IoT in schools: Revolutionizing education through smart technology
description: The Internet of Things (IoT) is rapidly transforming various sectors, and education is no exception. This chapter explores the potential and applications of IoT technology in the education sector, shedding light on how it can revolutionize teaching, learning, and overall school management. By seamlessly integrating smart devices and applications into the classroom environment, IoT creates an interconnected and efficient learning ecosystem. The discussion covers the current state of IoT adoption in education, highlighting the benefits, challenges, and future prospects of this technological integration. 2025 selection and editorial matter, Adesh Kumar, Surajit Mondal, Gaurav Verma, and Prashant Mani; individual chapters, the contributors.
creator: Kaushik P.
source: Embedded Devices and Internet of Things: Technologies and Applications, pp. 230-241.
publisher: CRC Press
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 978-104011389-9; 978-103260600-2
format: Online
language: English
type: Book chapter
coverage: Kaushik P., CHRIST (Deemed to be University), Bangalore, India

IoT Infrastructure to Energize Electromobility
subject: Battery electric vehicles; Charging capabilities; Cloud platform; Communication technology; Edge computing; Electromobility; Energy consumption; Energy production; Information; IoT architecture; Power grid
description: Mobile technology is becoming more sophisticated as it advances. A comparison of different mobility scenarios was conducted. This chapter examined how electric vehicles interact with local energy systems in Stuttgart. Utilizing a travel demand model, a charging profle based on mobility patterns was generated for electric vehicles. During a quarter, charging demand and standard household load profles were used to analyze peak hour load fow for 349 households. Considering that peak loads and charging capacity are usually separated in time, greater charging capacity might lead to lower utilization of transformers. Furthermore, a study was conducted to determine if the existing infrastructure was adequate for future demand, focusing on substation transformer reserves. Electromobility is a rapidly growing and evolving application domain of the Internet of Things, with a huge market potential in various areas. It incorporates many stakeholders from manufacturers to the players of the energy market with all sorts of physical and virtual resources. It is essential to allow these devices and systems to collaborate to create advanced e-mobility services. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
creator: Jaisingh W.; Nanjundan P.
source: Power Systems, pp. 75-97.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 16121287
format: Online
language: English
type: Book chapter
coverage: Jaisingh W., School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, Bhopal, India; Nanjundan P., Department of Data Science, Christ University, Pune, Lavasa, India

IoT innovation in COVID-19 crisis
subject: Agriculture; Artificial intelligence; Cardiovascular; Computed tomography; Contact tracing; COVID-19 pandemic; Deep learning; Digital technology; Global positioning system; Healthcare data; IoT robots; IoT technology; Machine learning; Magnetic resonance imaging; Perception layer; Random forest; Respiration rate; Support vector machine; Temperature
description: The COVID-19 pandemic is a current global threat that surpasses provincial and radical boundaries. Due to the onset of the pandemic disease, the whole world turned entirely in a couple of weeks. Its consequences have come across the personal and professional life of human beings. The current situation focuses on precautions such as wearing a mask, maintaining social distancing, and sanitizing hands regularly. An innovative platform, and smart and effective IoT technology may be applied to follow these steps. This platform fulfills all critical challenges at the time of lockdown situations. IoT technology is more helpful in capturing real-time patient data and other essential information. IoT allows the tracing of infected people and suspicious cases and helps diagnose and treat patients remotely. It also paves the way to deliver essential medical devices and medicines to quarantined places. In the present ongoing crisis, IoT technology is inevitable in monitoring patients infected with COVID-19 through sensors and intertwined networks. The consultations are given to the patients digitally through video conferencing without meeting the medical expert in person. After the diagnosis is made digitally, IoT devices are used to track health data. Smart thermometers are used instead of traditional ones to collect valuable health data and share it with experts. The IoT robots are now a proven technology used for cleaning hospitals, disinfecting medical devices, and delivering medicines, thus giving more time to healthcare workers to treat patients. 2023 Bentham Science Publishers. All rights reserved.
creator: Genish T.; Vijayalakshmi S.
source: The Role of AI, IoT, and Blockchain in Mitigating the Impact of COVID-19, pp. 48-68.
publisher: Bentham Science Publishers
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 978-981508065-0; 978-981508066-7
format: Online
language: English
type: Book chapter
coverage: Genish T., KPR College of Arts, Science and Research, Coimbatore, India; Vijayalakshmi S., Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus - The Hub of Analytics, Maharashtra, 412112, India

