Internet Of Things Monitoring Research Articles

GOVERNANCE OF IOT DEVICES USING NODE-RED ORCHESTRATOR AND WEB-BASED DASHBOARD

High electricity bills are often caused by a lack of monitoring the use of electronic equipment in the household. One common problem is forgetting to turn off appliances that are not in use, which causes electricity to continue flowing and increases electricity bills. Currently, although there are several technologies that allow the control of appliances, their features are still limited, especially in lamps. This research focuses on developing Internet of Things (IoT) technology in the field of energy and control. One of the implementations is using a Raspberry Pi microcontroller to monitor the use of electrical energy in households. In order to make the use of IoT components more efficient and easier, a NODE-RED-based system is used which serves as a link between end users and IoT equipment. For monitoring IoT components, a web dashboard using the Laravel framework was also built. This allows users to view the components used and the activities that occur on their IoT equipment. High electricity bills are often caused by a lack of monitoring the use of electronic equipment in the household. One common problem is forgetting to turn off appliances that are not in use, which causes electricity to continue flowing and increases electricity bills. Currently, although there are several technologies that allow the control of appliances, their features are still limited, especially in lamps. This research focuses on developing Internet of Things (IoT) technology in the field of energy and control. One of the implementations is using a Raspberry Pi microcontroller to monitor the use of electrical energy in households. In order to make the use of IoT components more efficient and easier, a NODE-RED-based system is used which serves as a link between end users and IoT equipment. For monitoring IoT components, a web dashboard using the Laravel framework was also built. This allows users to view the components used and the activities that occur on their IoT equipment.

Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique.

With the increased use of automated systems, the Internet of Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement for the timely detection of unexpected values. Technical faults can introduce anomalies, and a large incoming data rate might make the manual detection of erroneous data difficult. This research introduces and applies a pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to real-time water quality monitoring. MCN-LSTM is a cutting-edge deep learning technology designed to address the difficulty of detecting anomalies in complicated time series data, particularly in monitoring water quality in a real-world setting. The growing reliance on automated systems, the Internet of Things (IoT), and sensor networks for continuous water quality monitoring is driving the development and deployment of the MCN-LSTM approach. As these technologies become more widely used, the rapid and precise identification of unexpected or aberrant data points becomes critical. Technical difficulties, inherent noise, and a high data influx pose significant hurdles to manual anomaly detection processes. The MCN-LSTM technique takes advantage of deep learning by integrating Multiple Convolutional Networks and Long Short-Term Memory networks. This combination of approaches offers efficient and effective anomaly detection in multivariate time series data, allowing for identifying and flagging unexpected patterns or values that may signal water quality issues. Water quality data anomalies can have far-reaching repercussions, influencing future analyses and leading to incorrect judgments. Anomaly identification must be precise to avoid inaccurate findings and ensure the integrity of water quality tests. Extensive tests were carried out to validate the MCN-LSTM technique utilizing real-world information obtained from sensors installed in water quality monitoring scenarios. The results of these studies proved MCN-LSTM's outstanding efficacy, with an impressive accuracy rate of 92.3%. This high level of precision demonstrates the technique's capacity to discriminate between normal and abnormal data instances in real time. The MCN-LSTM technique is a big step forward in water quality monitoring. It can improve decision-making processes and reduce adverse outcomes caused by undetected abnormalities. This unique technique has significant promise for defending human health and maintaining the environment in an era of increased reliance on automated monitoring systems and IoT technology by contributing to the safety and sustainability of water supplies.

Door lock system based on internet of things and Bluetooth by using Raspberry Pi

Recently, there are a large number of smart lock systems that can be used in the workplace or home, but many of them depend on finding an external key, password, fingerprint, and facial recognition, where these systems suffer from weakness as the physical key may be forgotten in a place, the password can be memorized which is hard for some peoples, and the fingerprint system sometimes does not work on people who suffer from chronic diseases, such as people with diabetes and for each of these systems the person needs a free hand so that he/her can open the lock which means that is if the person carries something he/her needs to be placed somewhere. The facial recognition system cannot recognize the person's face when the place has poor lighting. This paper proposes a home door lock system that relies on the internet of things (IoT) technology and portable electronic devices Bluetooth such as a smart mobile using the device Bluetooth media access control (MAC) address as a key and the IoT for monitoring, which leads to a door lock that does not require a free hand. The proposed lock system outcome can be a good candidate for home security applications.

