Characteristics Of Internet Of Things Research Articles

Smart Home Powered by Solar: IoT-based SEPIC Converter Control

Background: In this article, an IOT solution for managing and controlling a PV system with applications for the home is presented. A DC-DC SEPIC converter, a bidirectional converter, a PWM generator, and a single-phase voltage source inverter with active clamping are used for power conditioning. An output voltage control loop is implemented, and real-time online communication with an internet server is accomplished using the PIC microcontroller. Methods: The ESP8266MOD WIFI IOT module works with the Blynk mobile app. Experimental findings showing the proper operation of IOT features demonstrate the effectiveness of voltage regulation. In this article, frequency is taken into consideration as solar power is integrated with the grid. A user may easily control the DC-DC converter, load batteries, and monitor battery voltage through our userfriendly GUI interface. Results: In this paper, an IOT (Internet of Things) application was used to control and track the output of a modest PV power plant. The study shows the efficiency of the digitally controlled system developed to regulate the output voltage of the SEPIC DC-DC converter. Practical Significance: They further argue that the ESP8266 and microcontroller's versatility make them an appropriate instrument for remote and real-time monitoring and control of the proposed model from around the world. The energy that is produced by the sun is added to the grid so that it may be put to effective use and contribute to satisfying the rising demand for energy. Solar energy is the input that SEPIC uses to charge the batteries and for all of its other processes. In the event that solar photovoltaic (PV) panels are not available, the SEPIC DC-DC converter will derive its energy from AC power. Conclusion: In this article, an IOT (Internet of Things) application was used to control and track the output of a modest PV power plant. The research demonstrates the effectiveness of the digitally controlled system created to manage the output voltage of the SEPIC DC-DC converter. They further argue that the ESP8266 and microcontroller's versatility make them an appropriate instrument for allowing remote and real-time monitoring and control of the proposed model from any part of the world. The energy that is produced by the sun is added to the grid so that it may be put to effective use and contribute to satisfying the rising demand for energy. Solar energy is the input that SEPIC uses to charge the batteries and for all of its other processes. In the event that solar photovoltaic (PV) panels are not available, the SEPIC DC-DC converter will derive its energy from AC power.

Efficient Cyber Security Framework for IoT Using Machine Learning Algorithms

Through the network's infrastructure, the IoT can impart perception, recognition, and remote control to inanimate objects. Due to IoT's characteristics, it's possible to integrate the real world into a digital system, which improves precision, productivity, and bottom line. While traditional internet infrastructure comprises of highly capable computers and servers, IoT gadgets have limited processing power and storage space. The authentication and key agreement system SecureAuthKey is very lightweight. The suggested technique is meant to solve the security and privacy problems that plague modern constraint-based CPS programmes. A lightweight approach for authenticating cyber-physical objects is one of the expected outcomes. Adaptation and autonomy in cyber-physical systems need not be compromised by a lack of trust or security in users' data or the devices themselves, according to a new type of security algorithm. To provide flexibility and scalability, a new middleware module has been developed on the Raspberry platform to facilitate communication between CPS-based devices and services. Secure Internet of Things (IoT) evaluation methodology that may be used in a variety of contexts and is user-focused. With the suggested system, the two CPS units will be able to authenticate one another. When a network grows larger, the possibility of an attack rises. Therefore, the IoT network is much more susceptible to attacks than conventional networks. As the number of connected devices grows, so do the number of potential threats to their security. To protect the IoT ecosystem from current threats, cutting-edge technology is essential. The proposed approach must be very efficient and have low computational overheads. It creates random session keys to protect wireless transmissions. The system is protected from a variety of cyber threats. The current constraint-based CPS system requires a new lightweight security solution.

