Implementation Of Monitoring System Research Articles

A PEER-TO-PEER AIR QUALITY MONITORING SYSTEM USING IOT SENSORS AND CLOUD PLATFORMS

This paper presents the design and implementation of an air quality monitoring system that combines Internet of Things (IoT) sensors, cloud computing platforms, and peer-to-peer (P2P) networking. The system uses IoT sensors to collect real-time air quality data from multiple geographic areas. This data is sent to cloud platforms, including AWS IoT Core, for processing and storage. A P2P network of servers, with each server responsible for a specific area, enables distributed access to the air quality data. The system also incorporates machine learning models deployed on the Google Cloud Platform to provide air quality predictions. The proposed architecture for wide-area air quality monitoring is feasible and scalable based on experimental results.

Implementation and Performance Analysis of Temperature and Humidity Monitoring System for Server Room Conditions on Lora-Based Networks

Abstract – A server room is a room used to store applications, data and network devices such as routers. The server room is the data center of a company or institution in which there are applications and databases that store all important and valuable information for the company or institution concerned, therefore the server room must always be in good temperature conditions because the devices work for 24 hours no stopping. If there is a significant increase in temperature, it can cause system performance to be disrupted or hardware damage occurs. The cooler in the server room is not optimal because the cooler is often constrained by frequent power outages. From these problems, a solution is needed to be able to monitor the system remotely so that changes in room temperature can be seen in real time. The Internet of Things (IoT) is the solution to this problem, by using LoRa, which then sends the data to the Telegram application that has been installed on the smartphone so that real-time temperature changes can be obtained

Detection of Diabetes Using 5G Network

bstract: Recent advances in wireless networking and big data technologies, such as 5G networks, medical big data analytics, and the Internet of Things, along with recent developments in wearable computing and artificial intelligence, are enabling the development and implementation of innovative diabetes monitoring systems and applications. Due to the life-long and systematic harm suffered by diabetes patients, it is critical to design effective methods for diagnosing and treating diabetes. Based on our comprehensive investigation, this article classifies those methods into Diabetes 1.0 and Diabetes 2.0, which exhibit deficiencies in terms of networking and intelligence. Thus, our goal is to design a sustainable, cost-effective, and intelligent diabetes diagnosis solution with personalized treatment. In this article, we first propose the 5G-Smart Diabetes system, which combines state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes. Then we present the data sharing mechanism and personalized data analysis model for 5G-Smart Diabetes. Finally, we build a 5GSmart diabetes testbed that includes smart clothing, a smartphone, and a big data cloud. These experimental results show that our system can effectively provide patients with personalized diagnoses and treatment suggestions.

Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach

This work discusses smart building applications involving the Internet of Things (IoT) which are focused on energy consumption monitoring and forecasting systems, as well as indoor air quality (IAQ) control. Low-cost hardware integrating sensors and open source platforms are implemented for cloud data transmission, data storage and data processing. Advanced data analytics is performed by the seasonal autoregressive integrated moving average (SARIMA) method and a long short-term memory (LSTM) neural network with an accurate calculation performance about energy predictions. The proposed results are developed within the framework of the R&D project Data System Platform for Smart Communities (D-SySCOM), which is oriented to a smart public building application. The main goal of the work was to define a guideline-matching energy efficiency with wellness in public indoor environments, by providing modular low-cost solutions which are easily implementable for advanced data processing. The implemented technologies are suitable to define an efficient organizational user protocol based on energy efficiency and worker wellness. The estimated performance of mean square error (MSE) of 0.01 of the adopted algorithms proves the efficiency of the implemented building monitoring system in terms of energy consumption forecasting. In addition, the possibility of designing and implementing a modular low-cost hardware–software system was demonstrated utilizing open source tools in a way that was oriented to smart buildings approaches.

A Retrospective, Observational Study of Catheter-Associated Urinary Tract Infection Events Post-Implementation of a Novel Urinary Catheter System with Active Drain Line Clearance and Automated Intra-Abdominal Pressure Monitoring.