Implementation of the Internet of Things for Monitoring and Protecting Hydrogen Production in Dry Cell HHO Generators

In the last few decades, researchers have been conducting various studies in search of alternative energy sources to reduce dependence on fossil fuels and anticipate future energy scarcity. One such alternative energy is hydrogen, which can be obtained through various methods, including electrolysis of water. Hydrogen possesses the characteristics of being flammable, odorless, colorless, and tasteless, necessitating the development of a rigorous monitoring and protection system to mitigate potential hazards. In this study, a monitoring and protection system was designed for the operation of an electrolyzer using a Dry Cell type HHO Generator. The system employs the concept of the Internet of Things, where data is connected through the internet network. The data received includes hydrogen concentration readings, electrolyzer operation automation, electrolyzer operating status, and hydrogen gas leak detection in the system. The research results obtained showed that the higher the concentration of electrolyte and the voltage supplied to the electrolysis process, the faster the production of hydrogen gas is accelerated, accompanied by an increase in temperature. If a leakage occurs in a system, the occurrence of the fire triangle will be faster when the concentration of electrolyte and the voltage supplied are greater.

Investigation on Water Quality for Farmed Aquatic Species by IoT Monitoring System

The objective of this research is to optimize fish growth in super-intensive aquaculture systems by addressing water quality issues. Changes in water parameters can significantly impact fish growth and even lead to mortality. To overcome this problem, real-time monitoring of water quality parameters is crucial, and the data should be automatically transmitted to the entrepreneur through the Internet of Things (IoT). This allows the owner to access real-time data remotely, eliminating the need to be physically present at the fish cage. By monitoring the data on a smartphone, the owner can efficiently manage the water quality. In this study, a water quality monitoring system utilizing temperature, pH, and ESP32, with the parameter values displayed on the Blynk platform was developed. The system successfully measures real-time water conditions and transmits the data to the Blynk application. The test results demonstrate the system's capability to receive and send data, but further analysis is needed to evaluate the accuracy and reliability of the measurements and identify any noteworthy findings.

Towards an IoT and Blockchain-based System for Monitoring and Tracking Agricultural Products

This paper presents the design and implementation of a monitoring and data collection system for agricultural product preservation during transportation to meet authenticity and traceability requirements. The system is built and performed using blockchain and Internet of Things (IoT) technology to use and integrate their benefits. This system comprises a private blockchain network built on the Ethereum platform and powered by the GoQuorum protocol, which enables distributed data exchange among members using a common protocol and cryptographic techniques while ensuring the transparency and integrity of the information. A wireless sensor network has been designed and implemented for collecting data from sensors about the storage environment of agricultural products. This study also proposes using LoRa technology to build low-cost wireless communication networks with broad coverage for data collection from sensor nodes mounted to agricultural products. Furthermore, the integrated online map web assists in real-time monitoring and tracing of information on the process of transporting agricultural products, as well as querying data for completed contracts. Experiments were conducted close to the reality of the application’s desire to deploy the system to calibrate and evaluate the functioning, with promising results.

Applications of IoT and digital twin in electrical power systems: A comprehensive survey

AbstractThis paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research.

Encoding IoT for Patient Monitoring and Smart Healthcare: Connected Healthcare System Fostering Health 6.0

Internet of Things (IoT) has revolutionized the way healthcare and patient monitoring was done until now. The Next Generation era that this evolved to is called Health 6.0 which is secured by IoT devices, this smart play in healthcare landscape gives the swat-the-clock monitoring inkling fostering detection controls for them to turn antibiotics at razor-edge level precision results enhancing efficiency and effectiveness box between disease endowment/therapy alternatives. The obvious examples is that IoT devices like wearables or medical equipment will be able to send health data in real time back to healthcare providers so they can leverage the additional information for better diagnosis, treatment as well as care management. This ecosystem provides the plumbing and tools to flow data between different healthcare systems so physicians can quickly access all of the information they need at point-of-care. IoT can also be used in patient monitoring, so that healthcare providers have data-supported decisions on care processes and treatment strategies; by predicting the health problems before reaching critical levels. This paper deeply dives in the futuristic vision of Smart Hospitals and their potential to reshape the healthcare 6.0 landscape.