Blockchain for Enhancing IoT Privacy and Security

The technological advancements in the network technologies that deal with interconnected computing devices has led to the dramatic increase in the number of Internet of Things (IoT) devices. It promises to improve the convenience of our lives by leveraging to modernize the way day to day items are connected to various elements of existence by converting it to smart object. However, with the unprecedented convenience, accessibility, and efficiency, IoT has caused acute security and privacy threats in recent years, and it must be addressed properly. Due to the massive scale and distributed nature of IoT networks, overlooking these privacy and security issues will negatively impact many elements of our existence that involves our surroundings, and these effects will influence our own masses too. The national or jurisdictional boundaries are not restricted to the centralized cloud infrastructure that provides interconnectivity of IoT items, hence maintaining robustness, authentication and security becomes crucial. Recently, the development of blockchain technology is a plausible solution to offer such features. Security, authentication, and maintenance issues can be solved by the secure decentralization of blockchain and overcome the current IoT ecosystem's drawbacks. This paper highlights the IoT features and presents a IoT security and privacy problems are examined and classified in accordance with the IoT layered architecture. Further, we explore the IoT security and privacy challenges and examine blockchain technology towards a solution that help solve many of the IoT privacy and security issues

The impacts of the internet of things and artificial intelligence on logistics in supply chain management

The transportation and management of goods industry, along with the operations of many related companies, could be significantly transformed by two fast-developing technologies: the internet of things (IoT) and artificial intelligence (AI). In more detail, for overseeing the movement and storage of goods in this sector, various strategies and structures have been suggested in academic writings. However, these suggestions often overlook how IoT and AI can be combined. To address this oversight, the present study introduces a new approach named IoT-AI-SCM, created using design science. This approach brings together the mentioned technologies within the management of supply chains. The method applied in this study is known as the design science method. The approach we developed is outlined in five key steps: 1) utilizing sensors and devices enabled by IoT, 2) gathering and connecting data, 3) storing and processing data using cloud technology, 4) applying AI, 5) analyzing and predicting outcomes, and 6) smart planning and improvement. By implementing this proposed approach, a company can reimagine its supply chain and logistics management using the essential features of IoT and AI.

A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices

The inter-class classification and source identification of IoT devices has been studied by several researchers recently due to the vast amount of available IoT devices and the huge amount of data these IoT devices generate almost every minute. As such there is every need to identify the source where the IoT data is generated and also separate an IoT device from the other using on the data they generate. This paper proposes a novel additive IoT features with the CNN system for the purpose of IoT source identification and classification. Experimental results shows that indeed the proposed method is very effective achieving an overall classification and source identification accuracy of 99.67 %. This result has a practical application to forensics purposes due to the fact that accurately identifying and classifying the source of an IoT device via the generated data can link organisations/persons to the activities they perform over the network. As such ensuring accountability and responsibility by IoT device users.

Advancing device and network security for enhanced privacy

The rapid proliferation of IoT (Internet of Things) devices has ushered in an era of unprecedented connectivity and automation. The widespread adoption has also exposed vulnerabilities, necessitating robust security and privacy measures. This research presents a comprehensive study focused on enhancing IoT device and network security and privacy through empirical investigation and advanced machine learning techniques. Commencing with an exhaustive literature review, it was assessed, the evolving landscape of IoT security threats, solutions, and identified research gaps. Building upon the foundation, it was designed and rigorously evaluated a machine learning-based classification model tailored for IoT device security. Utilizing a meticulously crafted simulated dataset mirroring real-world IoT features, our model undergoes comprehensive performance evaluations. Metrics include accuracy, precision, recall, F1 score, and ROC analysis. Our findings reveal a nuanced performance profile, shedding light on the model's capability to accurately classify IoT devices as Secure or Vulnerable. Precision-recall trade-offs, emphasizing the need for a judicious balance to mitigate false positives and false negatives was investigated. The critical role of feature engineering and model refinement, pointing to areas for future research and optimization. This research contributes to the burgeoning field of IoT security by employing machine learning as a proactive tool for fortifying IoT device and network security. Our findings advocate for a strategic approach to secure IoT ecosystems, ensuring data integrity and privacy in the face of evolving threats. As IoT devices continue to proliferate across industries, this research serves as a foundation for innovative strategies and ongoing investigations to harness the full potential of secure IoT environments while addressing multifaceted challenges in the ever-evolving IoT landscape.