IoT networks: Integrated learning for privacy-preserving machine learning
description: Financial fraud is a persistent problem for consumers and financial institutions worldwide. It loses billions of dollars annually. Consequently, a strong fraud detection system (FDS) is essential to minimizing damage to financial institutions as well as clients. One common technique for spotting fraud is to use machine learning algorithms, which analyze large volumes of data to help with pattern detection and future prediction. It is difficult for a centralized FDS to detect fraud trends when these problems are coupled. To train a fraud detection model, this work presents a framework for federated learning, a machine-learning environment in which several entities collaborate to solve a machine-learning problem under the guidance of a central server or service provider. Also, the chapter examines how combined learning can be used to protect privacy in machine learning in Internet of Things systems. It focuses on four main calculations: federated averaging (FedAvg), secure aggregation, holomorphic encryption-based federated learning, and differential privacy in combined learning. Extensive experiments were carried out to evaluate these computations in terms of proving accuracy, conserving protection, and computing efficiency. The findings are shown in the results, with FedAvg achieving the highest accuracy of 92.5% and secure conglomeration demonstrating competitive precision levels of 91.8%. Calculations for differential privacy and holomorphic encryption demonstrated strong security conservation with very little data leakage and security parameters of 2.5 and 1.0, respectively. With little communication overhead and the ability to alter accuracy and conserve protection, secure aggregation emerged as a potential configuration. The computational productivity assessments revealed that secure accumulation produced little communication overhead despite its strong security conservation, which makes it suitable for IoT scenarios with limited resources. By using this tactic…
creator: Singh K.; Yadav M.; Singh Y.; Malik P.S.; Siwach V.; Khurana D.; Kumar B.; Yadav R.K.; Elngar A.A.
source: Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications, pp. 250-275.
publisher: CRC Press
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 978-104026669-4; 978-103277164-9
format: Online
language: English
type: Book chapter
coverage: Singh K., Department of Computer Science and Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Haryana, India; Yadav M., Department of Mathematics, University Institute of Sciences, Chandigarh University, Mohali, India; Singh Y., Department of Computer Science and Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Haryana, India; Malik P.S., Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Haryana, India; Siwach V., Computer Science and Engineering, UIET, MDU, Rohtak, India; Khurana D., Department of Computer Science and Engineering, Symbiosis Institute of Technology (SIT), Pune, Maharashtra, India; Kumar B., Department of Chemistry, Guru Jambheshwar University of Science and Technology, Haryana, India; Yadav R.K., Department of Data Science, Christ University, Bengaluru, India; Elngar A.A., Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, Egypt

IoT Security with Blockchain Technology in the Financial Sector
description: Blockchain technology is to create immutable device IDs to stop identity faking whereas Internet of Things (IoT) is used in payment automation and to create smart payment systems. Both are evolving technologies, and their integration offers promising results in financial sector across the globe. Their combination holds a great deal of potential and is going to represent the next generation of smart finance technologies. But the major weaknesses that are present in IoT technologies includes those related to data security, privacy, device authentication, secure communication, and smart contract administration. This chapter focuses on the use of blockchain networks to regulate access and improve security in financial transactions, boost automation and improve efficiency of IoT applications. Blockchain and IoT integration in the financial industry provides a pathway to a more integrated, efficient, and secure financial environment. The chapter discusses the advantages of integrating IoT and Blockchain technologies in the financial sector and the challenges in its applications, and the regulatory mechanism thereof. 2025 Taylor & Francis Group, LLC.
creator: Divyashree K.L.; Mishra A.
source: Internet of Things Vulnerabilities and Recovery Strategies, pp. 166-182.
publisher: CRC Press
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 978-104002562-8; 978-103247331-4
format: Online
language: English
type: Book chapter
coverage: Divyashree K.L., Christ University, Bangalore, India; Mishra A., Christ University, Bangalore, India

