Use Of Internet Of Things Devices Research Articles

LoRa Network-based IoT Node for indoor environment monitoring

The concept of the Internet of Things (IoT), just by its name, presents itself as a network of physical objects, which are incorporated into a vast array of sensors, software, and any other type of technology, with the purpose of connecting and exchanging information with other devices and systems via the Internet. As we know it, the IoT is present in a wide range of devices ranging from simple household objects to complex tools spread across all sectors of industry. The means of communication used in IoT devices is based on Long Range (LoRa) technology. This technology stands out for its ability to operate with low energy consumption, which is vital for the use of IoT devices since they run on batteries. This paper will look at the development of an indoor space monitoring system based on an IoT Node made up of sensors supported by the LoRa network. To achieve the objectives, an in-depth study of the technology used in LoRa networks is carried out, followed by a presentation of the system's architecture and then the various tests carried out in indoor spaces to assess the functioning of both the sensors and the network itself. During the work, numerous tests were carried out over extended periods, which will be duly detailed throughout the document. These are used to ensure the stability of the monitoring system and gauge the environmental status of the space where it is installed.

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

An empirical analysis of evolutionary computing approaches for IoT security assessment

The Internet of Things (IoT) strategy enables physical objects to easily produce, receive, and exchange data. IoT devices are getting more common in our daily lives, with diverse applications ranging from consumer sector to industrial and commercial systems. The rapid expansion and widespread use of IoT devices highlight the critical significance of solid and effective cybersecurity standards across the device development life cycle. Therefore, if vulnerability is exploited directly affects the IoT device and the applications. In this paper we investigated and assessed the various real-world critical IoT attacks/vulnerabilities that have affected IoT deployed in the commercial, industrial and consumer sectors since 2010. Subsequently, we evoke the vulnerabilities or type of attack, exploitation techniques, compromised security factors, intensity of vulnerability and impacts of the expounded real-world attacks/vulnerabilities. We first categorise how each attack affects information security parameters, and then we provide a taxonomy based on the security factors that are affected. Next, we perform a risk assessment of the security parameters that are encountered, using two well-known multi-criteria decision-making (MCDM) techniques namely Fuzzy-Analytic Hierarchy Process (F-AHP) and Fuzzy-Analytic Network Process (F-ANP) to determine the severity of severely impacted information security measures.

Evaluation of an Internet of Things Device-Based Educational Approach to Engage a More Diverse Cybersecurity Workforce

Cybersecurity education heavily utilizes competition-based approaches, such as capture-the-flag (CTF) games to support the need for a skilled cybersecurity workforce. Although CTFs expose students to cybersecurity work competencies, their competitive nature may contribute to the lack of diversity in cybersecurity programs. In response, we developed a technology-based, experiential learning approach utilizing Internet of Things (IoT) devices to educate learners about cybersecurity concepts. We evaluated the approach’s effectiveness in engaging and sparking interest in a diverse sample of high school students. Our results indicated that (a) all participants reported a moderate challenge-skill balance, (b) underrepresented minorities (URMs) reported significantly higher engagement than non-URMs, and (c) significantly more female students compared to male students reported increased levels of intent to pursue cybersecurity after participating in the learning activity. We present the approach, methods, results, implications, and recommendations for the use of IoT devices in cybersecurity education to train a more diverse workforce.

UNDERSTANDING THE IMPACT OF SOCIAL INFLUENCE ON ENGAGEMENT IN IOT SPORTS TECHNOLOGY: A MEDIATION ANALYSIS

The Internet of Things (IoT) is a major revolution that is transforming the world of sports and enabling athletes to improve their physical performance. The use of IoT devices for sports is based primarily on the sharing of athletes' achievements online and the interaction between them, which allows them to motivate and encourage each other to improve their physical performance. Our research analyzes the influence of family and friends (INF_SOC) on engagement (ENGAG) in the ongoing and long-term use of these IoT devices and studies the mediating effect of perceived control (CP), perceived provider credibility (CRD), and trust (CONF) in this relationship. The population is the athletes who use IoT to measure their performance. Convenience sampling was deployed to collect the data using a questionnaire. 313 respondents participated in this study. To analyze our theoretical model, we used the method of partial least squares under SmartPls. The results confirmed all the theoretical hypotheses introduced and validated the mediating effects. They allowed us to generate interesting recommendations for IoT providers and users to reinforce engagement in the use of the Internet of Things for sports.