A quality improvement study to assess catheter-associated urinary tract infection (CAUTI) rate post-implementation of a bladder catheter with integrated active drain line urine clearance and automated intra-abdominal pressure monitoring in a burn intensive care unit (ICU). Eight-year retrospective before and after study (2015-2022). A single American Burn Association-verified Burn Center with 14 inpatient beds. Patients meeting criteria for admission to a Burn Center. Retrospective cohort study following the implementation of a novel urine output monitoring system with integrated drain line and urine clearance. Data from a 48-month (from January 2015-December 2018) historical control (period 1) were compared to data from a 28-month (from January 2020 to April 2022) post-implementation period (period 2). Pre- and post-implementation CAUTI event incidences were compared. Patients were transferred from outside hospitals with gravity bladder. A distinction in the chart between catheter types was impossible. Charts were reviewed to characterize patients with CAUTI events. A total of 42 CAUTIs in 2243 patients were identified using the National Health and Safety Network (NHSN) definition during the analyzed period. There were 40 CAUTI events in period 1 and two CAUTIs in period 2. The incidence of CAUTI events pre-implementation was 0.030 (mean of 10 CAUTI events per year) compared to 0.002 (mean of 1 CAUTI event per year) post-implementation of an automatic drain line clearing UO monitoring system showing a significant reduction in CAUTI events (p < 0.01, risk ratio novel vs. gravity bladder catheter 0.071, 95% confidence interval: 0.017-0.294). CAUTIs were reduced in the period following the implementation of a novel urinary catheter system with an integrated active drain line and urine clearance in burn patients.

Prescribing Pattern of Benzodiazepine Receptor Agonists (BZRA) and Factors Associated With Duration of BZRA Use in a Malaysian Psychiatry Outpatient Clinic

Introduction: Benzodiazepine receptor agonist (BZRA) are among the most frequently used psychotropic medications worldwide. We aim to understand the pattern of prescription of BZRA in the government healthcare facilities and identify factors affecting the likelihood of BZRA prescription and duration of use. Method: This is a retrospective study. Data was obtained from record of outpatient clinical notes. Medications studied were midazolam, alprazolam, lorazepam, bromazepam, clonazepam, diazepam and zolpidem. Mean duration per prescription, mean dosage per prescription and duration per patient per year were calculated for each sedative hypnotic. The likelihood of factors affecting duration of prescription were also analysed. Results: The prevalence of sedative hypnotic use in psychiatry outpatient clinic was 12.16%. Clonazepam was found to have the longest duration per patient per year (306.5 days). Insomnia and anxiety are the two most common reasons for sedative hypnotic prescription. Factors found to affect duration of prescription were unemployment, borderline personality disorder, alcohol and substance use disorders. Conclusion: Implementation of effective monitoring system on sedative hypnotic prescribing and increase use of non-pharmacological interventions for insomnia and anxiety are necessary to curb prolonged use of sedative hypnotic.

Effectiveness of a flash glucose monitoring systems implementation program through a group and telematic educational intervention in adults with type 1 diabetes

ObjectivesVerifying the clinical effectiveness and the impact on quality-of-life parameters, fear of hypoglycaemia and satisfaction with the treatment obtained with a flash glucose monitoring (MFG) devices implantation program that includes a telematic and group educational intervention in adults with type 1 diabetes. Patients and methodsProspective quasi-experimental study, carried out during the COVID-19 pandemic period with a 9-month follow-up at the Virgen Macarena University Hospital, Sevilla. ResultsEighty-eight participants were included (men: 46.6%; mean age (years) 38.08, SD: 9.38); years of DM1 evolution: 18.4 (SD: 10.49); treatment with multiple doses insulin (MDI) 70.5% vs 29.5% subcutaneous insulin infusion therapy (CSII)). Baseline HbA1c was 7.74% (1.08). After the intervention, the global decrease in HbA1c was −0.45% (95% CI [−0.6, −0.25], P < 0.01), increasing to −1.08% in the group that started with HbA1c ≥ 8% (P < 0.01). A mean decrease in the Fear of Hypoglycemia 15 (FH15) test score of −6.5 points was observed (P < 0.01). In the global score of the Spanish version of Diabetes Quality Of Life (DQOL-s) test, the decrease was −8.44 points (P < 0.01). In Diabetes Treatment Satisfaction Questionnaire test (DTQ-s), global score increased in + 4 points (P < 0.01). ConclusionsThe incorporation of an educational program in group and telematic format within the development of MFG devices implantation strategies is an effective option, with associated benefits in quality of life and fear of hypoglycemia in adult patients with DM1. This option can be implemented in usual clinical practice.

