Internet Of Things Technology Research Articles

The analysis of ecological security and tourist satisfaction of ice-and-snow tourism under deep learning and the Internet of Things

This paper aims to propose a prediction method based on Deep Learning (DL) and Internet of Things (IoT) technology, focusing on the ecological security and tourist satisfaction of Ice-and-Snow Tourism (IST) to solve practical problems in this field. Accurate predictions of ecological security and tourist satisfaction in IST have been achieved by collecting and analyzing environment and tourist behavior data and combining with DL models, such as convolutional and recurrent neural networks. The experimental results show that the proposed method has significant advantages in performance indicators, such as accuracy, F1 score, Mean Squared Error (MSE), and correlation coefficient. Compared to other similar methods, the method proposed improves accuracy by 3.2%, F1 score by 0.03, MSE by 0.006, and correlation coefficient by 0.06. These results emphasize the important role of combining DL with IoT technology in predicting ecological security and tourist satisfaction in IST.

The role of big data and IoT in logistics supply chain management of e-commerce

With the rapid development of social mode virtualization and electronic component technology, the application of data science and Internet of Things (IoT) technology in the field of e-commerce is gradually increasing. This study aims to explore how these emerging technologies can enhance the advantage of Chinese e-commerce companies in international competition. By comprehensively analyzing the massive data generated by online social networking and the application of IoT sensor technology in logistics and enterprise management, this paper proposes a decision support model based on data analysis. Research methods include data collection, data analysis and case studies. The results of the study show that data analytics and IoT technologies can effectively improve the efficiency of e-commerce operations and customer experience. The conclusion is that these technologies not only contribute to the domestic development of e-commerce enterprises, but also play a non-negligible role in international competition. This research has important implications for understanding the practical applications and potential of new technologies in the field of e-commerce.

Cybersecurity of Internet of Things in the health sector: Understanding the applicable legal framework

Although the digitalisation of healthcare is an ongoing process that dates back to more than two decades ago, it has gained more momentum with the COVID-19 pandemic. Recent developments in this sector include the adoption of wearable devices based on Internet of Things technology. The possibility of connecting devices that can work outside the physical boundaries of a hospital and follow patients at home, i.e. during their day-to-day life, has several obvious advantages. However, the digitalisation of the health sector through increased adoption of connected devices does not exclude vulnerabilities, particularly risks concerning the protection of patients’ data and the security of networks and information systems. Connected devices can gather, process, and store personal patient health data. Failure to safeguard the integrity and security of these data may affect the patients’ identity and finances and put their lives at risk. The presence of an IoT device in a healthcare setting may affect and reduce the level of network security of the overall system as it may provide an access point for an unlawful hacking attack. Although IoT technologies in the health sector are becoming increasingly pervasive, the European legal framework applicable to them is not clearly defined. This is extremely relevant in the case of cybersecurity, where the legal point of reference is the General Data Protection Regulation, addressing the measures and requirements applicable in case of data breaches, and the Medical Device Regulation, providing provisions focused on the security of data relevant to IoT defined as medical devices. The most recent interventions that address health data processing and cybersecurity are the proposed Cyber Resilience Act and the Health Data Space Regulation. The two acts provide measures and requirements applicable to IoT from two perspectives. Yet, they add complexities and some inconsistencies that may hamper the effectiveness of the overall cybersecurity framework.

Data acquisition and application of internet of things in financial management

With the rapid development of information technology, Internet of Things technology has become a hot topic in today’s society. This study aims to explore how IoT technology impacts various aspects of financial management, in particular how it changes the collection, processing, and parsing of financial data. Through in-depth literature review, questionnaire survey and experimental design, the research found that IoT technology has a significant positive effect on sales, transaction times and inventory control. But at the same time, data security, privacy concerns and initial technology investment are also challenges that enterprises need to consider when adopting these technologies. Overall, IoT technology offers tremendous opportunities for financial management, but businesses must fully assess the potential risks and benefits when implementing it.