Deep Reinforcement Learning Framework with Q Learning For Optimal Scheduling in Cloud Computing

Cloud computing is an emerging technology that is increasingly being appreciated for its diverse uses, encompassing data processing, The Internet of Things (IoT) and the storing of data. The continuous growth in the number of cloud users and the widespread use of IoT devices have resulted in a significant increase in the volume of data being generated by these users and the integration of IoT devices with cloud platforms. The process of managing data stored in the cloud has become more challenging to complete. There are numerous significant challenges that must be overcome in the process of migrating all data to cloud-hosted data centers. High bandwidth consumption, longer wait times, greater costs, and greater energy consumption are only some of the difficulties that must be overcome. Cloud computing, as a result, is able to allot resources in line with the specific actions made by users, which is a result of the conclusion that was mentioned earlier. This phenomenon can be attributed to the provision of a superior Quality of Service (QoS) to clients or users, with an optimal response time. Additionally, adherence to the established Service Level Agreement further contributes to this outcome. Due to this circumstance, it is of utmost need to effectively use the computational resources at hand, hence requiring the formulation of an optimal approach for task scheduling. The goal of this proposed study is to find ways to allocate and schedule cloud-based virtual machines (VMs) and tasks in such a way as to reduce completion times and associated costs. This study presents a new method of scheduling that makes use of Q-Learning to optimize the utilization of resources.The algorithm's primary goals include optimizing its objective function, building the ideal network, and utilizing experience replay techniques.

Analysis of Smart Parking System Using IOT Environment

The typical parking experience has been transformed by smart parking systems that use the Internet of Things (IoT) environment to integrate technology to improve efficiency, convenience, and sustainability. In order to monitor and manage parking spaces in real-time, this unique technique makes use of IoT devices, such as sensors, cameras, and networking technologies. As a result of the system's reliable information on parking availability, drivers may find and book parking spaces in advance, which eases traffic and reduces aggravation. Additionally, parking systems with IoT capabilities optimize resource use, lowering carbon emissions and fostering sustainability. The adoption of IoT in parking systems is a crucial step towards building smarter, more connected cities that will enhance both drivers' and parking operators' experiences with parking. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. Taken as Alternative parameters is Park Smart, Street line, Park Whiz, ParkMobile, Spot Hero. Taken as evaluation parameters is Light Sensor, CCTV coins, SMS, Cost-effectiveness, Timestamp. This demonstrates the rank of the data set Park Smart is on 1st Rank, ParkMobile is on 2nd Rank, Park Whiz is on 3rd Rank, Street line is on 4th Rank and Spot Hero is on 5th Rank. To sum up, implementing a smart parking system employing IoT technology has shown to be a potential way to deal with the problems associated with urban parking. The system increases parking efficiency, lessens traffic congestion, and enhances user experience by utilising IoT sensors, data analytics, and real-time communication. The parking scene in smart cities has the potential to change dramatically, enhancing ease and sustainability.