Improvement of Performance in Freezing of Gait detection in Parkinson’s Disease using Transformer networks and a single waist-worn triaxial accelerometer

Freezing of gait (FOG) is one of the most incapacitating symptoms in Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. The presence of FOG may lead to falls and a loss of independence with a consequent reduction in the quality of life. Wearable technology and artificial intelligence have been used for automatic FOG detection to optimize monitoring. However, differences between laboratory and daily-life conditions present challenges for the implementation of reliable detection systems. Consequently, improvement of FOG detection methods remains important to provide accurate monitoring mechanisms intended for free-living and real-time use. This paper presents advances in automatic FOG detection using a single body-worn triaxial accelerometer and a novel classification algorithm based on Transformers and convolutional networks. This study was performed with data from 21 patients who manifested FOG episodes while performing activities of daily living in a home setting. Results indicate that the proposed FOG-Transformer can bring a significant improvement in FOG detection over the reproduction of related approaches based on machine and deep learning (i.e., from 0.916 to 0.957 in the AUC metric compared with the baseline, with a corresponding sensitivity, specificity, and precision of 0.842, 0.939 and 0.617, respectively) using a leave-one-subject-out cross-validation (LOSO CV). These results present opportunities for the implementation of accurate monitoring systems for use in ambulatory or home settings.

Environmental, human health and socioeconomic impacts of Ostreopsis spp. Blooms in the NW Mediterranean

This paper summarizes the research conducted by the partners of the EU co-funded CoCliME project to ascertain the ecological, human health and economic impacts of Ostreopsis (mainly O. cf. ovata) blooms in the NW Mediterranean coasts of France, Monaco and Spain. This knowledge is necessary to design strategies to prevent, mitigate and, if necessary, adapt to the impacts of these events in the future and in other regions.Ostreopsis proliferations in the Mediterranean have been related to massive mortalities of benthic organisms and to symptoms of respiratory and cutaneous irritation in humans. A six-year epidemiologic study in a Ostreopsis hot spot in Catalonia and the accumulated experience of the French Mediterranean National Ostreopsis Surveillance Network confirm the main effects of these blooms on human health in the NW Mediterranean. The impacts are associated to direct exposure to seawater with high Ostreopsis cell concentrations and to inhalation of aerosols containing unknown irritative chemicals produced under certain circumstances during the blooms. A series of mild acute symptoms, affecting the entire body as well as the ophthalmic, digestive, respiratory and dermatologic systems have been identified. A main remaining challenge is to ascertain the effects of the chronic exposure to toxic Ostreopsis blooms.Still, the mechanisms involved in the deletereous effects of Ostreopsis blooms are poorly understood. Characterizing the chemical nature of the harmful compounds synthesized by Ostreopsis as well as the role of the mucus by which cells attach to benthic surfaces, requires new technical approaches (e.g., metabolomics) and realistic and standardized ecotoxicology tests. It is also necessary to investigate how palytoxin analogues produced by O. cf. ovata could be transferred through the marine food webs, and to evaluate the real risk of seafood poisonings in the area. On the other hand, the implementation of beach monitoring and surveillance systems in the summer constitutes an effective strategy to prevent the impacts of Ostreopsis on human health.In spite of the confirmed noxious effects, a survey of tourists and residents in Nice and Monaco to ascertain the socioeconomic costs of Ostreopsis blooms indicated that the occurrence of these events and their impacts are poorly known by the general public. In relationship with a plausible near future increase of Ostreopsis blooms in the NW Mediterranean coast, this survey showed that a substantial part of the population might continue to go to the beaches during Ostreopsis proliferations and thus could be exposed to health risks. In contrast, some people would not visit the affected areas, with the potential subsequent negative impacts on coastal recreational and touristic activities. However, at this stage, it is too early to accurately assess all the economic impacts that a potentially increasing frequency and biogeographic expansion of the events might cause in the future.

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends.

Linear infrastructures, such as railways, tunnels, and pipelines, play essential roles in economic and social development worldwide. However, under the influence of geohazards, earthquakes, and human activities, linear infrastructures face the potential risk of damage and may not function properly. Current monitoring systems for linear infrastructures are mainly based on non-contact detection (InSAR, UAV, GNSS, etc.) and geotechnical instrumentation (extensometers, inclinometers, tiltmeters, piezometers, etc.) techniques. Regarding monitoring sensitivity, frequency, and coverage, most of these methods have some shortcomings, which make it difficult to perform the accurate, real-time, and comprehensive monitoring of linear infrastructures. Distributed acoustic sensing (DAS) is an emerging sensing technology that has rapidly developed in recent years. Due to its unique advantages in long-distance, high-density, and real-time monitoring, DAS arrays have shown broad application prospects in many fields, such as oil and gas exploration, seismic observation, and subsurface imaging. In the field of linear infrastructure monitoring, DAS has gradually attracted the attention of researchers and practitioners. In this paper, recent research and the development activities of applying DAS to monitor different types of linear infrastructures are critically reviewed. The sensing principles are briefly introduced, as well as the main features. This is followed by a summary of recent case studies and some critical problems associated with the implementation of DAS monitoring systems in the field. Finally, the challenges and future trends of this research area are presented.