DL BASED IOT ENERGY AUDIT ANALYTICS FOR DETECTING AND IDENTIFYING CYBER-PHYSICAL ATTACKS

Internet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics and file access records, can be forged. Furthermore, existing solutions mainly target cyberattacks. This paper proposes the first energy auditing and analytics based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning (DL) model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single DL models for energy disaggregation, we propose a disaggregation-aggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g., CPU, network, disk, etc., to identify cyber-attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances.

An Evaluative Study on IoT Ecosystem for Smart Predictive Maintenance (IoT-SPM) in Manufacturing: Multiview Requirements and Data Quality

With the recent advances of the Internet of Things (IoT), innovative techniques and concepts have emerged, such as digital twins and industrial 4.0. As one of the essential parts of a digital twin, IoT-based smart predictive maintenance (IoT-SPM) is a key enabling technology for smart manufacturing. This paper introduces digital twins and their relationship to IoT-SPM and proposes a reference IoT-SPM, aiming to provide a comprehensive and systematic outlook for IoT-SPM field. Thus, it can be used as a guide map for interested readers. To give a complete picture of the IoT-SPM ecosystem in industrial 4.0 systems, this paper conducts an analysis from multi-view perspectives, starting with the architecture, followed by platforms and component. The key components or requirements of an IoT-SPM ecosystem are identified and outlined, including the IoT and cyber-physical system (CPS) as the cornerstone technologies, IoT monitoring data as the base, big data platforms as the backbone, an upgraded computing paradigm as the catalyst, and machine learning-based data analysis as the main processor. This paper also focuses on the issues surrounding IoT data when applying analytic models to a real-world industrial IoT system. Then, the current progresses relating IoT and IoT-SPM are depicted, and a research gap on IoT data quality is identified. In particular, regarding the identified IoT data quality problems, this paper qualitatively evaluates and discusses the existing solutions. These discussions lead to several open research issues and future directions.

BEAST: Behavior as a Service for Trust management in IoT devices

As the internet becomes intertwined into every aspect of human life, the security of the Internet of Things (IoT) is also becoming increasingly critical. IoT devices are becoming the primary data source for a variety of smart-city applications, where critical decisions are based on this collected data. If malicious actors gain control of and/or tamper with the data being transmitted, the integrity of an entire smart city will be compromised. However, through monitoring IoT devices’ behavior, anomalies can be detected and isolated to avoid any negative impact on decision-making. This behavioral monitoring process will complement traditional trust management approaches, since more accurate trust values can be calculated without the need to rely on a majority consensus. In this work, we present a BEhavior-As-a-Service for Trust management (BEAST) that implements a deep learning-based behavioral model to accurately classify IoT devices’ interactions in the system. Through the implementation of the Elo rating system, these classifications will be presented as a vector of behaviors per device, which dynamically reflects each device’s trust in the system. This work presents an analysis of our methodology as well as a threat model. Using simulations, a real-world use case is presented showing the interactions between IoT-based devices. Our results show that our BEAST model is able to dynamically evaluate each IoT device’s trust, as well as capture and mitigate multiple threats targeting the trust in the system.

Internet of Things Horizontal Platform Development for Expanded Application Scenarios and Use Cases

This paper presents the horizontal approach for the development of Internet of Things (IoT) platforms, based on a generic, multi-service, cloud-based IoT operational platform, called EMULSION, elaborated for the rapid development of mobile IoT systems and quick roll-out of corresponding IoT services. The platform is successfully used as a basis for the development of various IoT prototype systems for different IoT domains, such as smart environment control, smart health, smart electric appliances, smart water supply, etc. As an example of these, a ground surface displacement monitoring IoT system, operating in the area of smart environment control, is presented, along with aspects of the hardware design of the produced low-cost, small-size, global navigation satellite system (GNSS)-based sensor unit, utilized by it. The system architecture design is set out, supplemented by the presentation of initial test results.

