithm is also used as a proper way to eye detection. When the drivers fatigue is detected, the IoT module issues a warning message along with impact of collision and location information, thereby alerting with the help of a voice speaking through the Raspberry Pi monitoring system. Copyright 2021 Anil Kumar Biswal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -
IoT-based smart healthcare video surveillance system using edge computing
Managing distributed smart surveillance system is identified as a major challenging issue due to its comprehensive aggregation and analysis of video information on the cloud. In smart healthcare applications, remote patient and elderly people monitoring require a robust response and alarm alerts from surveillance systems within the available bandwidth. In order to make a robust video surveillance system, there is a need for fast response and fast data analytics among connected devices deployed in a real-time cloud environment. Therefore, the proposed research work introduces the Cloud-based Object Tracking and Behavior Identification System (COTBIS) that can incorporate the edge computing capability framework in the gateway level. It is an emerging research area of the Internet of Things (IoT) that can bring robustness and intelligence in distributed video surveillance systems by minimizing network bandwidth and response time between wireless cameras and cloud servers. Further improvements are made by incorporating background subtraction and deep convolution neural network algorithms on moving objects to detect and classify abnormal falling activity monitoring using rank polling. Therefore, the proposed IoT-based smart healthcare video surveillance system using edge computing reduces the network bandwidth and response time and maximizes the fall behavior prediction accuracy significantly comparing to existing cloud-based video surveillance systems. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
IoT-Based Smart Indoor Navigation System with Voice Assistance for Museums
In the current era of smart heterogenous devices, the surrounding environment too needs to be smarter to match the gravity of such devices. Such advanced environment can be built with the technology called Internet of Things (IoT). Due to the presence of such vivid thing devices in the Internet of Things (IoT) environment, the task of automatically predicting the end users desires can play an important role when it comes to match the pace of modern society with too much diverse aspects. Since last decade, people have deviated their attention towards Indian ancient culture and Museums are eye catching attraction where our ancient cultural heritage exist. To improvise the slow pace growth of the tourism sector, there is the crucial requirement of technological improvement especially due to the restrictions on installations of external hardware within the close proximity. One prominent way of improving tourists experience at museums is to renovate existing museums with IoT-based smart devices which is programmed such a way to automatically navigate the user indoor and briefs the associated information about artwork without any user intervention. In this paper, we propose an IoT-based smart indoor navigation system along with voice assistance which can enhance the tourists experience in a museum. In addition, the proposed design also delivers the very personalized cultural contents related to the visited artworks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
IoT-based traffic prediction and traffic signal control system for smart city
Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of 5 phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
IoT-Driven Credit Scoring Models: Improving Loan Decision Making in Banking
By the game-changing possibilities of credit scoring models driven by the Internet of Things, this hopes to shed light on how the banking sector may enhance its loan decision-making procedures. Financial organisations are putting more and more faith in Internet of Things technologies to improve their risk assessment and lending processes. These IoT-driven models provide a more accurate and thorough assessment of creditworthiness by including real-time and detailed data on borrowers' activities, spending habits, and asset utilisation. This research examines the practicality and accuracy of Internet of Things (IoT) credit scoring by comparing it to conventional methods, looking closely at case researches, and analysing empirical data. The findings shed light on potential ways to enhance the loan approval and risk prediction procedures while also addressing concerns and considerations related to data privacy, security, and regulatory compliance. It is possible that decision-making frameworks could be altered by IoT-driven credit scoring algorithms, which could lead to a more inclusive and informed lending atmosphere. The contributes to the growing area of banking credit evaluation by showing that these models have promise. 2024 IEEE. -
IoT-Driven Dynamic Behavior Intervention Model for Sustainable Hygiene Practices: Insights from Household Water Consumption