IOT Wearable Medical Device for Heart Disease Recognition Based ML and DL: A Classification Approach
subject: deep learning; Heart Disease prediction; IoT; long short-term memory; Supervised learning
description: In the past few years, heart disease has become the foremost worldwide contributor to mortality. This ailment, with a profound effect on the functioning of the heart, leads to issues such as infections in the coronary arteries and diminished blood vessel performance. These complications can culminate in severe unlikely events like heart attacks and strokes. In India alone, approximately one person succumbs to heart disease every minute. To curb the fatalities stemming from cardiac disorders, there is an urgent need for a swift and efficient detection strategy. IoT sensors are utilized in conjunction with Machine Learning (ML) and Deep Learning (DL) techniques to identify heart disease. In this research, we have successfully applied IoT devices and a sensor network to detect heart diseases. This study introduces a medical IoT device designed to gather heart data from patients both before and after the onset of heart disease. This continuously transmitted data is processed using RBF, MLP, and Bi-LSTM models for predicting heart disease. The deep learning approach utilizes past analyses to learn critical features related to heart disease, achieving efficiency in handling complex data. After conducting a series of experiments, we evaluate the systems performance using metrics such as f-measure, sensitivity, specificity, loss function, and Receiver Operating Characteristic (ROC) curves. The HDRBi-LSTM method, in combination with IoT-based analysis, achieves an impressive accuracy rate of 99.5% with minimal time complexity (5 s), effectively reducing heart disease mortality by simplifying the diagnosis of this condition. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
creator: Ajesh, F.; Philip, Felix M.; Jims, Anupama; Alapatt, Bosco Paul
source: Lecture Notes in Networks and Systems;Volume;1243 LNNS;pp.353-363
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23673370; ISBN: 978-303181079-4;
format: online
language: English
type: Conference paper
coverage: Ajesh F., Department of Computer Science and Engineering, Sree Buddha College of Engineering, Kerala, Alappuzha, India; Philip F.M., Department of CS & IT Jain (Deemed-to-be University), Bangalore, India; Jims A., Department of Computer Science and Engineering, CVV Institute of Science and Technology, Kerala, India; Alapatt B.P., CHRIST (Deemed to be University), Delhi-NCR, India

IoT-Based automated dust bins and improved waste optimization techniques for Smart City
description: Effective waste management systems are essential for maintaining sustainability, environmental health, and cleanliness in the age of smart cities. This chapter provides a thorough analysis of the combination of cutting-edge waste minimization techniques with the deployment of an internet of things-based automatic dust bin. The suggested system optimizes garbage collection, lowers operating costs, and has a less environmental effect by combining the creative use of proximity sensors, real-time data analytics, and smart bin technologies. Remote monitoring and administration are made possible by the linked ecosystem that is created by the integration with the internet of things (IoT). In order to further promote environmentally friendly urban life, the study also examines waste-to-energy technology, circular economy ideas, and sustainable waste management techniques. The results provide insightful information for scholars, decision-makers, and urban planners looking for ground-breaking waste management solutions for today's cities. 2024, IGI Global.
creator: Singh K.; Yadav M.; Yadav R.K.
source: Revolutionizing Automated Waste Treatment Systems: IoT and Bioelectronics, pp. 167-194.
publisher: IGI Global
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-836936018-7; 979-836936016-3
format: Online
language: English
type: Book chapter
coverage: Singh K., University Institute of Engineering and Technology, Maharshi Dayanand University, India; Yadav M., University Institute of Sciences, Chandigarh University, India; Yadav R.K., Christ University, India