Exploring the Confluence of IoT and Metaverse: Future Opportunities and Challenges

The Internet of Things (IoT) and the metaverse are two rapidly evolving technologies that have the potential to shape the future of our digital world. IoT refers to the network of physical devices, vehicles, buildings, and other objects that are connected to the internet and capable of collecting and sharing data. The metaverse, on the other hand, is a virtual world where users can interact with each other and digital objects in real time. In this research paper, we aim to explore the intersection of the IoT and metaverse and the opportunities and challenges that arise from their convergence. We will examine how IoT devices can be integrated into the metaverse to create new and immersive experiences for users. We will also analyse the potential use cases and applications of this technology in various industries such as healthcare, education, and entertainment. Additionally, we will discuss the privacy, security, and ethical concerns that arise from the use of IoT devices in the metaverse. A survey is conducted through a combination of a literature review and a case study analysis. This review will provide insights into the potential impact of IoT and metaverse on society and inform the development of future technologies in this field.

An improved firefly algorithm for the rank generation to optimize the route discovery process in IoV

<p>Vehicular ad hoc networks (VANET) have been the attention gainer for the last couple of years due to increasing number of vehicles on the road. Incorporation of VANET with Internet of Things (IoT) has created large number possibilities in terms of power efficiency and secure transmission. The article focuses on the ad-hoc on-demand distance vector (AODV) protocol and its applications in route discovery in VANETs. In this work, the swarm intelligence (SI) inspired modified firefly algorithm has been employed for rank generation of the nodes. It is concluded that the use of IoT devices and advanced routing protocols with SI algorithms can lead to efficient and low-latency route discovery in VANETs using quality of service (QoS) parameters. The experimental analysis shown that the proposed technique has been outperformed the other existing technique in terms of QoS parameters and provides the optimal route discovery mechanism with high throughput and minimum latency.</p>

Automatic Irrigation and Worm Detection in Crop using Raspberry PI with OpenCV

The Internet of Things (IoT) is a system of networked devices that can talk to one another and function on their own. Agriculture provides a wealth of indications for data analysis that help produce improved agricultural yields. Intelligent farming enhances information and communication thanks to the use of IoT devices in agriculture. For the best crop growth, it is important to consider a variety of parameters, including soil types, soil moisture, mineral nutrients, temperature, light, and oxygen. Now, a range of sensors can collect these parameters and transmit them to the cloud. A few of these criteria are taken into account in this study's data analysis in order to advise to the consumers better agricultural decisions using IoT. The Internet of Things (IoT) is a system of networked devices that can talk to one another and function on their own. Agriculture provides a wealth of indications for data analysis that help produce improved agricultural yields. Intelligent farming enhances information and communication thanks to the use of IoT devices in agriculture. For the best crop growth, it is important to consider a variety of parameters, including soil types, soil moisture, mineral nutrients, temperature, light, and oxygen. Now, a range of sensors can collect these parameters and transmit them to the cloud. A few of these criteria are taken into account in this study's data analysis in order to advise to the consumers better agricultural decisions using IoT.

Exploring the Dark Side of IoT: A Survey on Blackhole Attacks

The Internet of Things (IoT) is rapidly growing and becoming an integral part of our daily lives. However, the increasing use of IoT devices also raises significant security concerns. One of the most pressing threats to IoT security is the blackhole attack, where an attacker can selectively drop or discard packets to disrupt communication between IoT devices. In this paper, we conduct a comprehensive survey on blackhole attacks in IoT networks. We explore the types of blackhole attacks, the methods attackers use to exploit vulnerabilities in IoT devices, and the potential impact of these attacks. We also review existing solutions and strategies for mitigating the effects of blackhole attacks in IoT networks. Through our survey, we provide a deeper understanding of the blackhole attack's nature and the potential implications for the security and reliability of IoT networks. Ultimately, our findings highlight the need for increased awareness of this type of attack and the implementation of robust security measures to protect IoT devices and networks.

