Real-time Remote Monitoring System Research Articles

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.

A Smartphone Based Application for Medical Assistance of Elderly Patients

The importance of a routine health check-up for elderly patients, as well as the documentation of that check- up, cannot be overstated. Maintaining adequate documentation for a patient by himself or by his attendance, on the other hand, is extremely difficult. This paper primarily focuses on developing a framework that includes a smartphone application and introduces a real-time remote monitoring system for basic medical treatment of an elderly patient. It can keep track of a patient’s emergency contacts. Using the SMS manager API, emergency contacts can receive messages with the patient’s daily health alerts. Furthermore, the system will keep track of the patient’s symptoms, and if anything goes wrong for a continuous period of time, the system will automatically send out reminders to schedule medical appointments. For medical appointments, a patient may be able to search for nearest doctors and schedule an appointment for him via the system. Here, Google map technology is used to search nearby doctors. A reminder is introduced in this system where all of the activities for a patient are notified on a regular basis.

Remote Real Time Monitoring System of Slope and Its Application in Sustainable Mining of Open Pit Coal Mine

The mining of open-pit coal resources will form a large number of mine slopes and the geological hazard of slope landslide restricts the sustainable production of open-pit coal mine, monitoring and early warning of landslide disasters can reduce the losses caused by landslide disasters but now the most widely used displacement monitoring technology can not achieve the function. Based on the mechanism of landslide mechanics, the new remote real time monitoring method is proposed and its equipment system is developed, the remote real-time monitoring and early warning work of Antaibao open-pit coal mine slope is carried out. The monitoring locations are determined by finite difference numerical simulation method, through the remote monitoring results and slope stability analysis indicate that the slope is at a stable state. It has been proved by engineering practice that the monitoring of slope mechanics can ensure security of the continuous mining of the mine.

IoT-enabled cloud-based real-time remote ECG monitoring system

Statistical reports all around the world have deemed cardiovascular diseases (CVDs) as the largest contributor to the death count. The electrocardiogram (ECG) is a widely accepted technology employed for investigation of CVDs of the person. The proposed solution deals with an efficient internet of things (IoT) enabled real-time ECG monitoring system using cloud computing technologies. The article presents a cloud-centric solution to provide remote monitoring of CVD. Sensed ECG data are transmitted to S3 bucket provided by Amazon web service (AWS) through a mobile gateway. AWS cloud uses HTTP and MQTT servers to provide data visualisation, quick response and long-live connection to device and user. Bluetooth low energy (BLE 4.0) is used as a communication protocol for low-power data transmission between device and mobile gateway. The proposed system is implemented with filtering algorithms to ignore distractions, environmental noise and motion artefacts. It offers an analysis of ECG signals to detect various parameters such as heartbeat, PQRST wave and QRS complex intervals along with respiration rate. The proposed system prototype has been tested and validated for reliable ECG monitoring remotely in real-time.

A LoRaWAN IoT-Enabled Trash Bin Level Monitoring System

Municipal solid waste management remains a major problem in urban areas, leading to serious health and environmental issues.Consequently, trash bins are placed in many places to handle the municipal solid waste, but these bins can overflow, spreading around the area, polluting the environment, and causing inconvenience to the public. Therefore, there is a need for a real-time remote monitoring system that alerts the level of garbage in the trash bins to the municipality or a waste management company. To manage the municipal solid waste efficiently, this article presents the development and validation of a self-powered, LoRaWAN Internet-of-Things (IoT)-enabled trash bin level monitoring system. The end nodes of the proposed IoT system are called trash bin level measurement unit (TBLMU) and are installed in each trash bin where the status needs to be monitored. The TBLMU measures the unfilled level and geographical location of a trash bin, processes the data, and transmits it to a LoRaWAN gateway at a frequency of 915 MHz. A LoRaWAN gateway serves as a concentrator for the TBLMUs and relays data between a TBLMU and an IoT trash bin level monitoring server. The users can view and analyze the status of every bin and its geolocation by using a smart graphical user interface. The accuracy of the developed system, wireless range between a TBLMU and a LoRaWAN gateway, average current consumption and life expectancy of the TBLMU, battery charging time, and the cost were studied and are reported here.