IOT-BASED cyber security identification model through machine learning technique
subject: Cyber security; Internet of things; Machine learning algorithms; Meta-classifier methods; Repositories; Security
description: 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
creator: Lal B.; Ravichandran S.; Kavin R.; Anil Kumar N.; Bordoloi D.; Ganesh Kumar R.
source: Measurement: Sensors, Vol-27
publisher: Elsevier Ltd
date: 2023-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 26659174
format: Online
language: English
type: Article
coverage: Lal B., Dept. of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist, A.P, India; Ravichandran S., Department of Computer Science and Engineering, School of Technology, GITAM University, Rudraram, Telangana, Hyderabad, 502 329, India; Kavin R., Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Tamil Nadu, Coimbatore, 641008, India; Anil Kumar N., Department of Electronics & Communication Engineering, School of Engineering & Technology, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Andhra Pradesh, Tirupati, India; Bordoloi D., Computer Science and Engineering, Graphic Era Hill University, Dehradun, India, R/S, CSE, Graphic Era Deemed To Be University, Uttarakhand, Dehradun, 248002, India; Ganesh Kumar R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Kengeri Campus, Kumbalgodu, Bangalore, 560074, India

IoT-Based Emergency Vehicle Detection Using YOLOv8
subject: email notifications; emergency vehicles; Internet of Things (IoT); Pi Camera; Raspberry Pi; simple mail transfer protocol (SMTP); You Only Look Once (YOLOv8)
description: 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 developmen…
creator: Suhana, Syed; Prathap, Boppuru Rudra; Narang, Kavish; Anto, Ivin
source: Journal of Automation, Mobile Robotics and Intelligent Systems;Volume;19;Issue;2;pp.79-88
publisher: Sciendo
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 18978649;
format: online
language: English
type: Article
coverage: Suhana S., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), India; Prathap B.R., Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, India; Narang K., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), India; Anto I., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), India

IoT-Based Response Time Analysis of Messages for Smart Autonomous Collision Avoidance System Using Controller Area Network
description: 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.
creator: Biswal A.K.; Singh D.; Pattanayak B.K.; Samanta D.; Banerjee A.; Seteikin A.Y.; Samusev I.G.
source: Wireless Communications and Mobile Computing, Vol-2022
publisher: Hindawi Limited
date: 2022-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 15308669
format: Online
language: English
type: Article
coverage: Biswal A.K., Department of CSE, ITER, SOA Deemed to Be University, India; Singh D., Department of Ca, ITER, SOA Deemed to Be University, India; Pattanayak B.K., Department of CSE, ITER, SOA Deemed to Be University, India; Samanta D., Department of Computer Science, Christ University, India; Banerjee A., Physics Department, Bidhan Chandra College, Asansol, 713 303, India; Seteikin A.Y., Immanuel Kant Baltic Federal, University, Kaliningrad, 236000, Russian Federation, Amur State University, Blagoveshchensk, 675027, Russian Federation; Samusev I.G., Immanuel Kant Baltic Federal, University, Kaliningrad, 236000, Russian Federation, Amur State University, Blagoveshchensk, 675027, Russian Federation

IoT-based smart alert system for drowsy driver detection
description: 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.
creator: Biswal A.K.; Singh D.; Pattanayak B.K.; Samanta D.; Yang M.-H.
source: Wireless Communications and Mobile Computing, Vol-2021
publisher: Hindawi Limited
date: 2021-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 15308669
format: Online
language: English
type: Article
coverage: Biswal A.K., Department of CSE, ITER, SOA Deemed to be University, India; Singh D., Department of CSIT, ITER, SOA Deemed to be University, India; Pattanayak B.K., Department of CSE, ITER, SOA Deemed to be University, India; Samanta D., Department of Computer Science, CHRIST Deemed to be University, India; Yang M.-H., Department of Computer Science and Info. Engineering, National Central University, Taiwan