Stakeholder analysis for designing an urban air quality data governance ecosystem in smart cities

Cities, the world over, are fuelling economic growth. At the same time, rapid urbanization is a root cause of serious environmental damage. Recent WHO global air pollution guidelines highlight air pollution as a critical environmental threat along with climate change. To address these threats, smart cities and clean air programs are on a rise. In smart cities, data and Information and Communication Technologies (ICT) are major drivers of city transformations. The 4th Industrial Revolution (4IR) technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cloud computing have the potential to accelerate these transformations toward urban resilience. However, the success of smart cities and clean air programs depends on cohesive multi-sector stakeholder contributions. This study conducted interdisciplinary participative stakeholder analysis to understand the data, and sectorial challenges, to outline the technological opportunities to facilitate clean air programs in Indian smart cities. The research highlights gaps due to siloed stakeholder operations, lack of data calibration, non-alignment of smart city and air quality management services, non-availability of health exposure data, and difficulty in translating scientific data into implementable actions. Stakeholders expressed potential ‘fit for the purpose’ use of IoT devices, satellites, smartphones, and mobility data augmented by AI methods in bridging these gaps. The analysis points toward a need to develop an easily accessible and ubiquitous urban data governance ecosystem enabling seamless cross-sector data exchanges to build trusting relationships among the stakeholders across the air quality management value chain

IoT Malware Analysis Using Federated Learning: A Comprehensive Survey

The Internet of Things (IoT) has paved the way to a highly connected society where all things are interconnected and exchanging information has become more accessible through the internet. With the use of IoT devices, the threat of malware has increased rapidly. The increased number of existing and new malware variants has made protecting IoT devices and networks challenging. The malware can hide in the systems and disables its activity when there are attempts to discover and detect them. With technological advances, there are various emerging techniques to address this problem. However, they still encounter issues concerning the privacy and security of the user’s data and suffer from a single point of failure. To address this issue, there are recent research developments conducted to use Federated Learning (FL). FL is a decentralized technique that trains the user’s data on-device and exchanges the parameters without sharing the user’s data. FL is implemented to secure the user’s data, provide safe and accurate models, and prevent the single point of failure in the centralized models. This paper provides an overview of different approaches that integrate FL with IoT. Finally, we discuss the applications of FL, the research challenges, and future research directions.

Bezbjednost u sajber fizičkim i IoT sistemima

The security of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) is a significant issue due to the increasing integration of these systems into critical infrastructure, production and other industrial environments, as well as the growing use of IoT devices in various areas of life. It is therefore important to consider the security challenges in these systems and work on their resolution in order to maintain the stability and reliability of these systems. This paper discusses the security of CPS and IoT systems, including the architecture of such systems, security requirements and standards, as well as specific security issues that arise in both types of systems. In particular, CPS security is considered through the physical interface, control system, availability and time constraints, while IoT security is considered through trust management, secure routing protocols, integration of heterogeneous networks and privacy protection.

A Review of Internet of Things (IoT) and Security Concerns

Wireless technology networks are particularly vulnerable to security attacks. Wireless communication networks are widely used in education, defense, industry, healthcare, retail, and transportation. These systems rely on wired and cellular networks. In society and industry, wireless sensor networks, actuation networks, and vehicle networks have garnered a lot of attention. The Internet of Things has gotten a lot of research attention in recent years. The Internet of Things is regarded as the internet's future. IoT will play an important role in the future, changing our lifestyles, standards, and business structures. The use of IoT in various applications is likely to skyrocket in the next years. The Internet of Things enables billions of devices, people, and services to communicate and exchange information. IoT networks are vulnerable to several security vulnerabilities as a result of the rising use of IoT devices. It is crucial to implement effective privacy and security protocols in IoT networks to guarantee, among other things, confidentiality, authentication, access control, and integrity. This paper provides a thorough analysis of the security and privacy challenges in IoT networks. Keywords: Internet of Things, Security, Security Concerns, Attacks, Networks, Internet

Connecting food supply chains

Connecting food supply chains

Data Analytics and Modeling in IoT-Fog Environment for Resourceconstrained IoT-Applications: A Review

Introduction: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) in recent years has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously is putting tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. This will help in optimizing the data at the device, edge/fog level for better performance of the network in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

Cross Deep Learning Method for Effectively Detecting the Propagation of IoT Botnet.