MIAS: An IoT based Multiphase Identity Authentication Server for Enabling Secure Communication

Internet of Things (IoT) provides an imminent floor to exchange information and enhance communication technology using embedded devices that are too sophisticated daily and deployed in diverse fields of life. The colossal volume of sensors employed within IoT application leads to disperse an enormous amount of data in the open air. The rudimentary facet of IoT enabled technologies having the pitfall, security. It is a practical problem in terms of networking, application and communication. Since the resource constraint feature of IoT enabled technology, both security and efficiency can be balanced. The effective algorithm builds over Elliptic Curve Cryptography (ECC) provides an effective solution due to its small key size. The sensor node in this scheme is incorporated with TPM to protect the system in crucial IoT applications. In this proposed work, a three-way authentication factor with key agreement protocol proposed with consecutive phases such as registration, login, authentication, and access control. The security assessment of the proposed scheme with diverse attacks are elaborated and the computation overhead compared with related schemes.

Internet of things-enabled smart controller for polymer dispersed liquid crystals films

<p><span lang="EN-US">The evolvement of smart glass technology has gained a lot of interest through its energy-saving potential as one of the heating, ventilating, and air-conditioning (HVAC) system. This paper focuses on polymer dispersed liquid crystal (PDLC) film, a smart glazing film that changes its opacity in response to an electrical impulse. The power consumption of the smart film is considerably small. However, improper handling of the smart film such as not turning off the film after usage can lead to energy wastage. Hence, connecting the smart film to an internet of things (IoT) controller would be one of the possible solutions to ensure that the film is maintained properly. The objective of the work here is to develop a smart, low cost and efficient IoT-enabled smart controller for PDLC films with energy-saving features. In pursuance of materializing this concept, this paper delineates the design of a smart controller for the PDLC films. The implementation of the IoT features, NodeMCU, and environmental sensors enabled the smart film to be capable of switching automatically. In addition, voice-command features were also incorporated into the controller. With the successful development of the IoT smart controller, the PDLC films can operate autonomously and wirelessly.</span></p>

Artificial Intelligence-Based Approach on Cybersecurity Challenges and Opportunities in The Internet of Things & Edge Computing Devices

The Internet of Things (IoT) presents a wide range of issues and challenges. Security is a major concern for IoT technologies, applications, and networks. IoT's research progress is discussed in this paper, which focuses on this primary feature of IoT and describes various security issues and concerns. The Internet of Things and the concept of edge computing have enabled many new IoT applications, including smart homes, intelligent transportation, pioneering health, smart grids, and smart energy. It also introduces a slew of unanticipated challenges to data security. Cybersecurity, edge computing, the Internet of Things, and artificial intelligence all present exciting new research and development prospects. There are many new threats and opportunities and this paper will focus on them.

The Impact of Mobility on Physical Layer Security of 5G IoT Networks

Internet of Things (IoT) is rapidly spreading and reaching a multitude of different domains, since the fifth generation (5G) wireless technologies are the key enablers of many IoT applications. It is hence apparent that the broadcast nature of IoT devices makes data security unprecedentedly critical. Compared with traditional cryptography algorithms, which cannot cater for the features of IoT devices characterized by the severe limits in terms of energy, computation and storage capabilities, physical layer security (PLS) has been regarded as a promising solution to facilitate secure communications by exploiting the intrinsic randomness of the wireless medium. However, most of previous works assumed that all devices are static, and the impact of mobility on PLS deserves further investigation. In this paper, applying two types of random mobile models, i.e., the models of Random WayPoint (RWP) and Random Direction (RD), we study the impact of mobility on PLS in a scenario with three types of wireless devices (i.e., a destination, multiple interferers and an eavesdropper). Specifically, we establish an analytical framework for secrecy transmission capacity (STC), a fundamental metric in the study of PLS, under RWP and RD models. To the best of our knowledge, this is the first paper to derive STC and present the condition to achieve a positive STC with the consideration of mobility. We conclude that the RWP mobile destination can achieve a higher STC than that achievable in RD mobile and static scenarios, while RWP mobile eavesdropper is a challenging scenario to obtain a positive STC. Therefore, we propose an effective secrecy improvement strategy for the latter. Simulation validates the theoretical analyses.