IoT-based smart healthcare video surveillance system using edge computing
subject: Ambient assistive living; Edge computing; Elderly people fall detection; Healthcare application; Smart video surveillance
description: 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.
creator: Rajavel R.; Ravichandran S.K.; Harimoorthy K.; Nagappan P.; Gobichettipalayam K.R.
source: Journal of Ambient Intelligence and Humanized Computing, Vol-13, No. 6, pp. 3195-3207.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2022-01-01
rights: Restricted Access
relation: ISSN: 18685137
format: Online
language: English
type: Article
coverage: Rajavel R., Department of CSE, Galgotias University, Uttar Pradesh, Greater Noida, India; Ravichandran S.K., Department of CSE, Christ University, Bangalore, India; Harimoorthy K., Department of CSE, Christ University, Bangalore, India; Nagappan P., Department of CSE, Galgotias University, Uttar Pradesh, Greater Noida, India; Gobichettipalayam K.R., Wireless Networks, Springer Asia Ltd, Chennai, India

IoT-Based Smart Indoor Navigation System with Voice Assistance for Museums
subject: Bluetooth low energy; Internet of Things; Navigation system; Smart indoor systems
description: 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.
creator: Manju; Singh S.; Kumar S.
source: Lecture Notes in Networks and Systems, Vol-967 LNNS, pp. 449-457.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981972052-1
format: Online
language: English
type: Conference paper
coverage: Manju, Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, India; Singh S., Department of Computer Science and Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, 560074, India

IoT-based traffic prediction and traffic signal control system for smart city
subject: Improved beetle swarm optimization (IBSO); Intel 80,286 microprocessor; Internet of Things (IoT); Optimized weight elman neural network (OWENN); Smart city
description: 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.
creator: Neelakandan S.; Berlin M.A.; Tripathi S.; Devi V.B.; Bhardwaj I.; Arulkumar N.
source: Soft Computing, Vol-25, No. 18, pp. 12241-12248.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2021-01-01
rights: Restricted Access
relation: ISSN: 14327643
format: Online
language: English
type: Article
coverage: Neelakandan S., Department of IT, Jeppiaar Institute of Technology, Chennai, India; Berlin M.A., Department of CSE, R.M.D Engineering College, Chennai, India; Tripathi S., Department of Computer Science & Engineering, Birla Institute of Applied Sciences, Bhimtal, India; Devi V.B., Department of Information Technology, Sri Sai Ram Institute of Technology, Chennai, India; Bhardwaj I., Galgotias University, Greater Noida, India; Arulkumar N., Department of CS, CHRIST (Deemed To Be University), Banglore, India

IoT-Driven Credit Scoring Models: Improving Loan Decision Making in Banking
subject: Banking analytics; financial inclusion; IoT data in lending; IoT-driven credit scoring; Loan approval optimization; Loan decision-making; Risk assessment
description: 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.
creator: Thirumagal P.G.; Das T.; Das S.; Vinit Sikka C.S.; Amit Kumar C.S.; Kusuma T.
source: 5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 193-197.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835035137-8
format: Online
language: English
type: Conference paper
coverage: Thirumagal P.G., VISTAS, Department of MBA, Tamil Nadu, Chennai, India; Das T., Christ University, Department of School of Business Management, Delhi NCR Campus, UP, Ghaziabad, India; Das S., Christ University, Department of School of Commerce, Finance & Accountancy, UP, Ghaziabad, India; Vinit Sikka C.S., School of Leadership and Management, Manav Rachna International Institute of Research and Studies, Department of UG Management Studies, Haryana, Faridabad, India; Amit Kumar C.S., School of Leadership and Management, Manav Rachna International Institute of Research and Studies, Department of UG Management Studies, Haryana, Faridabad, India; Kusuma T., Sree Vidyanikethan Engineering College, Department of Commerce, Andhra Pradesh, Tirupati, India