Experimental research on remote real-time monitoring of concrete strength for highrise building machine during construction

It is critical to evaluate the strength of concrete structures during construction. Here, we propose a remote real-time monitoring system (RRTMS) and method for determining concrete strength. The RRTMS includes wireless monitoring modules based on IoT and remote analysis software based on cloud services. The corresponding method introduces one concept of reference conditions and reference tests. A case study of a 250 m building during construction using a highrise building machine (HBM) is presented to illustrate the RRTMS and method. A series of laboratory tests and field tests for real-time monitoring of concrete strength was conducted. Experimental findings show that the strength of the concrete structure can be monitored remotely in real time through the RRTMS, which accomplishes the safe climb of the HBM during construction. The application results further indicate that the RRTMS and method can achieve nondestructive, high-precision and real-time monitoring of the strength of concrete structures.

Age-Energy Tradeoff for Two-Hop Status Update Systems With Heterogeneous Truncated ARQ

Age of information (AoI) and energy cost (EC) are two key metrics in real-time remote monitoring systems. In this letter, we investigate the age-energy tradeoff for a short packet-based two-hop status update system, where the packet transmission may fail because of channel errors. In this case, the source can either retransmit the failed update to save EC for sensing, or generate a new one to maintain the AoI. Accordingly, we consider the truncated automatic repeat request (ARQ) scheme in both the first and second hops with different retransmission thresholds. Closed-form average AoI and average EC are given as a function of the failure probability and the thresholds of two hops. Moreover, a weighted sum of AoI and EC is minimized to tradeoff the age and energy. Numerical results demonstrate that increasing the difference between the thresholds of two hops can reduce the average AoI but increase the average EC, and an optimal packet length can always be identified to achieve age-energy tradeoff.

IoT-based GPS assisted surveillance system with inter-WBAN geographic routing for pandemic situations

BackgroundWorldwide pandemic situations drive countries into high healthcare costs and dangerous conditions. Hospital occupancy rates and medical expenses increase dramatically. Real-time remote health monitoring and surveillance systems with IoT assisted eHealth equipment play important roles in such pandemic situations. To prevent the spread of a pandemic is as crucial as treating the infected patients. The COVID-19 pandemic is the ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). MethodsWe propose a surveillance system especially for coronavirus pandemic with IoT applications and an inter-WBAN geographic routing algorithm. In this study, coronavirus symptoms such as respiration rate, body temperature, blood pressure, oxygen saturation, heart rate can be monitored and the social distance with ‘mask-wearing status’ of persons can be displayed with proposed IoT software (Node-RED, InfluxDB, and Grafana). ResultsThe geographic routing algorithm is compared with AODV in outdoor areas according to delivery ratio, delay for priority node, packet loss ratio and bit error rate. The results obtained showed that the geographic routing algorithm is more successful for the proposed architecture. ConclusionThe results show that the use of WBAN technology, geographic routing algorithm, and IoT applications helps to achieve a realistic and meaningful surveillance system with better statistical data.

Feasibility of home-based cardiac rehabilitation using an integrated telerehabilitation platform in elderly patients with heart failure: A pilot study

BackgroundCardiac rehabilitation (CR) is a widely recommended evidence-based intervention for patients with cardiovascular disease. However, the participation rate in CR has been reported to be low globally, mainly due to barriers in access to the CR center. We evaluated the feasibility and safety of a new remote real-time monitoring system for supervising home-based CR among elderly patients with heart failure (HF). MethodsHospitalized patients or outpatients followed for HF were enrolled. Patients received 12-week home-based CR under remote supervision using an integrated platform for telerehabilitation. Feasibility was evaluated by the participation and completion rates of the home-based CR sessions. Safety was assessed by adverse events during the sessions. All patients underwent baseline and 12-week assessment of exercise tolerance and lower extremity muscle strength. ResultsAll 10 patients (mean age 76 ± 7 years; 60% male) who participated in the study completed the program without withdrawal during the study period. Median participation rate in the exercise sessions was 94.4% (interquartile range: 88.9–97.9%). While fatigue, common cold, and palpitation were observed, no serious cardiovascular events were reported. Six-minute walk distance significantly improved from 383 ± 94 m to 432 ± 83 m (p=0.003). ConclusionsHome-based CR under real-time supervision was feasible and safe among elderly HF patients. Our study suggests that home-based CR using an integrated telerehabilitation platform may be a potential option for patients who are unable to participate in center-based CR due to geographic or social accessibility and physical barrier issues.

Raspberry Pi Based IoT for Aquaculture Realtime Remote Monitoring System with Self Energy Harvesting

Temperature and pH are two essentials water quality parameters in shrimp culture. Therefore, monitoring these two variables is very important to improve productivity in aquaculture. In this study, a monitoring system was created to monitor the two variables in the pond. For this reason, Raspberry Pi IoT based hardware and software has been made to carry out the monitoring function. Monitoring data is automatically sent to online servers using a cellular internet network. For electrical power needs, remote devices are equipped with solar power plants. The results showed that the system could read the temperature and pH of the water accurately and sent it to an online server. There is a web-based calibration menu that allows users to easily calibrate sensors. Moreover, a user interface is also made to display the sensor readings data that has been stored in the database. In conclusion, the monitoring system is applicable for water quality data logger in the field operation with user friendly operation.

