Real-time Remote Monitoring System Research Articles

Acoustic ecology of Adélie penguins in the West Antarctic Peninsula

Adélie penguins (Pygoscelis adeliae) are bioindicators for the rapidly changing Antarctic environment, making understanding their population dynamics and behavior of high research priority. However, collecting detailed population data throughout the breeding season on many colonies is difficult due to Antarctica’s harsh conditions and remote location. The colonial breeding ecology of Adélie penguins has led to the evolution of a highly vocal species with individualized calls, making them well-suited for passive acoustic monitoring (PAM) with autonomous recording. PAM units can potentially provide an easily deployable and scalable way to collect fine-scale data on population estimates and breeding phenology. Here I present a framework for using acoustic indices to monitor phenology of dense penguin colonies even under high wind conditions. I evaluate the relationship between acoustic indices such as RMS amplitude and penguin colony size between distinct breeding stages (incubation, guard, crèche, and fledge) on Torgersen and Humble Islands in the West Antarctic Peninsula with an automated pipeline implemented in R. Using PAM to interpret penguin vocalizations for population size and breeding phenology estimates could lead to the development of a real-time remote monitoring system over a large spatial footprint, revealing Adélie penguin responses to climate change.

Real-Time Fault Identification of Photovoltaic Systems Based on Remote Monitoring with IoT

The increase in energy demand, as well as the need to protect the environment, has led to the promotion of new forms of generation, including photovoltaic energy. In this scenario, new challenges arise in the field of real-time monitoring of the characteristic variables of this type of system to determine correct operation. This paper presents the methodology of remote monitoring to detect faults in real-time in a photovoltaic system, taking advantage of the variables values that can be obtained from it, and estimating an operating state based on the behavior of these variables. The study used IoT technology for remote data acquisition, and by analyzing them, an estimate of the panel's operating status was made in real time by comparing the values of the variables registered. The study resulted in a real-time remote monitoring system that allows the estimation of the state of operation of a photovoltaic system and the classification of different types of failures that could occur in it. The study concludes that complex monitoring systems can be configured in real-time by technology based on IoT and with an adequate treatment of these variables, it is possible to estimate the photovoltaic systems' state of operation and identify electrical failures in them.

Advancing structural health monitoring: A vibration-based IoT approach for remote real-time systems

In the modern world, civil engineering structures are of paramount importance. Early damage detection through continuous health monitoring, instead of conventional periodic inspection, is the promising way forward for healthy structures with the least maintenance requirement. The damage is identified at the initial stage, so the cost associated with its repair can be reduced. Considering the immense scale of civil infrastructure, currently, available structural health monitoring systems are not feasible for mass application as they are prohibitively expensive. Hence, it is the need of the hour to develop a remote real-time Structural Health Monitoring System (SHMS). In the current study, a system is proposed that utilizes a vibration-based structural health monitoring approach and extracts dynamic parameters from acceleration data. The system consists of a node tasked with data collection and transmission and a server assigned with data post-processing and results display. The node comprises the following main hardware components: ESP32 as a microcontroller unit and ADXL345 as an accelerometer. The system was developed with the capability of multiple node integration, onboard data storage to eliminate sampling rate inconsistencies, dual trigger modes to achieve a high signal-to-noise ratio (SNR), power usage optimization to prolong stand-by time, and wireless data transmission along with automated post-processing. To minimize the cost of the system, Internet of Things (IoT) technology was adopted to develop the system.

Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases.

Telemedicine and remote patient monitoring (RPM) systems have been gaining interest and received adaptation in healthcare sectors since the COVID-19 pandemic due to their efficiency and capability to deliver timely healthcare services while containing COVID-19 transmission. These systems were developed using the latest technology in wireless sensors, medical devices, cloud computing, mobile computing, telecommunications, and machine learning technologies. In this article, a real-time remote patient monitoring system is proposed with an accessible, compact, accurate, and low-cost design. The implemented system is designed to an end-to-end communication interface between medical practitioners and patients. The objective of this study is to provide remote healthcare services to patients who need ongoing care or those who have been discharged from the hospital without affecting their daily routines. The developed monitoring system was then evaluated on 1177 records from MIMIC-III clinical dataset (aged between 19 and 99 years). The performance analysis of the proposed system achieved 88.7% accuracy in generating alerts with logistic regression classification algorithm. This result reflects positively on the quality and robustness of the proposed study. Since the processing time of the proposed system is less than 2 minutes, it can be stated that the system has a high computational speed and is convenient to use in real-time monitoring. Furthermore, the proposed system will fulfil to cover the lower doctor-to-patient ratio by monitoring patients from remote locations and aged people who reside in their residences.