An Intelligent Task Scheduling Model for Hybrid Internet of Things and Cloud Environment for Big Data Applications

One of the most significant issues in Internet of Things (IoT) cloud computing is scheduling tasks. Recent developments in IoT-based technologies have led to a meteoric rise in the demand for cloud storage. In order to load the IoT services onto cloud resources efficiently even while satisfying the requirements of the applications, sophisticated planning methodologies are required. This is important because several processes must be well prepared on different virtual machines to maximize resource usage and minimize waiting times. Different IoT application tasks can be difficult to schedule in a cloud-based computing architecture due to the heterogeneous features of IoT. With the rise in IoT sensors and the need to access information quickly and reliably, fog cloud computing is proposed for the integration of fog and cloud networks to meet these demands. One of the most important necessities in a fog cloud setting is efficient task scheduling, as this can help to lessen the time it takes for data to be processed and improve QoS (quality of service). The overall processing time of IoT programs should be kept as short as possible by effectively planning and managing their workloads, taking into account limitations such as task scheduling. Finding the ideal approach is challenging, especially for big data systems, because task scheduling is a complex issue. This research provides a Deep Learning Algorithm for Big data Task Scheduling System (DLA-BDTSS) for the Internet of Things (IoT) and cloud computing applications. When it comes to reducing energy costs and end-to-end delay, an optimized scheduling model based on deep learning is used to analyze and process various tasks. The method employs a multi-objective strategy to shorten the makespan and maximize resource consumption. A regional exploration search technique improves the optimization algorithm’s capacity to exploit data and avoid becoming stuck in local optimization. DLA-BDTSS was compared to other well-known task allocation methods in accurate trace information and the CloudSim tools. The investigation showed that DLA-BDTSS performed better than other well-known algorithms. It converged faster than different approaches, making it beneficial for big data task scheduling scenarios, and it obtained an 8.43 percent improvement in the outcomes. DLA-BDTSS obtained an 8.43% improvement in the outcomes with an execution time of 34 s and fitness value evaluation of 76.8%.

Anomaly-based intrusion detection system in the Internet of Things using a convolutional neural network and multi-objective enhanced Capuchin Search Algorithm

Nowadays, the growth and pervasiveness of Internet of Things (IoT) devices have led to increased attacks by hackers and attackers. On the other hand, using IoT infrastructure in various fields has increased the number of node security breaches, attacks, and anomalies. Therefore, detecting anomalies in IoT devices is vital to reduce attacks and strengthen security. Over the past few years, various research has been conducted in anomaly-based intrusion detection using machine learning and deep learning methods. The biggest challenge in machine learning methods is the inability to extract new features. To do this, researchers use deep learning methods to extract new features that lead to increased accuracy in intrusion detection. There are important unsolved challenges in research, including determining important features in detecting malicious attacks, extracting features from raw network traffic data using deep networks, and insufficient accuracy in detecting attacks against IoT devices. Convolutional neural networks are considered a powerful and reliable method in this field due to the ability to automatically extract features from data and perform faster calculations. This study has designed and implemented the IoT features extraction convolutional neural network called IoTFECNN with hybrid layers for better anomaly detection in the IoT. Moreover, a binary multi-objective enhanced Capuchin Search Algorithm (CSA) called BMECapSA is developed for efficient feature selection. The combination of the IoTFECNN and BMECapSA methods has led to the introduction of a new hybrid method called CNN-BMECapSA-RF. Finally, the proposed method is implemented and tested on two data sets, NSL-KDD and TON-IoT. The results of various experiments exhibit that the proposed method has better results regarding classification criteria compared to existing deep learning and machine learning-based anomaly detection systems. The proposed method has reached 99.99% and 99.85% accuracy by identifying 27% and 44% of the effective features on the TON-IoT and NSL-KDD datasets, respectively.

THE USE OF INTERNET-BASED TECHNOLOGIES IN DIETARY AND PHYSICAL ACTIVITY INTERVENTION FOR PATIENTS WITH HYPERTENSION: A SYSTEMATIC REVIEW

The Internet of Things (IoT) is widely used in Internet-based technology (IBT) interventions for hypertension management. However, little is known about the impact of IBT and how the IoT is used to manage hypertension. This article analysed the types of IBT, the features of IoT and the impact of IBT. This review included 14 articles selected from the following databases: Scopus, Web of Science, PubMed, ScienceDirect and Google Scholar. Randomised controlled trials (RCT) and quasi-experimental studies published from 2014 to 2021 in English focused on managing dietary intake and physical activity (PA) via IBT for hypertensive adults were included. The IoT features, Mobile IoT and Wi-Fi communication protocols were covered by three articles while six digital platforms (smartphone applications, email, WeChat, url, web page and online multimedia) were utilised for the non-pharmacological management. The current review identified the important features of IoT and how implementing IBT in hypertension management can effectively improve blood pressure and bring about behavioural and psychological changes. Finally, this study highlighted only limited IoT systems that had been integrated into IBT, thus, warranting the need for further studies in the future.