Smart Agriculture Using IoT

The project focuses on physical components and sensors to leverage the Internet of Things (IoT) for smart agriculture, presenting a myriad of applications with the potential to revolutionize the agricultural sector. By connecting various physical objects, sensors, and machinery to the internet, IoT facilitates data collection and exchange for more effective agricultural management. The project introduces an in-house, user-friendly smart farming device designed to streamline farming operations, integrating essential information and communication technology with sensing technologies to provide efficient agricultural services. The goal is to acquire real-time data on temperature, humidity, water level, and soil moisture for environmental monitoring. Through the utilization of IoT technologies, farmers can enhance decision-making, increase output, and promote sustainable farming practices and effective agricultural systems.

On the impact of Industrial Internet of Things (IIoT) - mining sector perspectives

ABSTRACT The application of the Internet of Things (IoT) in industrial systems and other new technological advancements led to the development of the Industrial Internet of Things (IIoT). IIoT can help overcome the shortcomings of the conventional monitoring and control system while enabling enterprises to create a unified monitoring system to automate operations, provide a safe working environment, enforce compliance effectively, and regulate environmental issues. Given the advantages that IoT brings to the forefront, it makes sense that so many mining companies have raised their investment following the implementation of IoT-enabled solutions in their organisations. To increase safety, productivity, and environmental sustainability in mining operations, this article examines the current state of information technology in the mineral industry with an emphasis on the implications and challenges brought on by the technological diversity of various systems and devices used in those activities. The study contributes valuable insights into the integration of IoT technologies in the mining industry, highlighting its potential to improve safety, efficiency, and sustainability. The paper paves the way for future research and development efforts aimed at overcoming the challenges of adopting IIoT in mining operations by identifying gaps and proposing a comprehensive IoT architecture. The innovations of the study are encapsulated in its approach to detailing the application of IoT technologies in enhancing mining operations and the establishment of an overall IIoT architecture suitable for the general operations in the mining industry.

Anomaly-based Intrusion Detection System in Industrial IoT-Healthcare Environment Network

The Internet of Things (IoT) technology facilitates automation, monitoring, and control of tangible objects and surroundings by enabling connected devices to interact and exchange data over the Internet. Developments in edge computing, blockchain, and artificial intelligence (AI) are incorporated into IoT technologies for more reliable operations. Inadequate authorization, authentication, and encryption protocols could render IoT networks insecure and open the door to illegal access and data breaches which can have terrible consequences, most notably in the healthcare industry. In this regard, to identify malicious and incursion traffic, machine learning (ML) is crucial to Internet of Things (IoT) cybersecurity. The paper proposes a framework to detect intrusion or malicious traffic in IoT-enabled different medical equipment such as medical sensors, and controllers for real-time data collection, creating communication channels and data monitoring and analysis over locally available network nodes. IoT-Flock has been utilized for both normal and malicious traffic generation in a wide dataset found by the sensors connected to IoT integrated healthcare network. The feature selection-based proposed framework has been evaluated by three distinct machine learning classifiers, KNN, RF, and DT where corresponding accuracy, sensitivity, precision, and F1-score have been measured for performance analysis. With an accuracy of 99.74%, the KNN technique performed better than the other tactics used by RF and DT regarding intrusion detection in IoT networks. The suggested framework will be helpful in developing or analyzing security solutions in IoT-integrated network systems.