Penerapanan Internet 0f Things (IoT) Dalam Membangun Sistem Aplikasi Kendali Rumah Kos Menggunakan Aplikasi Android

Internet of Things (IoT) devices are now starting to be widely used, one of which is IoT Monitoring. The use of electricity by boarding house tenants is often negligent, they feel they have paid the boarding fee and are wasting electricity even though it is not used, for example, the lights in a boarding room where there are no people inside. Likewise with the bathroom if the water tub is full, often the occupants of the boarding house are indifferent to water. The use of ACS712 and ZMPT101B sensors to see the electricity usage of each boarding room. Boarding room security is strengthened by the use of RFID modules and PPIR sensors. Electrical tools and Solenoid Door Lock can be controlled via an android application. The accuracy of the sensor reading electricity usage is still below normal limits, namely 0.11% -0.74% for volts, and 0.72% -3.84% for current. In sending orders from the android application there is a delay in the response of the device due to factors that cannot be calculated, namely internet connection

Modernizing Medical Waste Management: Unleashing the Power of the Internet of Things (IoT)

The rapid technological advancements of modern times have brought about the need for an innovative and contemporary approach to medical waste management procedures. This arises from the inadequacy of conventional manual techniques in ensuring the safety of employees and the environment from infections. The increasing amount of waste produced each day can exacerbate the situation if no action is taken to address the current issue. This article presents a systematic review of the use of the Internet of Things (IoT) in medical waste management, utilizing the PRISMA approach. The adoption of the IoT in waste and medical waste monitoring is analyzed for its potential to enhance the overall waste monitoring procedure and contribute to achieving net-zero goals. Empirical evidence from studies conducted in the last five years has revealed the benefits of employing waste bin sensors as a digital surveillance tool for real-time waste status monitoring. While a few researchers have proposed the use of the IoT in medical waste monitoring, the application is currently limited to either monitoring storage facilities, waste transportation, or disposal processes, specifically. These limitations are discussed to understand the barriers that hinder further development. Among the selected analyzed studies are published articles and conference papers that offer solutions for addressing waste management issues and facilitating further development. This paper also aims to identify IoT technologies for monitoring waste and medical waste management. The digitalization of medical waste can ensure that the entire monitoring procedure is conducted directly and in real time. The collected data can be easily shared, and the condition of the waste can be updated periodically.

Mapping a Decade of Smart Homes for the Elderly in Web of Science: A Scientometric Review in CiteSpace

Challenges caused by the care of the ageing population in many countries are becoming increasingly severe. However, high expenditures on healthcare services and the cost of infrastructure construction will be an unbearable burden for both the government and individuals. Due to its security, convenience, and lower prices compared with traditional medical care, smart home technology, which includes information and communication technologies (ICTs), artificial intelligence (AI), and the Internet of Things (IoT), can reduce the care burden. While smart home studies targeting older adults have increased, systematic analysis of these studies is lacking. CiteSpace enables rapid clustering and visualisation of the literature using scientific diagrams, exhibiting the results more comprehensibly. This analysis of academic research published within the last decade (from 2012 to 2023) through a scientometric approach can help to understand the historical variations, the latest technologies and emerging trends, and the research clusters that have emerged related to the study of smart homes for the elderly (SHFTE). The study results showed that recent research mainly centres on IoT for home automation and home-based healthcare, including wearable device applications and information security analysis. Research trends regarding SHFTE have focused primarily on developing and updating smart technologies based on the IoT, sensors, and related home monitoring. Therefore, greater emphasis must be placed on connecting smart homes and intelligent buildings with communities and societies, developing the potential of smart communities, and investigating the application of smart integration to the home and community in cities.

Intelligent solar panel monitoring system and shading detection using artificial neural networks

Detecting shading in Photovoltaic panels (PV) is crucial for ensuring optimal energy generation. This paper proposes a novel monitoring system that uses Artificial Neural Network (ANN) technology to detect shading and other faults in PV panels. The system is also supervised using an Internet of Things (IoT) monitoring platform, which provides real-time data analysis and alerts. The proposed system’s main contribution is its ability to detect shading, which can significantly impact energy generation. The ANN technology accurately detects shading and other faults, while the IoT platform enables remote monitoring and data analysis. Overall, this paper presents a valuable contribution to the field of PV monitoring systems by proposing a novel system that detects shading using ANN technology and is supervised using an IoT monitoring platform. The system’s ability to accurately detect shading and other faults can significantly improve energy generation efficiency and reduce maintenance costs.