IoT-enabled technologies have advanced so that smart sensor systems can observe and recognize human behavior in various contexts, including energy consumption and healthcare, with remarkable efficiency and effectiveness. One example is using the Internet of Things (IoT) technology to better comprehend human water consumption behavior and establish and maintain clean environments. Static models have typically been used to model the behavior intervention process throughout time. While these static approaches perform adequately when predicting general human behavior, they fall short when tracking and reacting to shifts in behavior in IoT settings. The authors of this study proposed a dynamic behavior intervention model to forecast the hygiene-related water-use habits of individual households. This model takes its cues from the structure equation model method and the notion of control engineering, which originated in the expanded theory of planned behavior (ETPB). The current ETPB dynamic behavior model with system parameter estimation using an artificial neural network (ANN)is assessed for its intervention trend using a residential water use case study. It has been shown that the ETPB dynamic model helps the process of intervening in peoples behavior. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
IoT-Enabled Analysis of COVID Data: Unveiling Insights from Temperature, Pulse Rate, and Oxygen Measurements
The COVID-19 pandemic has forced unparalleled transformation on healthcare systems around the world, demanding new and improved approaches for effective monitoring and diagnosis. In this context, we present a study titled IoT-Enabled Analysis of COVID Data: Unveiling Insights from Temperature, Pulse Rate, and Oxygen Measurements. The global impact of COVID-19, with millions of confirmed cases and fatalities, underscores the urgency of finding efficient monitoring solutions. To address this crisis, IoT-Enabled Health Monitoring Systems have emerged as a promising tool for remote patient monitoring and infection risk reduction. These systems leverage sensors to collect real-time data on the temperature, pulse rate, and oxygen saturation levels of the subject. The integration of a mobile application enables immediate access to this critical health information. In this study, we explore the use of IoT systems, which have demonstrated accuracy comparable to other devices on the market. By leveraging these technologies, we aim to provide healthcare professionals with valuable insights into patients health status, aiding in early detection, monitoring, and timely intervention. Our research contributes to the efforts to battle the COVID-19 pandemic by highlighting the potential of IoT-enabled monitoring systems in enhancing healthcare delivery, reducing infection risks, and ultimately saving lives. 2024 Scrivener Publishing LLC. -
IoT-Enabled Smart Breath Analyzer for Real-Time Monitoring of Ammonia, Alcohol, and VOC Biomarkers for Early Disease Detection
Real-time monitoring of breath biomarkers such as ammonia, alcohol, and volatile organic compounds (VOCs) is important for early disease diagnosis of metabolic and organ-related diseases. Traditional disease diagnosis techniques are invasive, time-consuming, expensive and not suitable for using in remote areas. To overcome these limitations, this paper proposes the design and development of an IoT-based Smart Breath Analyzer for real-time monitoring and analysis of ammonia, alcohol, and volatile organic compounds (VOC) concentration present in exhaled human breath. The system consists of different kinds of gas sensors connected to an ESP32 microcontroller for measuring gas concentration, which is processed and sent to Blynk application via Wi-Fi for visualization and disease prediction. A trained machine learning model is used in the system which classifies biomarker patterns that may be associated with conditions such as kidney disorders, respiratory issues, or alcohol influence, based on literature-derived thresholds. The system is presented as a proof-of-concept screening tool rather than a clinical diagnostic solution. The results are showed on an OLED display and accessed via a mobile app developed using the Blynk IoT platform. This non-invasive, affordable, and scalable solution improves continuous health monitoring and early diagnosis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
IoT-Enabled Smart Security Surveillance System for Farmland and Livestock Monitoring Using Computer Vision
Agriculture is the main source of income of every country and agriculture plays a critical role in sustaining human civilization by providing food, fuel, and other essential resources. Even though, the conflict between farmers and wildlife remains a significant challenge even today. It is essential to arrange the protection of fields and farms by deterring wild animals and predators without making harm to the wildlife. This study proposes a framework that can detect and classify animals or intruders or any natural calamities. The novelty of the study is deterring the wild animals using innocuous devices and chemical components instead of the harmful electric fences and other methods. Based on the classification it will take preventive measures and will send an alert to the Farmer. The preventive measures are ensuring that will not make any harm to the wild animals. The framework incorporated four major components PIR (Passive Infrared) Sensor, Raspberry Pi Camera, Scarecrow and GSM (Global System for Mobile Communications) Module. PIR Sensor detects an object, Raspberry Pi Camera captures the images and images classify the specific object, later based on the type of the animal the scare tactics will be applied by the scarecrow and if any unusual incident is happening like the presence of an intruder, fire or tornado the alert will send to the Farmer with the help of a GSM Module. 2025 IEEE. -