Security and privacy challenges, issues, and enhancing techniques for Internet of Medical Things: A systematic review

AbstractThe Internet of Things (IoT) is a rapidly expanding network of interconnected things that use embedded sensors to gather and share data in real‐time. IoT technologies have given rise to many networking applications in our everyday life such as smart homes, smart cities, smart transport, and so forth. Smart healthcare is one such application that has been revolutionized by the IoT, introducing a new branch of IoT known as the Internet of Medical Things (IoMT). IoMT encompasses an entire ecosystem consisting of smart wearable, implantable sensing equipment's or devices, transmitters that are critical for monitoring the patients remotely and continuing the real‐time and has opened the door to new innovative smart healthcare approaches while improving patient care outcomes. IoMT wearable and embedded sensing devices are commonly utilized in smart healthcare to capture medical data and transmit the medical data in a communication network stored in the cloud. The large volume of data generated and transmitted by these IoMT devices is rising at an exponential rate, resulting in an increase in security and privacy vulnerabilities of healthcare data. To ensure the Confidentiality and integrity of the IoMT devices and the sensitive medical data, there should be proper security and privacy measures such as access control, passwords, multifactor authentication, and encryption of data generated, transmitted, or processed in the IoMT framework. In this paper, we identified the internet of things and its applications in smart healthcare systems. Additionally, the paper focuses on the architecture of IoMT, and several challenges, including the IoMT security and privacy requirements, and attack taxonomy. Furthermore, the paper thoroughly investigates both cryptographic and non‐cryptographic based security and privacy‐enhancing techniques for IoMT or healthcare systems with particular emphasis on advancements in key areas such as Homomorphic Encryption, Differential Privacy, and Federated Learning.

IOT Based Anti - Theft Flooring System Using NodeMCU (ESP8266)

Traditional anti-theft security systems often rely on easily circumvented methods such as motion sensors or wall-mounted devices, leaving vulnerabilities that can be exploited by savvy thieves. To address these limitations, we propose an innovative IoT-based anti-theft flooring system utilizing Node MCU technology. This system offers comprehensive monitoring of designated areas, detecting any movement within its coverage zone. Each secure flooring tile within the system is seamlessly integrated with IoT capabilities. Upon activation, the system begins monitoring the area, transmitting data over the internet to alert users of any intrusions. When an unauthorized individual steps onto the floor, sensors promptly detect the movement and relay the information to the central controller. The controller verifies the signal's validity and triggers the camera to capture images of the detected movement area. These images are then transmitted over the internet, allowing the owner to remotely assess the situation in real-time. By leveraging IoT technology, our anti-theft flooring system offers enhanced security measures, providing peace of mind for property owners. Keywords: IoT (Internet of Things), Anti-theft system, Flooring security, NodeMCU (ESP8266) ,IoT security , Real time monitoring.

Intelligent IoT-based Control System of the UAV for Meteorological Measurements

This study focuses on the creation and examination of an intelligent automated control system for UAVs utilized in meteorological measurements based on the Internet of Things (IoT) and mobile technologies. The proposed system enables the achievement of commendable flight control standards for UAVs during meteorological data gathering, thereby markedly enhancing the overall effectiveness of meteorological stations. Notably, this system is constructed on the foundation of three integrated principles: (a) a hierarchical two-level approach for control and data collection based on IoT and mobile technologies, (b) a straightforward and dependable fuzzy logic control characterized by high performance, and (c) the effective optimization of fuzzy control components through the application of bio-inspired multi-agent computing techniques. To assess the performance of the suggested intelligent system, this study involves the creation and bioinspired optimization of the climb speed fuzzy controller. Subsequent simulation experiments are conducted to evaluate the automatic control of UAV’s flight processes under different modes. The analysis of the simulation results indicates that the developed system, utilizing fuzzy control, exhibits superior efficiency and higher quality metrics when compared to existing counterparts, especially in diverse flight scenarios such as uniform climbing, gradual approach to designated altitude levels, and smooth landing during meteorological measurements.