Monitoring and Controlling Hydroponic Units using IoT

The use of hydroponic systems for growing crops is becoming increasingly popular due to its efficiency and sustainability. However, monitoring and controlling these systems can be challenging and time-consuming. This is where the Internet of Things (IoT) comes into play, as it allows for the real-time monitoring and control of hydroponic systems from anywhere at any time. In this paper, we explore the use of IoT for monitoring and controlling hydroponic units, including sensors for measuring various parameters such as pH, temperature, and nutrient levels, and actuators for controlling water and nutrient supply. We also discuss the advantages of using IoT for hydroponic farming, such as increased yield and reduced resource usage, as well as potential challenges and future research directions. Overall, this paper provides a comprehensive overview of the use of IoT in hydroponics and its potential to revolutionize modern agriculture.

Advances in Triboelectric Flow Sensor

AbstractWith the rapid development of advanced materials and manufacturing technologies, flow sensors have made significant progress in the field of mechanical engineering, which could cater to the development trend of the Internet of Things (IoT) and the ongoing Fourth Industrial Revolution. However, traditional power supply modes such as lithium batteries require regular recharging or replacement, which greatly causes too much inconvenience and maintenance consumption, and may also pose potential risks to the marine environment. The triboelectric nanogenerators (TENGs), based on the coupling effect of contact electrification and electrostatic induction, have been demonstrated to convert mechanical movements of the fluid into electrical signals with various features such as flexibility, conformability, and user‐friendliness. This review systematically summarizes for the first time the fundamental working mechanism, rational structural design, and analysis of practical application scenarios of triboelectric flow sensor as an emerging technology for flow monitoring of the Industrial IoT. According to the different fluid objects monitored, the latest representative achievements of triboelectric flow sensors can be divided into gas flow sensors, liquid flow sensors, and two‐phase flow sensors. Finally, the current challenges and future development directions of triboelectric flow sensors are examined, which can promote the further development of the field of the flow sensors.

Smart platform based on IoT and WSN for monitoring and control of a greenhouse in the context of precision agriculture

The rapid evolution of communication technology has spurred a revolution in many domains, which makes life more comfortable and convenient. In order to meet the surge in food demand due to the worldwide growing population, reduce farmers' efforts, and benefit from these technologies in agriculture, an intelligent platform based on IoT (Internet of Things) is proposed in this paper for monitoring and controlling greenhouse climate and irrigation. The system used a low-cost wireless sensors network (WSN) based on radio frequency (RF) communication for collecting and sending the greenhouse data (temperature, humidity, and soil moisture) to a processing unit based on Raspberry Pi. A fusion system based on fuzzy logic controller (FLC) was used to combine the collected data and to supply smart and optimal decisions to manage the internal climate and irrigation. The user can remotely monitor the greenhouse using a Human Machine Interface (HMI) developed under Node-RED server of IBM. In addition, the system allowed storing the environment data in a MySQL database to be exploited for control, monitoring, or history storage. The test results showed the effectiveness and success of the proposed platform.

IoT and health monitoring wearable devices as enabling technologies for sustainable enhancement of life quality in smart environments

The Internet of Things (IoT) technology with wearable devices provides a promising solution that enables con-tinuous monitoring of health parameters. Non-invasive sensors enable remote health monitoring and improve many aspects of life quality. The study explores advances in IoT and Wearable Devices through a conceptualization of health monitoring observer networks. The paper proposes an architecture inspired by a formal description of human perception, which has been successfully employed in environmental monitoring and can be adapted to healthcare for better integration and comprehension of patient health data. The proposed architecture involves a three-layer ap-proach Sensors, Integration and Association and Application and Diagnostic, similar to the architecture used in forest fire monitoring. The Sensors layer describes state-of-the-art cutting-edge equipment and related sensors for gathering information. The second layer, Integration and Association propose an ontology and a potential solution for integrat-ing sensor information from the device to the cloud. Furthermore, the third layer, Application and Diagnostic gives a review of used machine learning algorithms for detecting and diagnosing various diseases. Finally, a case study of elderly health monitoring using wearable devices is presented, discussing the methodology for economic value calcu-lation to decrease the cost of efficient health management and improve quality of life. The use of a health monitoring observer network not only improves quality of life, but also reduces the cost of health monitoring, making it more sustainable for the general community.