Real-time Remote Monitoring and Security System for a Local Area Network Environment

Real-time Remote Monitoring and Security System for a Local Area Network Environment

Sound-based remote real-time multi-device operational monitoring system using a Convolutional Neural Network (CNN)

Smart factory is the main keyword in the field of manufacturing processes about the fourth industrial revolution. To realize the smart factory, making all pieces of device into smart devices that are connected to the centralized system to enable a real-time exchange of information is essential. Sound can be efficient means to make devices as smart devices because sound can contain the status information of various devices simultaneously, and it can be recorded easily outside of a device using only a microphone. In this study, multi-device operation monitoring system by analyzing sound is developed. Mic arrays for acquiring the sound were installed at the outside the devices and recorded the sounds from several devices simultaneously. By analyzing the recorded sound with log-mel spectrogram and Convolutional Neuron Network (CNN), the system could detect the operational status of three devices with an accuracy of 71–92 %. To improve the performance, virtual data set was created by composition of individual device operating sounds of different intensities. With this virtual data set, accuracy can be enhanced to 87 % ∼ 99 % accuracy and, required sound data amount could be reduced. Developed system was applied successfully in monitoring experiments in two different environments: a workshop in which hand-operated device was used and a factory with a computer numerical control machine and verifying the performance.

Design of a Remote Real-time Groundwater Level and Water Quality Monitoring System for the Philippine Groundwater Management Plan Project

Design of a Remote Real-time Groundwater Level and Water Quality Monitoring System for the Philippine Groundwater Management Plan Project

Real-Time Monitoring of Frost/Defrost Processes Using a Tapered Optical Fiber

A tapered fiber sensor was developed to detect the presence of condensed water and frost. The rough surface of the tapered fiber efficiently scattered the light propagating through the tapered fiber. The scattering loss is selective to the medium surrounding the tapered region of the fiber. Compared to the transmission intensity in the air, liquid water on the tapered fiber causes a 40% increase in transmission, whereas the presence of frost decreases the light transmission by 15%. Frost induces roughness on the surface of the tapered fiber and as a result increases light scattering and transmission losses. In contrast, liquid water uniformly covers the tapered fiber surface and reduces the light scattering. Furthermore, the amount of frozen water on the tapered surface is related to the duration of the transmission intensity peak during the defrosting process. The proposed detection scheme based on a rough tapered fiber can be utilized in real-time remote monitoring systems of the frost formation in large refrigeration systems.

An IoT-based bin level monitoring system for solid waste management

Improper disposal of solid waste that impacts human health and pollutes the environment, arising a need for successful and necessary collection of waste materials. However, most trash bins placed in cities can be seen overflowing due to traditional or inefficient waste management approaches. Therefore, a real-time remote monitoring system is needed to alert the level of garbage in bins to the relevant authority for immediate waste clearance. This paper presents the development and validation of a self-powered, simple connect, IoT solution to monitor the unfilled level of trash bins from a central monitoring station. The end sensor nodes of the developed IoT system are called Bin Level Monitoring Unit (BLMU) which are installed in every trash bin where the unfilled level needs to be monitored. Every BLMU measures the unfilled level of the trash bins and transmits it to a wireless access point unit (WAPU). Each WAPU receives the unfilled level data from several BLMUs and uploads it to the central server for storage and analysis. The waste collection authority can view and analyze the unfilled level of each bin using a smart graphical user interface. The following important experiments were carried out to validate the developed system: (1) the developed bin level monitoring system was tested by filling a trash bin with solid waste at various levels, and the corresponding unfilled level of the trash bin was monitored using the smart graphical user interface. (2) The life expectancy of the BLMU was evaluated as approximately 434 days. (3) The maximum transmission distance between a BLMU and a WAPU is 119 m. (4) The cost of a developed trash bin is 107 USD. Based on the results achieved, the developed trash bins can be suitable for smart cities.

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT.

Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.