IoT ENBALED SMART AQUARIUM FOR MONITORING THROUGH SENSORS

The development of an IoT-enabled smart aquarium system offering real-time monitoring and automated control of crucial environmental parameters. The core of the system lies in the ESP8266 microcontroller, facilitating seamless internet connectivity and data processing. Water quality assessment is achieved through a turbidity sensor, detecting suspended particles indicative of potential pollution. Environmental conditions are monitored using a DHT11 sensor, providing data on both temperature and humidity – essential factors for maintaining a healthy habitat for specific fish species. For automated lighting control mimicking natural day/night cycles and preventing excessive algae growth, a Light-Dependent Resistor (LDR) is implemented. The LDR adjusts aquarium lighting based on ambient light levels. Finally, a relay module controls the operation of a water pump, ensuring optimal water circulation and distribution of nutrients throughout the aquarium. While an I2C LCD connected to an Arduino Nano provides local data visualization within the aquarium setup, the ESP8266 transmits sensor readings to a user-friendly platform accessible remotely through a mobile application or web interface. This allows for convenient real-time monitoring of water quality, temperature, humidity, and lighting conditions. The system can be further programmed to generate alerts when critical parameters deviate from the ideal range, enabling proactive user intervention to maintain a healthy environment. This project demonstrates the potential of IoT technology to create a more automated, efficient, and user-friendly system for maintaining a thriving aquatic ecosystem. KEYWORDS: IoT (Internet of Things), Smart Aquarium, ESP8266, Turbidity Sensor, DHT11 Sensor, LDR(Light-Dependent Resistor), Relay Module, Water Pump, I2C LCD, Arduino Nano, Real-Time Monitoring, Automated Control, Remote Access, Mobile Application, Web Interface, Water Quality, Temperature, Humidity, Light Control, Aquatic Environment.

Internet of things technology, research, and challenges: a survey

Internet of things technology, research, and challenges: a survey

Efficient Smart Water Management Techniques to Enhance Crop Productivity for Maize

Maize is identified as the “Queen of Cereals” owing to its high yield. Water scarcity is a major problem, where agriculture consumes significant freshwater. Smart Irrigation with Internet of Things technology can help farmers to maximize crop production with less water. The field experiment on sensor-based drip Irrigation and farmer practice was conducted in the research field to enhance growth, yield, and water efficiency. The experiment comprised eight treatments such as T1: IoT based Drip Irrigation at 60% depletion level, T2: IoT based Drip Irrigation at 80% depletion level, T3: Drip Irrigation at 60% PE, T4: Drip Irrigation at 80% PE, T5: Drip irrigation as Normal Practice, T6: Surface Irrigation at 60% IW/CPE ratio, T7: Surface Irrigation at 80% IW/ CPE ratio and T8: Flood irrigation as Farmer’s Practice. The proposed systems-based Drip irrigation at a 60% depletion level can be recommended for hybrid maize to augment higher grain and straw yield. The highest water saving was recorded in IoT-based Drip Irrigation at a 60% depletion level(46.88%) followed by IoT-based Drip Irrigation at an 80% depletion level(40.63%).

Multicriteria group decision making for prioritizing IoT risk factors with linear diophantine fuzzy sets and MARCOS method

In a dynamic world of technological advances, the Internet of Things (IoT) is a transformational and widespread force that has revolutionized the way we communicate with our surroundings and regulate our environments. It offers several advantages but also introduces inherent risks. In this study, we provide a comprehensive analysis of the risks associated with IoT and employ the effectiveness of a Linear Diophantine Fuzzy Set to rank the risk factors. Because of the significant uncertainties frequently present in IoT contexts, the use of a fuzzy framework is invaluable in discerning and addressing these risks. The primary contribution is to employ the Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) method and linear diophantine fuzzy sets to propose a multi-criteria group decision-making method (MCGDM) for ranking attributes to facilitate risk prioritization, enabling consumers to determine the crucial hazards in their IoT systems. Furthermore, we implement a comparative study and a sensitivity analysis to demonstrate the robustness of our proposed methodology. The insights obtained from our research not only improve the awareness of IoT hazards but also enable organizations and individuals to make informed decisions when navigating IoT fields. By proactively addressing these risks, we endorse the development and secure deployment of IoT technology.