BCmaster: A Compatible Framework for Comprehensively Analyzing and Monitoring Blockchain Systems in IoT

With the ever-increasing applications of the Internet of Things (IoT), e.g., smart homes, smart cities, smart factories, etc., data security and device trustworthiness become the major concerns. Although blockchain contributes to achieve the data traceability and fault tolerance, the huge resource consumption and limited performance severely restrict its deployments in IoT. Moreover, the unique features of IoT, such as mobility, resource constraints, and security vulnerabilities, create even greater difficulties for blockchain running. Observing the lack of blockchain analyzing tools for IoT, we intend to provide a fair means with standard metrics for better understanding IoT-oriented blockchain. In this article, we present BCmaster, a blockchain analyzing and monitoring framework focusing on IoT scenarios. Based on the detailed modeling of blockchain-assisted IoT, we propose a novel metric set named 5-D quantitative metric framework, which can conduct the comprehensive blockchain analysis from five dimensions. Moreover, we design a modular architecture for BCmaster, wherein the interaction requests (IRs)-based data parser ensures a high system compatibility and the synchronous metric visualizer facilitates the real-time blockchain monitoring in IoT. Extensive evaluations in a real IoT environment demonstrate the validity of BCmaster and explore the performance of four IoT-oriented blockchain systems. Last but not least, we discuss the ways to customize IoT-oriented blockchain with the help of BCmaster.

Integrating Teachers’ TPACK Levels and Students’ Learning Motivation, Technology Innovativeness, and Optimism in an IoT Acceptance Model

The growing use of the Internet of Things (IoT) around the world has encouraged researchers to investigate how and why the IoT is implemented in colleges and universities. Previous studies have focused on individual attitudes rather than the integration of attitudes from two different perspectives. Furthermore, other studies have investigated the use of the IoT in non-educational settings, ignoring the effect of the IoT related to the technology acceptance model (TAM) and technological pedagogical content knowledge (TPACK) model. The present work aims to address this research gap by determining the main factors that influence acceptance of the IoT, leading to increased awareness in collaborative learning, where technology forms the core tool in enhancing the use of the IoT. A questionnaire was used to collect data from teachers and students from colleges and universities in Oman and the United Arab Emirates (UAE). The data were analyzed through the structural equation modeling (SEM) method. The findings indicated that there are two levels of positive effects on the intention to use IoT. The first level is technology features, which are represented by technology optimism and technology innovation; these factors are crucial to using the IoT. The second level is learning motivation, which has a close relationship with teachers’ knowledge, and content pedagogy, which has a significant effect on the familiarity with IoT tools and applications. TAM constructs have a positive and direct impact on the intention to use IoT. The practical and managerial implications show that teachers, educators, and students can obtain benefits from these results to help IoT features to suit users’ needs.

Prediction of Emergency Mobility Under Diverse IoT Availability

INTRODUCTION: Prediction of emergency mobility needs to consider more scenarios as Internet of Things (IoT) develops at a high speed, which influences the quality and quantity of data, manageable resources and algorithms. OBJECTIVES: This work investigates differences in dynamic emergency mobility prediction when facing dynamic temporal IoT data with different quality and quantity considering diverse computing resources and algorithm availability. METHODS: A node construction scheme under a small range of traffic networks is adopted in this work, which can effectively convert the road to graph network structure data which has been proved to be feasible and used for the small-scale traffic network data here. Besides, two different datasets are formed using public large scale traffic network data. Representative widely used and proven algorithms from typical types of methods are selected respectively with different datasets to conduct experiments. RESULTS: The experimental results show that the graphed data and neural network algorithm can deal with the dynamic time series data with complex nodes and edges in a better way, while the non-neural network algorithm can predict the with a simple graph network structure. CONCLUSION: Our proposed graph construction with graph neural network improves dynamic emergency mobility prediction. The prediction should consider the scenarios of availability of computing resources, quantity and quality of data among other IoT features to improve the results. Later, automation and data enrichment should be improved.