IoT-Driven Dynamic Behavior Intervention Model for Sustainable Hygiene Practices: Insights from Household Water Consumption
subject: artificial neural network (ANN); dynamic behavior; ETPB; Internet of Things (IoT)
description: 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.
creator: Srinivasan, R.; Vimala Devi, K.; Praveenraj, David Winster; Venkatasuvrananian, S.; Subramani, K.; Prasanth, V.S.; Gaddala, Babu Rao
source: Communications in Computer and Information Science;Volume;2308 CCIS;pp.136-154
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 18650929; ISBN: 978-303184393-8;
format: online
language: English
type: Conference paper
coverage: Srinivasan R., Department of Mechanical Engineering, Sri Krishna College of Technology, Kovaipudur, Coimbatore, 641042, India; Vimala Devi K., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 631014, India; Praveenraj D.W., CHRIST (Deemed to be) University, Bangalore, India; Venkatasuvrananian S., CSBS, Saranathan College of Engineering, Trichy, India; Subramani K., School of Business and Management, CHRIST (Deemed to be University), Bangalore Yeshwanthpur Campus, Bangalore, 560 073, India; Prasanth V.S., Department of Electronics and Communication Engineering, Madanapalle Institute of Technology and Science, Andhra Pradesh, Madanapalle, 517 325, India; Gaddala B.R., Department of Chemical Engineering, University of Technology and Applied Sciences (Higher College of Technology Muscat), Muscat, Oman

IoT-Enabled Analysis of COVID Data: Unveiling Insights from Temperature, Pulse Rate, and Oxygen Measurements
subject: COVID-19; Health monitoring systems; IoT-enabled analysis; Measurements; Sensors; Temperature-pulse rate-oxygen
description: 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.
creator: John J.; Kumar K.P.; Murthy H.
source: Internet of Things in Bioelectronics: Emerging Technologies and Applications, pp. 297-310.
publisher: wiley
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 978-139424190-3; 978-139424187-3
format: Online
language: English
type: Book chapter
coverage: John J., Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Kumar K.P., Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Murthy H., Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India

IoT-Enabled Smart Breath Analyzer for Real-Time Monitoring of Ammonia, Alcohol, and VOC Biomarkers for Early Disease Detection
subject: Ammonia; Breath Biomarkers; Disease Prediction; Internet of Things; Real-Time Monitoring; Smart Healthcare; VOCs
description: 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.
creator: Kodavathungal Mohammed, Isha Fathima; Mathew, Joswin Joseph; Nagaraju, Shamanth
source: Lecture Notes in Networks and Systems;Volume;1829 LNNS;pp.1-13
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23673370; ISBN: 978-303218140-4;
format: online
language: English
type: Conference paper
coverage: Kodavathungal Mohammed I.F., Department of Information Technology, CHRIST University, Bangalore, India; Mathew J.J., Department of Information Technology, CHRIST University, Bangalore, India; Nagaraju S., Department of Computer Science and Engineering, CHRIST University, Bangalore, India

IoT-Enabled Smart Security Surveillance System for Farmland and Livestock Monitoring Using Computer Vision
subject: Computer Vision; GSM; IoT Scarecrow; Livestock Monitoring; PIR Sensor
description: 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.
creator: Varghese, Nisha; Shereef, Shafi
source: 2025 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2025;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833150236-2;
format: online
language: English
type: Conference paper
coverage: Varghese N., Christ University, Department of Computer Science, Bangalore, India; Shereef S., Jain University, Department of Cs & It, Bangalore, India

IOT-Enabled Supply Chain Management for Increased Efficiency
subject: Artificial intelligence; Big Data; IoT; Supply chain management; Technology; Traditional supply chain
description: 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.
creator: Nanjundan P.; James B.V.; George J.P.; Tiwari A.
source: TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835038427-7
format: Online
language: English
type: Conference paper
coverage: Nanjundan P., Christ (Deemed to Be University), Department of Data Science, India; James B.V., Christ (Deemed to Be University), Department of Commerce, India; George J.P., Christ (Deemed to Be University), Department of Data Science, India; Tiwari A., Christ (Deemed to Be University), Department of Commerce, India