Design of Remote Real-Time Monitoring and Biofeedback System to Connect Patients and Clinicians During Orthopaedic Rehabilitation.

The ORME (Orthopaedic Rehabilitation for ME) is a comprehensive solution developed by NTR Biosensors to address the issue of uncertain weight monitoring during orthopaedic rehabilitation. It consists of multiple ultra-thin force resistance sensors-equipped insole, microelectronics for data processing and transmission, a dedicated smartphone app called ORME control app, and a cloud platform called ORME PRO for remote monitoring by clinicians. The system alerts patients and clinicians to overload events by offering real-time biofeedback and providing haptic and audible cues to correct gait during telemonitoring or telerehabilitation. This minimizes the risk of new injuries and prevents overloading-related setbacks during rehabilitation, while also enabling gait analysis and plantar pressure monitoring. The ORME & ORME PRO systems present an innovative solution to enhance home care, telemonitoring, and tele-orthopaedic rehabilitation outcomes, empowering patients and specialists with an effective tool to monitor and manage the telerehabilitation process and patient-reported outcomes.

Self-Powered Robotic Sensor for Remote and Real-Time Monitoring of Heavy Metal Ions

In the recent years, heavy metal pollution has tremendously increased worldwide due to natural as well as anthropogenic reasons. Heavy metals accumulate in the human body through drinking water and food, ultimately causing irreversible damage to the body. For most of the heavy metals, the World Health Organization (WHO) has set permissible limits from 0.003-3 ppm in drinking water, above which it is contaminated. In addition, chemical threats remain a major global concern for sampling and sensing factors in unsafe environments. Therefore, robotic hand-based chemical sensing platforms that can monitor hazardous pollutants in the real environment are essential. Although robotic hand-based chemical sensors show great potential to substantially prevent humans from chemical threats in the environment, applications are often limited by the requirement of high-power consumption. In this study, we have fabricated a self-powered triboelectric nanosensor, which is integrated with a robot platform with wireless transmission capability, to detect heavy metal ions in the environment through a rapid onsite detection mechanism. In this regard, a copper electrode modified by an ion selective membrane (ISM) is used as a solid triboelectric material and is integrated with a robotic hand. Since the ionophore selectively binds to the corresponding ions, the solid-liquid TENS could detect Cr6+, Pb2+, and As3+ ions at a wide range of concentrations by the periodic contact and separate motion of the sensor probe to lead (Pb2+), arsenic (As3+) and chromium (Cr6+) solutions. This realization of direct integration of self-powered sensors with robotics provides low-cost and automated chemical sensing mechanisms. It can be applied to various environments for on-site detection of hazardous analytes and security. Finally, a real time remote monitoring system allows the presentation of the environmental monitoring data on mobile devices and enables remote real-time monitoring of the hazardous pollutants.

A Prototype Robotics Wheelchair with An IOT Based Paralyzed Patient Health Care Monitoring System

This project presents an implementation of wearable, portable, low power consumption, real-time remote bio-signals monitoring system based on the internet of thing technology. This internet technology along with modern electronic devices could offer promising solutions in this field. Based on that, this project utilizes a web or mobile application as an IoT platform to monitor remotely the live pulse rate signal, BPM and the body temperature of patients. The signals are measured and processed by using a microcontroller-based device (Arduino). The main contribution of this paper is sending an electrocardiogram to a web or mobile phone to be watched by a doctor. If some abnormal conditions are detected, the IoT alert send to doctor and relations, This assists in heart diseases diagnosing before the worst case can happen. Finally, the obtained results of this project are illustrated on both smartphone and personal computer (PC) as well. Index Terms— Microcontroller, LCD, flex sensor, remote sensor, ultrasonic sensor ,temperature sensor , cloud connectivity, data processing ,IoT

