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IoT Framework, Architecture Services, Platforms, and Reference Models
Internet of things (IoT) is spawning a twirl in the world of connected devices by aiding the devices to connect, compute, and coordinate with each other. While the concept of IoT is still embryonic, its outcomes are trailblazing. IoT acts as a facilitator in creating a smart world by connecting devices through sensors and actuators to the Internet. The acceptance of IoT in various sectors indicates that the partakers in an IoT ecology are diverse. This demands common functionalities, interoperability standards, and network protocols across sectors. But there exists an extremity of incongruency in devices, capabilities, and network protocols, and therefore it is imperative to have a complete reference architecture model that necessitates the existing diversities and defines a new monody for the IoT environment. The lack of standard and uniform architectural knowledge, frameworks, and platforms is presently resisting the researchers to reap the benefits that the Internet of things (IoT) offers. This chapter summarizes various Internet of things frameworks, architectures, platforms, and reference models and thereby paves way for businesses to build IoT on it. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
IoT in schools: Revolutionizing education through smart technology
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. -
IoT Infrastructure to Energize Electromobility
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. -
IoT innovation in COVID-19 crisis
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. -
IoT networks: Integrated learning for privacy-preserving machine learning
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, financial institutions may avoid sharing datasets and benefit from a shared model that has seen more fraud than any one bank has on its own. Thus, the sensitive data of the user is protected. The results of the chapter indicate that the federated model (federated averaging) may be as good as or better than the central model (multi-layer perceptron) in detecting financial fraud. This chapter adds to the growing conversation around mixed learning in the Internet of Things by providing insights into the trade-offs between accuracy, security, and efficacy and by laying the groundwork for future developments in privacy-preserving machine learning standards. 2025 selection and editorial matter, Ahmed A. Elngar, Diego Oliva and Valentina E. Balas. All rights reserved. -
IoT Security with Blockchain Technology in the Financial Sector
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. -
IoT-Based automated dust bins and improved waste optimization techniques for Smart City
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. -
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 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-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 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-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.