IOT-Enabled Supply Chain Management for Increased Efficiency
Deep learning methods have demonstrated potential Supply chain is a set or group of people as well as companies responsible for producing goods and getting it to their consumers. The producers of the raw materials are the first links in the chain, and the vehicle that delivers the finished goods to the client is the last. Lower costs and higher productivity are the benefits of an efficient supply network, which emphasizes the importance of management of supply chain. The internet of things, or IoT, is a network of mechanical and digital technology that can communicate with one another and send data without the need for human contact. Smart items were included into the conventional supply chain system to increase intelligence, automation potential, and intelligent decision-making. The existing supply chain system is offering previously unforeseen chances to increase efficiency and reduce cost. The aim and motive of our research is to analyze the methods of supply chain management where the main elements of IoT in management of supply chain will be highlighted. 2024 IEEE. -
IoT-Integrated CNN Deep Learning for Automated Breast Cancer Detection and Diagnosis
Breast cancer continues to be a primary cause of death in women, requiring prompt and accurate diagnosis to enhance treatment results. Traditional diagnostic techniques depend on manual assessment, which leads to possible misclassification, significant inter-observer variability, and delays in decision-making. Current deep learning models, including CNNs, frequently experience feature loss, gradient declining and restricted adaptability to real-time data. To overcome these restrictions, we present a hybrid framework combining CNN and ResNet that merges deep learning-based feature extraction with real-time data collecting from IoT devices. The proposed approach utilises CNNs for preliminary feature extraction, ResNet for hierarchical learning with residual connections, and IoT for real-time patient monitoring and automatic notifications. The dataset undergoes preprocessing through normalisation, augmentation, and histogram equalisation to improve image quality and learning efficacy. The model is trained with cross-entropy loss and the Adam optimiser, guaranteeing stability and excellent performance. The evaluation results indicate a substantial enhancement compared to baseline models, with an accuracy of 97, an F1-score of 95.3, and a recall rate of 96.4%, exceeding traditional deep learning (90 accuracy) and CNN-based models (80% accuracy). The suggested model similarly minimises mistakes, with RMSE and MSE values declining to 1.2 and 1.6, respectively, signifying reduced misclassification rates. The inclusion of IoT facilitates instantaneous data transmission with little latency, hence improving clinical decision-making and minimising diagnostic delays. This advanced system facilitates automated and precise breast cancer detection, providing an innovative method for early diagnosis, optimised treatment planning, and improved patient outcomes, while ensuring data privacy and security through encryption and commitment to healthcare regulations. 2026 Yamini Kalva, R. Ganesh Babu, Sindhu V, S. Gokul Pran, Garaga Srilakshmi, Kavitha C T, Sathish Kumar Shanmugam and V. Bhoopathy. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
IoT-Powered Health Monitoring System for Protecting Vital Organs Through Cloud-Based Diagnosis
The main objective of this research was the development and evaluation of an IoT- and machine learning-based health monitoring system capable of protecting patients vital organs through cloud diagnosis. This could be achieved by connecting a set of sensors, including temperature, pressure, heart rate, and oxygen sensors, to the patient and allowing them to communicate with the cloud to transmit real-time data via IoT technologies. The data could be further analyzed and predicted using cloud-based machine learning algorithms. This study investigated the performance of different machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naive Bayes (NB), and Negative Decision Trees (DT), for the purpose of patients health prediction based on the sensor data. After experimentation and evaluation, we found that the ANN model demonstrated the best predictive ability, with an accuracy level of 99.45%. The SVM, NB, and DT models also demonstrated good performance, with the accuracies of 96.5%, 94.34%, and 91.2%, respectively. Therefore, this research demonstrated that IoT and machine learning technologies could be successfully employed in healthcare for remote patient monitoring and timely prediction. The created system allows for real-time monitoring, which enables early prediction, potentially leading to improved patient outcomes, cost savings, and higher efficiency of provided care. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
IoT-Powered Innovations in Renewable Energy Generation and Electric Drive