Smart Hand-Free Trash Manager

Abstract: Indian Municipal waste management remains a main problem in urban areas, leading to consequential health and environmental issues. Therefore, trash bins are placed in many places to handle municipal solid waste, but dustbins can overfill and spread around the area, spoiling the environment, and creating disturbance to the public. Therefore, a real-time remote monitoring system is required that warns the level of garbage to the firm of waste management or municipality. The proposed structure consists of an ultrasonic sensor to measure the garbage level in the dustbin. ESP32 is a microcontroller board with integrated Wi-fi and Bluetooth that controls the system operation. It is supposed to display messages on the IOT Cloud server regarding the garbage level of the dustbin, whether it is full or empty, so the municipality or respective garbage monitoring company can take required actions in time. Also, there will be a Solar panel to generate the power required to run the system. Furthermore, a system is proposed to contribute to improving the efficiency of solid waste disposal management.

Development of a Soft Sensor Using Machine Learning Algorithms for Predicting the Water Quality of an Onsite Wastewater Treatment System.

Developing advanced onsite wastewater treatment systems (OWTS) requires accurate and consistent water quality monitoring to evaluate treatment efficiency and ensure regulatory compliance. However, off-line parameters such as chemical oxygen demand (COD), total suspended solids (TSS), and Escherichia coli (E. coli) require sample collection and time-consuming laboratory analyses that do not provide real-time information of system performance or component failure. While real-time COD analyzers have emerged in recent years, they are not economically viable for onsite systems due to cost and chemical consumables. This study aimed to design and implement a real-time remote monitoring system for OWTS by developing several multi-input and single-output soft sensors. The soft sensor integrates data that can be obtained from well-established in-line sensors to accurately predict key water quality parameters, including COD, TSS, and E. coli concentrations. The temporal and spatial water quality data of an existing field-tested OWTS operated for almost two years (n = 56 data points) were used to evaluate the prediction performance of four machine learning algorithms. These algorithms, namely, partial least square regression (PLS), support vector regression (SVR), cubist regression (CUB), and quantile regression neural network (QRNN), were chosen as candidate algorithms for their prior application and effectiveness in wastewater treatment predictions. Water quality parameters that can be measured in-line, including turbidity, color, pH, NH4+, NO3-, and electrical conductivity, were selected as model inputs for predicting COD, TSS, and E. coli. The results revealed that the trained SVR model provided a statistically significant prediction for COD with a mean absolute percentage error (MAPE) of 14.5% and R2 of 0.96. The CUB model provided the optimal predictive performance for TSS, with a MAPE of 24.8% and R2 of 0.99. None of the models were able to achieve optimal prediction results for E. coli; however, the CUB model performed the best with a MAPE of 71.4% and R2 of 0.22. Given the large fluctuation in the concentrations of COD, TSS, and E. coli within the OWTS wastewater dataset, the proposed soft sensor models adequately predicted COD and TSS, while E. coli prediction was comparatively less accurate and requires further improvement. These results indicate that although water quality datasets for the OWTS are relatively small, machine learning-based soft sensors can provide useful predictive estimates of off-line parameters and provide real-time monitoring capabilities that can be used to make adjustments to OWTS operations.