Innovative pre-fire alert smart detection system-based embedded system

Early fire identification is critical to saving lives and property. One viable strategy for improving fire detection systems’ efficacy and precision is the use of Internet of Things (IoT) technology. This article details research into the conception and development of an IoT, ESP32-based, Arduino MEGA-connected smart pre-alert detection system for fires. Because of its flexible nature, the Arduino microcontroller can easily exchange data with various sensors for real-time monitoring and analysis. This study surveys the existing literature to provide insight into many approaches to early fire detection (forecasting), such as sensor fusion methods and the use of the intelligence concept as a pre-predictor. The research highlights the need to set up sophisticated fire monitoring and alarm systems and to develop predictive models based on sensor data. This study illustrates how embedded systems via IoT technology and the ESP32 architecture may improve the efficiency and dependability of very early fire detection systems. This research adds to the expanding corpus of information that may be used to improve fire safety in contemporary settings. The proposal’s sensors include an Arduino MEGA, an ESP32, a MAX6675 module, an MQ-Smoke, and an MQ-Natural Gas for collecting data, which are then processed using Cloud Firebase and a sleek mobile interface that can be reached overseas at any time.

Construction of urban low-carbon development and sustainable evaluation system based on the internet of things

Low-carbon (LC) cities are the cities that people long for today. LC environmental protection plays a very important role in people's health. The construction of a city in LC is a great cause that contributes to the present and benefits the future. In this study, we propose a development and sustainability evaluation system for building low-carbon cities based on the Internet of Things (IoT). The study is novel in that it considers key areas such as urban planning, environmental issues and solutions, and how the Internet of Things can optimize low-carbon logistics and smart grids, with the aim of promoting the formation of low-carbon city models. This comprehensive approach not only presents problems and solutions in low-carbon urban planning but also focuses on how the Internet of Things can be used as a key technology to promote low-carbon urban development. The urban development of LC was constructed with a sustainable evaluation system, so that people could experience the life of LC. Through the investigation of the degree of atmospheric pollution of LC cities using the Internet of Things, this article found that the highest degree of atmospheric pollution was 30. The highest degree of atmospheric pollution in cities in LC without IoT was 53. The severity of water pollution in cities in LC using IoT technology ranged from 10 to 25, while those without IoT ranged from 30 to 60. The degree of soil pollution in LC cities using IoT technology was concentrated in 10–30, while those without using IoT were concentrated in 30–50. Through these experimental data, it could be seen that IoT technology could reduce environmental pollution, thus achieving the effect of LC cities. This shows that the use of IoT technology in LC cities was highly feasible.

Impact of Internet of Things (IoT) on Enhancing Transparency and Efficiency in Bangladesh's Agri-food Supply Chain

Background: The agri-food supply chain in Bangladesh is crucial for the nation's sustenance and economic stability but faces challenges like lack of transparency, inefficiencies, and information asymmetry. The integration of Internet of Things (IoT) technologies offers transformative potential, enhancing the Transparent Physical and Information Flow (PHF) and thereby influencing the Transparency in the Agri-food Supply Chain (TASC).   Objective: This study aims to investigate the impact of IoT integration on PHF and its subsequent effect on TASC in Bangladesh, framed around two core hypotheses: the significant influence of IoT on PHF and the consequent impact of an IoT-enhanced PHF on TASC.   Methods: Employing a comprehensive methodological framework, the study utilized IBM SPSS for exploratory factor analysis and IBM AMOS for confirmatory factor analysis (CFA) and structural equation modeling (SEM) to assess the relationships between IoT technologies, PHF, and TASC. The analysis was based on data collected from 400 respondents in Bangladesh's agriculture sector.   Results and Conclusion: The findings validate both hypotheses, demonstrating a significant and positive impact of IoT technologies on PHF and, subsequently, on TASC. The results highlight the crucial role of IoT in enhancing supply chain transparency and operational efficiency.   Implications of the Research: This research provides empirical evidence on the effectiveness of IoT technologies in addressing transparency issues within the agri-food supply chain. It underscores the potential of IoT to foster improved decision-making, efficiency, and consumer trust in the agricultural sector, particularly in developing contexts like Bangladesh.   Originality/Value: This study contributes original insights into the role of IoT technologies in enhancing the agri-food supply chain's transparency and efficiency. By focusing on the context of Bangladesh, it offers valuable implications for policymakers, industry practitioners, and researchers, emphasizing the transformative potential of IoT in agricultural supply chain management.