This review explores the growing impact of the Internet of Things (IoT) on the energy sector, particularly in the context of renewable energy generation and electric drive systems. IoT technology has rapidly expanded into various sectors, including energy, smart cities, and industrial automation, revolutionizing monitoring, control, and management processes. In this paper, we examine the existing literature on IoT applications in energy systems, with a focus on smart grids. We also delve into the core IoT technologies, such as cloud computing and data analysis platforms, that underpin these innovations. Additionally, we address challenges associated with IoT implementation in the energy sector, notably privacy and security concerns, and suggest potential solutions, such as blockchain technology. Our findings provide valuable insights for energy policy-makers, economists, and managers, offering a comprehensive overview of how IoT can optimize energy systems. Furthermore, we highlight IoT's expanding role in renewable energy and electric drive applications, enhancing performance monitoring, management, and energy savings while also advancing research and education in engineering. The Authors, published by EDP Sciences, 2024. -
IoVST: Internet of vehicles and smart traffic - Architecture, applications, and challenges
The internet of things (IoT) is the network of sensors, devices, processors, and software, enabling connection, communication, and data transfer between devices. IoT is able to collect and analyze large amounts of data which can then be used to automate daily tasks in various fields. IoT holds the potential to revolutionise and create many opportunities in multiple industries like smart cities, smart transport, etc. Autonomous vehicles are smart vehicles that are able to navigate and move around on their own on a well-planned road. 2023, IGI Global. -
IPR in Stem Cell Research, Therapy, and Regenerative Medicine
According to the World Trade Organization, intellectual property rights are rights given to persons over the creations of their minds. They usually give the creator an exclusive right over the use of his or her creation for a certain period. There is a critical need for fresh developments in the existing medical diagnostic techniques, therapy, pharmaceutical medications, and research, in a world where such a sizable number of people are afflicted with various ailments, some of which are fatal and still incurable. Pharmaceutical companies are developing novel and cutting-edge ways to treat diseases at an increasing rate. The major pharmaceutical corporations in the world, including Pfizer, Miltenyi, Biotec, AstraZeneca, and Mesoblast Limited are pursuing research in the area of stem cell and regenerative medicine. Regenerative medicine, stem cell research, and therapy are currently regarded as groundbreaking developments in the medical sciences. Understanding their intellectual property rights and the legal means through which these businesses can safeguard their discoveries becomes crucial. This paper will analyze the meaning of stem cell and regenerative medicines, the eligibility of IPR in Stem cell research under the Indian Patents Act, of 1970 and the morality and public issues related to the same. 2024 Taylor & Francis. -
IPWM Based IBMSC DC-AC Converter Using Solar Power for Wide Voltage Conversion System; [Convertisseur DC-AC IBMSC bassur l'IPWM et utilisant l'ergie solaire pour un syste de conversion large tension]
This article proposes isolated bidirectional micro dc-ac single phase controlled (IBMSC) converter based on in-phase-voltage pulsewidth modulation (IPWM). This resonant IPWM converter, ratio of voltage conversion can be controlled from 0 to ?. So, this converter is highly referred for huge range voltage conversion. However, voltage conversion ratio determines power transfer direction and duty ratio. Power flow direction and duty cycle value can be varying smoothly, so it is suitable for dc-ac bidirectional power conversion application. Inverter mode and also rectifier mode are possible from bidirectional operation, which is controlled by a unified current controller. The proposed solution can achieve smooth switching grid operation with high efficiency. Working principle, design procedure, control strategy, and characteristics of the proposed converter are implemented with a prototype model of power rating 500 W with a voltage range of 20-50 V to test the ability of withstanding. Performance, feasibility, and effectiveness of the proposed converter are tested with this hardware test-bench model. 2022 IEEE. -
IRIS Data Classification using Genetic Algorithm Tuned Random Forest Classification
Optimising hyper-parameters in Random Forest is a time-consuming undertaking for several academics as well as professionals. To acquire greater performance hyper-parameters, specialists should explicitly customize a series of hyper-parameter settings. The best outcomes from this manual setting are then modelled and implemented in a random forest algorithm. Several datasets, on the other side, need various prototypes or hyper-parameter combinations, which may be time-consuming. To solve this, we offered various machine learning models and classifiers for correctly optimising hyper-parameters. Both genetic algorithm-based random forest and randomised CV random forest were assessed on performance measures such as sensitivity, accuracy, specificity, and F1-score. Finally, when compared to randomised CV random forest, our suggested model genetic algorithm-based random forest delivers more incredible accuracy. 2022 IEEE.