Research on Monitoring Method of Impulse Vibration in Large Transformer Transportation

The safety of long-distance transportation of large power transformers has become an important constraint factor for the EHV projects. In view of the problems existing in the Three-dimensional impact recorders and condition monitoring system of the long-distance transportation of large power transformers, this paper analyzes the amplitude frequency characteristics and influencing factors of the impact vibration during the transportation of transformer, adopts the fast Fourier transform to deal with the interference in the vibration signal, designs a monitoring terminal of the impact vibration based on the Internet of things which can accurately measure the vibration impact of the transformer, and develops a remote real-time online monitoring system to monitor the displacement and impact of transformers at anytime and anywhere. The monitoring terminal and monitoring system are applied in the actual transportation of converter transformer in an UHVDC project. Three monitoring terminals are installed in different positions of the transformer to monitor the transformer status and send the monitored information to the monitoring system. The reliability of the terminal and the superiority of the monitoring system are verified.

Design of Mobile Healthcare Monitoring System Using IoT Technology and Cloud Computing

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 implementation provides an improved step-in remote health monitoring field. Numbers of people, who require health care increase year by year and the conventional bio-signals monitoring systems require patients’ attendance in person inside hospitals. This might cause an inefficient situation to take care of the patients, especially those who have critical and unstable health conditions. Therefore, internet technology along with modern electronic devices could offer promising solutions in this field. Based on that, this project utilizes a mobile application as an IoT platform to monitor remotely the live ECG signal, heart rate, SPO2, 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 (ECG signal) to a specific smart mobile phone to be watched by a doctor. 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.

Development of a New Multifunctional Induced Polarization Instrument Based on Remote Wireless Communication Technology

Induced polarization (IP) is the primary method used for the exploration of various metal minerals and for groundwater prospecting. The function and performance of IP instruments have significantly improved over the years; however, most instruments still have some shortcomings, such as limited data acquisition accuracy, large size, separate transmitter and receiver, complex human-computer interaction, difficult networking and remote monitoring. In this study, a novel multifunctional IP instrument system is developed based on remote wireless communication technology, which has high data acquisition accuracy, small size, lightweight, flexible and diverse functions (single instrument can realize transmission and reception), remote wireless real-time monitoring, and convenient networking of multiple instruments. We independently designed a high-precision data acquisition circuit with a noise floor of 0.29 μV and a 5-fold increase in acquisition accuracy. With an intelligent power module as its core, the designed transmitter has a maximum output power of 6 kW and can transmit stepped waves and rectangular waves of various frequencies and duty cycles. We also developed a host computer program on an Android mobile terminal based on Java, which allows users to wirelessly control the instrument through mobile phones over Wi-Fi networks. In addition, we introduced the Internet of Things technology in geophysical instruments and designed a real-time remote monitoring system for data quality and multiple instruments networking scheme based on the short message service of the BeiDou navigation satellite system and cloud service platform. Multiple tests revealed that the proposed instrument meets the requirements of field exploration.

Motion Recognition-Based Robot Arm Control System Using Head Mounted Display

This paper proposes a novel remote monitoring system using a head mounted display (HMD). By using an HMD, a high degree of immersion and the sense of reality is provided to the user. Further, it becomes more convenient to move the camera position while controlling the robot arm on which the camera is mounted through the coordinates obtained from the HMD sensors. The proposed system performs two tasks. Initially, in order to render images in the HMD, the camera captures input images, and the PC connected to the robot arm sends the image to the PC connected to the HMD in real-time after which the image is rendered in the HMD. For the real-time monitoring, we increased the frame per second by reducing the data size. In order to reduce the data size, we define region of interest which is the region a user can see. Then, region of interest of the image is cropped and the resolution of entire image is degraded. Therefore, two images composed of cropped image and degraded image are transmitted and merged into an image. In this way, we can reduce the data size and provide the user with monitoring the image of the original quality. Next, using the inertial measurement unit sensor and base station sensors built into the HMD, the user controls the displacement of the camera during rotation and translation by controlling the robot arm by the user’s own motion. The PC connected to the HMD transmits the motion coordinates of the HMD acquired from the sensor into the PC connected to the robot arm. To control the translation of the camera, we define a coordinate system that represents the y- axis as running from top to bottom, and the x- axis as running from the front to the back in three-dimensional space. Thus, each step motor along each axis, which controls the robot arm, has its movement controlled by supplying appropriate values of angular degrees. Through the above process, the proposed method implements a remote monitoring system with five degrees of freedom. In the experiment, we measured the data transmission delay time and the displacement error due to the robot arm’s motion control to determine whether the system is suitable for a real-time remote monitoring system. Experimental results show that the proposed system can be optimally operated with a frame rate of 15 frames per second and a group of pixel resolutions of $640\times 640$ of the cropped image and $240\times 120$ of the degraded image. In addition, the average error rate of the robot arm’s displacement was 6.45% when the camera position was controlled through the robot arm.