A Novel and Self-Calibrating Weighing Sensor with Intelligent Peristaltic Pump Control for Real-Time Closed-Loop Infusion Monitoring in IoT-Enabled Sustainable Medical Devices

Technological advancements are propelling medical technology towards automation through the application and widespread use of automatic control, sensing, and Internet of Things (IoT) technologies. Currently, IoT technology has been extensively applied in medical devices, aiming to ensure patient safety through more real-time detection and more effective management. In the monitoring of intravenous infusion, accurately sensing the infusion conditions in real time is particularly important. This article introduces a low-cost smart infusion device based on IoT technology, which controls the infusion rate with a peristaltic pump and monitors the volume of fluid delivered. It uses an improved, self-calibrating weighing sensor to achieve the real-time closed-loop control of the flow rate, ensuring patient safety. Additionally, the Blynk dashboard can be used for monitoring and controlling the flow rate and infusion volume.

IoT applications in asset management: A review of accounting and tracking techniques

In the digital age, the confluence of Internet of Things (IoT) technologies with asset management practices heralds a transformative era, promising unprecedented efficiency and strategic advantage. This paper delves into the intricacies of this integration, exploring the evolution, challenges, and potential of IoT in reshaping asset management paradigms. With a keen aim to elucidate the transformative impact of IoT and navigate the complexities of its integration into asset management systems, the study adopts a qualitative research methodology, underpinned by a systematic literature review. This approach enables a comprehensive exploration of the subject matter, ensuring a nuanced understanding of the IoT's potential and pitfalls within the asset management domain. The findings illuminate a landscape where IoT applications significantly enhance asset utilization rates, underscored by empirical success stories and quantifiable improvements in operational efficiency. However, the journey reveals barriers—technical, organizational, and cultural—that impede seamless IoT adoption. Addressing these challenges, the paper offers strategic recommendations, emphasizing the need for strategic alignment, inter-organisational collaboration, and robust data management strategies to overcome integration hurdles. Conclusively, the study not only achieves its aim of highlighting the potential of IoT in asset management but also charts a course for future endeavors in this field. It posits that the strategic integration of IoT technologies stands as a cornerstone for the next generation of asset management practices, promising a future marked by enhanced efficiency, economic viability, and strategic agility. The recommendations provided serve as a compass for organizations embarking on the IoT integration journey, guiding them towards a horizon of transformative potential.

AGRICULTURAL FIELD MANAGEMENT PROBLEMS: FROM CLASSIC AGRO-TECHNOLOGIES TO ARTIFICIAL INTELLIGENCE

The text is straightforward and precise. The agricultural sector has been significantly impacted by artificial intelligence (AI) technologies such as expert systems, natural language processing, speech recognition, and machine vision. This is due to factors such as the rising global population, increasing demand for food, changing weather conditions, and water availability. AI has not only increased the quantity of work in agriculture but also improved its quality. Researchers and scientists are now adopting new IoT technologies in smart farming to enable farmers to use AI technology for the advancement of seeds, crop protection, and fertilizers. This would enhance the financial viability of farmers and contribute to the general economic growth of the nation. AI is being used in three primary areas in agriculture: soil and crop monitoring, predictive analytics, and agricultural robots. Farmers are increasingly using sensors and soil sampling to collect data for farm management systems, which will be utilized for future studies and analysis. This paper enhances the area by doing a study of artificial intelligence applications in the agriculture industry. The text provides an introduction to AI, covering several AI techniques used in the agricultural sector, such as machine learning, the Internet of Things (IoT), expert systems, image processing, and computer vision.