In recent times, organisations in a variety of businesses, such as healthcare, education, and others, have been using the Internet of Things (IoT) to produce more competent and improved services. The widespread use of IoT devices makes our lives easier. On the other hand, the IoT devices that we use suffer vulnerabilities that may impact our lives. These unsafe devices accelerate and ease cybersecurity attacks, specifically when using a botnet. Moreover, restrictions on IoT device resources, such as limitations in power consumption and the central processing unit and memory, intensify this issue because they limit the security techniques that can be used to protect IoT devices. Fortunately, botnets go through different stages before they can start attacks, and they can be detected in the early stage. This research paper proposes a framework focusing on detecting an IoT botnet in the early stage. An empirical experiment was conducted to investigate the behaviour of the early stage of the botnet, and then a baseline machine learning model was implemented for early detection. Furthermore, the authors developed an effective detection method, namely, Cross CNN_LSTM, to detect the IoT botnet based on using fusion deep learning models of a convolutional neural network (CNN) and long short-term memory (LSTM). According to the conducted experiments, the results show that the suggested model is accurate and outperforms some of the state-of-the-art methods, and it achieves 99.7 accuracy. Finally, the authors developed a kill chain model to prevent IoT botnet attacks in the early stage.

An Intelligent Approach for Preserving the Privacy and Security of a Smart Home Based on IoT Using LogitBoost Techniques

Development and use of IoT devices have grown significantly in recent years. Many departments such as smart homes, smart healthcare, smart sports analysis, and different smart industries use IoT-based devices. In IoT devices, traffic is a very important part. IoT device traffic is distinct from traditional device traffic in various respects. In this study, 41 Internet-of-Things (IoT) devices were used. IoT devices provided 13 network traffic attributes to construct a multiclass classification model. Pre-processing techniques such as Normalization and Scaling of Dataset were used to pre-process the raw data acquired. Features can be extracted from text data using feature engineering algorithms. After stratification, the dataset contains 117,423 feature vectors utilized to develop the classification model further. Multiple performance metrics were used to demonstrate how well LogitBoost algorithms perform in this research. Using ensemble-based hybrid machine learning models to detect network anomalies in this research is an early step in developing an intrusion detection system (IDS). The main objective of this study is to detect attacks and anomalies in an IoT environment in a smart home. We have proposed a novel approach to developing LogitBoost algorithms, i.e., Logi-XGB, Logi-GBC, Logi-ABC, Logi-CBC, Logi-LGBM, and Logi-HGBC. After applying LogitBoost algorithms to the dataset for the classification, Logi-XGB scored 80.20% accuracy, and Logi-GBC scored 77.80% accuracy. Logi-ABC scored 80.33% accuracy. Logi-CBC scored the highest accuracy of 85.66%. Logi-LGBM and Logi-HGBC scored the same accuracy of 81.37%. Compared with previous LogitBoost algorithms implemented in previous studies, our proposed Logi-CBC has scored the highest accuracy on the given dataset.

An Overview of Vulnerabilities in Smart Farming Systems

Novel methods of food production are required to feed an ever-growing world population. The emergence of Internet of Things (IoT) technology has had an impact on a wide variety of industries. The use of IoT devices in agriculture, known as smart farming, is a potential solution to the growing food crisis. This technology has been shown to greatly increase farm yield while simultaneously reducing the number of farm-related injuries in agricultural workers. However, a major drawback of IoT systems is their vulnerability to cyberattacks. Man in the Middle attacks, Denial of Service attacks, and Phishing attacks among others have all been shown to be effective avenues to attack IoT systems. This paper will provide an overview of smart farming, IoT devices used on smart farms, and potential vulnerabilities present in these systems. In addition, it will also provide mitigation techniques to prevent cyberattacks on smart farms. More targeted research and penetration testing is needed to identify approaches to improving the cybersecurity of smart farming and associated technologies.