IoT-Based Alternating Current Electrical Parameters Monitoring System

Energy monitors are indispensable for achieving efficient electrical grids and even more so in the age of the Internet of Things (IoT), where electrical system data are monitored from anywhere in the world. This paper presents the development of a two-channel electrical parameter-monitoring system based on the M5 Stack Core2 kit. The acquisition of variables is done through PZEM 004T V3.0 sensors, and the data are sent to the ThingSpeak cloud database. Local readings are done through the LCD, and data re stored on a micro SD card. Remote monitoring is done through two applications, namely a web application and a mobile application, each designed for different purposes. To validate this proposal, a commercial device with IoT features (Gen 2 Vue Energy Monitor) is used, comparing the active power and active energy readings recorded continuously for 7 days. The results indicate an accuracy of up to 1.95% in power and 0.81% in energy, obtaining a low-cost compact product with multiple features.

Application of Speech Recognition Interaction and Internet of Things in Data Mining

The current data mining technology cannot attain the database of voice retrieval, and the data mining process has a high risk of interference. Therefore, the application of speech recognition interaction and Internet of Things (IoT) technology in data mining has been investigated. Using a speech recognition engine to recognize a user’s intention, a database retrieval model based on speech recognition interaction has been constructed. To enhance the security of data mining, the IoT data were classified by differential privacy clustering, and the false data features of IoT were detected efficiently. Finally, data mining was completed by combining data fusion and a Bayesian classifier. Experimental results demonstrated that the accuracy of the proposed method is over 90%, the time of data fusion is shorter, the time of data mining is shorter, the precision is higher, and the false alarm rate is lower than 5%.

Optimization of Energy Efficiency in UAV-Enabled Cognitive IoT With Short Packet Communication

The unmanned aerial vehicle (UAV) assisted communication can significantly improve the coverage and spectrum efficiency of the Internet of Things (IoT). One main feature of IoT is short packet communication (SPC), in which the data transmission uses finite block-length codewords. The transmission rate and the packet error rate will affect the effective throughput of the IoT. In this paper, we investigate the impact of SPC on the performance of UAV-enabled spectrum sharing network. Specifically, the optimization of energy efficiency (EE) in the UAV communication system is considered since the battery of the UAV is usually limited. We design the packet error rate, the sensing duration, the normalized sensing threshold and the UAV’s transmit power to maximize the EE under the constraint that the primary user is sufficiently protected. Since the system parameters are intertwined with each other, we employ a successive optimization algorithm to solve the optimization problem by dividing it into three subproblems. Then, an efficient iterative algorithm is proposed to obtain the optimal parameters. Simulation results reveal that the proposed algorithm can converge to global optimal value and has better EE performance than other benchmark schemes. And there is a fundamental tradeoff between the throughput and the EE.

Blockchain-based Internet of Musical Things

Blockchain technology is impacting several industries, including the creative industries and those operating in the Internet of Things (IoT). Lately, researchers' attention has been devoted to the application of blockchain in the recorded music industry. However, thus far, no research has investigated the use of such technology in the Internet of Musical Things (IoMusT). The IoMusT is a new area emerging in industry and academy as an extension of the IoT to the musical domain. The IoMusT itself, as the IoT, is a distributed network of musical things, which are objects augmented with information and communication technologies serving a musical purpose. The IoMusT vision requires, above all, IoT features such as decentralization, seamless authentication, transparency, data integrity and privacy, and self-maintenance, as well as the musical domain features such as efficient handling of copyrights and speed of royalties payment. Such features can be brought by blockchain. In this paper, we investigate the integration of blockchain technology with the IoMusT, and we name such synthesis “Blockchain-based IoMusT”. We present a vision for this new paradigm in terms of the novel opportunities that are enabled, and we propose a set of application scenarios enabled by technological integration. Finally, we outline the open research directions in this promising area.