Passive IoT Optical Fiber Sensor Network for Water Level Monitoring with Signal Processing of Feature Extraction

This paper presents a real-time remote water level monitoring system based on dense wavelength division multiplexing (DWDM)-passive optical fiber sensor (OFS) network for the application of the Internet of Things (IoT). This network employs a broadband light source based on amplified spontaneous emission (ASE) as a seed light. This ASE light is spectrum-sliced by an athermal type arrayed waveguide grating (200 GHz × 16 channel), then distributed towards multiple sensing units (SU). Here, 16 SUs are installed vertically at the specified height in the water pool according to the design specification (i.e., spatial resolution). Then, each SU reflects an optical spectrum having a different reflection coefficient depending on the surrounding medium (e.g., air or water). By measuring these reflected optical spectra with an optical spectrum analyzer, the water level can be easily recognized in real time. However, as the sensing distance increases, system performance is severely degraded due to the Rayleigh Back-Scattering of the ASE light. As a result, the remote sensing capability is limited at a short distance (i.e., <10 km). To overcome this limitation, we propose a simple signal processing technique based on feature extraction of received optical spectra, which includes embedding a peak detection algorithm with a signal validation check. For the specific, the proposed signal processing performs the peak power detection, signal quality monitoring, and determination/display of the actual water level through three function modules, i.e., data save/load module, signal processing module, and Human–Machine Interface display module. In particular, the signal quality of the remote sensing network can be easily monitored through several factors, such as the number of spectral peaks, the wavelength spacing between neighboring peaks and the pattern of detected peak power. Moreover, by using this validation check algorithm, it is also possible to diagnose various error types (such as peak detection error, loss of data and so on) according to the pattern of measured optical spectra. As a result, the IoT sensor network can recognize 17 different level statuses for the water level measurement from a distance of about 25 km away without active devices such as optical amplifiers (i.e., passive remote sensing).

Internet-based centralized remote real-time long-span bridge health monitoring system

The safe operation of bridges is a major issue related to the national economy and people’s livelihood. With the rapid development of the water transport industry, the problems of ship yaw and bridge collision avoidance occur frequently, which cause serious harm to the bridge structure and safety. However, the current bridge health monitoring system is difficult to feed back the monitored information in time, and in the process of collecting information by wireless sensors, information errors or missing problems easily occur, which hinders the normal monitoring of bridge health. With the rapid development of the Internet and the gradual maturity of wireless sensor technology, how to achieve complete and effective collection and feedback of monitoring information has become a hot research topic and an urgent problem. Therefore, on the basis of Internet technology, this study perceives, collects and processes the ship information in the bridge monitoring area covered by the network through the wireless sensor network, uses the embedded wavelet neural network model to denoise the monitoring data, and finally transmits it to the data processing center, thus establishing a centralized remote real-time Long-span bridge health monitoring system based on the Internet. The experimental results show that the monitoring deformation displacement curve based on the Internet centralized remote real-time long-span bridge health monitoring system technology presents a stable fluctuation state. The cumulative shape variable fluctuates in the range of -5 mm to 5 mm, indicating that the deformation trend of the bridge has always existed.

The internet of things healthcare monitoring system based on MQTT protocol

In overpopulated nations, the need for medical treatment is increasing along with the population; healthcare difficulties are becoming more common. The population's need for high-quality care is growing despite decreasing treatment costs. Because of technological improvements, a machine may remotely monitor health, which is more reliable than manual monitoring. The time required for individualized training may be shortened, and the dependability of complicated equipment may be increased. This study recommends a real-time remote patient monitoring system based on the Internet of Things (IoT) to assure the accuracy of the vital real-time signal. The vital real-time signal is sent from the proposed method to the website using the Message Queuing Telemetry Transport (MQTT) protocol. This work aims to read and analyze patients’ vital signs and reduce the latency while transmitting the signals.

Research on internal force of 3-D modeling of fish belly beam supported in foundation pit based on computer intelligent stress monitoring system

High prestressing force stiffness fish belly beam tool type combined internal support system, referred to as IPS construction method, is a new type of internal support structure system for deep foundation pit support, which is developed based on the principle of prestress and aiming at the shortcomings of traditional concrete internal support and steel support through a large number of engineering research and practical application, it is composed of fish belly beam (high-strength and low relaxation steel strand as the upper chord member. H-shaped steel as the stress beam, and H-shaped steel support beam with different length, butt brace, corner brace, column, cross beam, pull rod, triangular joint, preloading and jacking device and other standard components, and prestress is applied to form a plane restressed support system and three-dimensional structure system. Compared with the traditional concrete internal support and steel support, it greatly improves the overall stiffness and stability of the support system. Combined with the remote real-time monitoring system, it can effectively and accurately control the displacement of the foundation pit and greatly reduce the deformation of the foundation pit. This technology has made a major breakthrough in the internal support technology of deep foundation pit support. It is the most advanced internal support technology in the world. In this paper, a 3-D model of fish belly beam foundation pit support system is established completely, the stress characteristics of the support system are analysed by computer intelligent stress monitoring system, and the distribution areas of prestress and internal support stress are summarized, which can be used as a reference for this type of engineering experience in the future.

Design and Application of IOT-Based Real-Time Patient Telemonitoring System Using Biomedical Sensor Network

The field of Internet of Things (IoT) and remote patient monitoring (RPM) system has been developing rapidly causing these technologies to be adopted in multiple domains all around the world. This paper presents the design of a real-time remote monitoring system for vulnerable patients. The proposed device measures body temperature, heart rate, blood pressure, oxygen saturation level and electrocardiogram signals (ECG) of patients admitted into intensive care unit (ICU) or operating room (OR), and automatically updates all these data on a corresponding hybrid cloud server in every 45 s to which a corresponding physician has access. More than 2000 cycles are conducted within a day where all five biomedical parameters are measured and stored on each cycle. Furthermore, the system includes alarm triggering system and emergency short message service (SMS) alert service using GSM technology to initiate proper control actions in case of emergencies. This device facilitates doctors, nurses, relatives of patients, or anyone around the world to monitor real-time parameters of patients. The process was tested on 10 patients obtaining a satisfactory outcome. The system proposed in this paper reveals a new vision of biomedical engineering which tends to the era of remotely controllable ICU environment with more interactive, efficient and fast response of both patient and corresponding physicians.

Real-time remote monitoring and control system for underground pipelines

Underground pipelines suffer from corrosion in soil layers, and this corrosion is accelerated with the increasing of soil thickness due to more water contained. Cathodic protection (CP) is one of the most common methods for controlling corrosion of metals. Its popularity returns to the fact that CP system is simple, cheap, and suitable for many industrial applications. The drawback of the available CP systems is the need to go to the site for gathering data using classical instruments and methods, which is tedious, dangerous, uneconomic, and inaccurate. The main objective of this paper is to present a real-time remote monitoring and control (RT-RMC) system for any CP platform. The work started with implementation of an industrial-like CP prototype to realize the desired task. The implemented CP system consists of two famous CP methods, the sacrificial anodes (SACP) and the impressed current (ICCP). After that, the RT-RMC system is implemented with two techniques, global system for mobile communications(GSM), and web of things (WoT) to facilitate monitoring and control tasks. Experimental results are obtained for voltage and current measurements with different environments, disturbance, and pipe coatings.

Wireless sensor network enabled real-time remote intelligent health monitoring and management system using Internet of Things (IoT) sensing with cloud-based processing during covid-19 situation

Mankind was living quite an ordinary life when Covid 19 pandemic struck. The whole world was in turmoil and was busy trying to make the situation normal again. But it was impossible to regain the old scenario and people had to accept the new normal. The new normal demands people to follow different guidelines out of which maintaining a social distance of 6 feet was a prominent one. Even in this situation occurrence of any disease does not stop and there are always some patients visiting a doctor. Also, a doctor doesn't always have the luxury to visit every place to see patients, especially in rural areas where there is a transportation problem. So we have come up with a cloud-based system that will use the internet of things to diagnose a patient. This device will contain different sensors like temperature sensor, body oxygen level sensor, blood pressure sensor, heart rate sensor, and height and weight measuring gadgets to measure the body parameters of the patient and then store this information in the secured cloud which can then be accessed by the doctor to diagnose the patient. The sensors will be embedded in an Arduino and it will be connected to the cloud wirelessly with the help of a GSM module and node MCU. Also, a laptop will be present to connect the patient and the doctor in video mode for conversations. This system will also generate a prescription provided by the doctor which can be used anywhere. Thus, this device will not only promote social distancing but also it will prohibit the spread of diseases that are communicable. The doctors can work from the comfort of their home without touching a patient and also without traveling long distances to remote locations.

Design of Remote Real-Time Monitoring and Control Management System for Smart Home Equipment Based on Wireless Multihop Sensor Network

With the intensification of the pace of modern urban life and the increase of work pressure, people have higher requirements for the safety and comfort of home. However, the rapid development of Internet of Things technology, wireless communication technology, embedded technology, and data fusion technology has made this desire possible, thus making intelligent and safe. Using wireless sensor network, intelligent home system can realize the real-time collection of object location and state information, remote operation of home equipment, as well as the monitoring of the information of personnel activities, etc. However, how to efficiently analyze and integrate these information to be applied to the intelligent home system is one of the problems that need to be solved; further study of the smart home system of personal behavior identifies fire monitoring and remote monitoring of mining applications. Such information collection and integration of technology and method are given based on wireless sensor network transfer of key technologies; intelligent home security monitoring system based on Bayesian networks is proposed and a card. According to the theoretical fire monitoring method, and based on the Bayesian belief network personal behavior identification model, this paper provides the theory and method for the remote safety monitoring and control in the intelligent home environment. In this paper, based on the multisensor data fusion of smart home security, real-time monitoring alarm platform is built. Through the multisensor detection module for the home fire gas human infrared monitoring, the detected data is transmitted to the data fusion center for processing; processed results are uploaded to the cloud through the Internet. When abnormal conditions occur, users can log in to the mobile phone client to view abnormal alarm results.

Smart trash bin level monitoring system

Solid waste management is still a big issue in cities, causing considerable health and environmental problems. Trash cans are strategically distributed across the city to handle solid waste. These containers have the potential to overflow, contaminating the environment and creating trouble for people. A real-time remote monitoring system that informs a waste management firm or the relevant authorities of the level of rubbish in trash bins is required. The proposed system describes the creation and validation of a self-powered, LoRaIoT-enabled Smart Trash Bin Level Monitoring System for effective solid waste management (IoT-STBLMS). Smart Trash Bin Level Measurement Unit (STBLMU) monitors the unfilled level and geographical position of a trash bin, then analyses and communicates the data to a cloud platform using the MQTT protocol. A dashboard allows users to examine and evaluate the status of each bin as well as its geo-location. The designed system's accuracy, average current consumption and life expectancy, battery charging time, and cost were investigated and published here.

Remote Real-Time Monitoring System for Mine Slope Based on Cloud Computing

A remote real-time monitoring system was developed for the remote real-time monitoring of mine slope deformation and internal forces. The system is based on cloud-computing technology and a 5S multimedia streaming monitoring data transmission system. It has now been applied to an open-pit iron mine in Nanfen to monitor the horizontal displacement of potential sliding surfaces. Compared with geometric monitoring methods in traditional geodetic surveying, the results show that this monitoring system has higher accuracy (0.01 mm) and the ability to monitor in real time for 24 h. These features can realize early warnings of mine slopes from multiple dimensions, and provide a guarantee for safe, continuous and the efficient production of mines. In addition, it can provide a certain reference for the prevention and control of mine slope disasters, and is beneficial to the application of information-based early warning technology.

Design of AGV Fault Diagnosis Data Acquisition System Based on Cloud Platform

With the development of industrial Internet of Things technology, online fault diagnosis system has become an important part of remote operation and maintenance. Aiming at the problem that the AGV online fault diagnosis system requires higher real-time data, more data volume and faster transmission rate, this paper proposes a cloud platform-based AGV fault diagnosis data acquisition system. The system uses the STM32 single-chip microcomputer to collect the sensor data carried by the AGV, uses the wireless WIFI module to upload the data to the cloud server, and finally saves the data to the cloud database after conversion and verification, which provides a test platform for the Web remote real-time monitoring and online fault diagnosis system. By building an AGV test platform to conduct experiments, the results show that the throughput rate of the system is 90PPS, and the packet loss rate is within 0.1%. The system can meet the data requirements of the online fault diagnosis system, and has good real-time performance and stability.