MONITORING THE RESULTS OF DEVELOPING THE COMPETENCES OF HEALTHCARE MANAGERS AT ADDITIONAL PROFESSIONAL EDUCATION COURSES

The aim of the study was to develop a system for monitoring the results of the formation of universal and professional competencies of healthcare managers when studying at additional professional education courses. The objectives of the study included defining the stages of monitoring, developing a fund of assessment tools, criteria and assessment indicators for the competencies specified in the Federal State Educational Standards for Higher Education (FSES HE), implementing and testing the effectiveness of monitoring in the cycles of professional retraining of healthcare managers. The developed and implemented monitoring system has proved its effectiveness. The result of its application is obtaining the most accurate, objective and timely information about the level of development of universal and professional competencies of healthcare organizers in the process of training in professional retraining cycles. In the course of monitoring, reliable results were obtained to improve the level of competencies of healthcare managers related to organizational and managerial activities.

IoT-based drinking water quality measurement: systematic literature review

Sustainable development throughout the world depends on several factors such as the economy, quality education, agriculture, industry, among others, but the environment is one of the most important. Industrialization and new land use plans have caused the proliferation of pollutants in water resources, which poses a serious public challenge. As outlined in the sustainable development goals (SDGs), innovative water quality monitoring methods are needed to ensure access to water, sustainable management and sanitation. In this sense, technologies are sought that contribute to the development and implementation of groundwater and surface water quality monitoring systems in real time, so that their parameters can be evaluated through descriptive analysis, in rural populations and areas of difficult access. Nowadays, the internet of things (IoT) and the development of modern sensors are more used, so this research reviews the latest technologies to monitor and evaluate water quality using the potential and possibilities of the IoT. The main contribution of this article is to present an overview of the state of the art of IoT applications and instrumentation for water quality monitoring, focusing on the latest innovations, in order to identify interesting and challenging areas that can be explored in future research.

Implementation of IoT-Based Low-Delay Smart Streetlight Monitoring System

Smart streetlight is an outdoor infrastructure that uses technologies, such as sensors and actuators, to provide intelligent outdoor lighting and replace the power-consuming traditional streetlights. Although the current smart streetlight systems are employing these technologies for the maintenance, they simply gather data wirelessly on a periodical basis and still have drawbacks to provide real-time monitoring operation. To address these issues, the implementation of an IoT-based low-delay smart streetlight monitoring system is proposed in this article. In addition, a data filtering algorithm is also proposed in this article where redundant data are ignored to avoid overloading and excessive data storage consumption. Implementation results show that the proposed monitoring system and data filtering algorithm are able to provide real-time monitoring of smart streetlights with minimal time execution up to 0.11 ms and greatly reduce data storage usage up to 88.57%, respectively.

The Design and Implementation of an IoT Sensor-Based Indoor Air Quality Monitoring System Using Off-the-Shelf Devices

IAQ monitoring studies have gained renewed interest post COVID-19. Furthermore, accessibility to the corresponding enabling technologies has improved considerably in terms of cost and requisite knowledge. This paper aims to outline the key decisions involved for researchers and building managers alike to seek to implement their own environmental monitoring study using commercially available hardware and software. To do so, this paper first outlines the essential elements or building blocks of an IoT architecture, detailing the design criteria for selecting various hardware and software. Secondly, it outlines the process of integrating these different components and the flow of data from IoT device to databases and end-user applications. To demonstrate this process, an IAQ monitoring study was conducted at an open-plan office. Our results demonstrated the framework can be adapted to different worksites with minor modifications and provide the flexibility to interchange components. The data collected can be easily integrated into open-source analytic software for visualization and to make informed decisions to manage IAQ. It is through this process that we provide recommendations on how other users may adopt similar frameworks.

Design and Implementation of Environmental Monitoring System Based on Multi-Protocol Fusion Internet of Things

Internet of Things (IoT) is a highly integrated and comprehensive information technology that is currently a research hotspot. However, it presents many challenges; e.g., the use of multiple products in one IoT can lead to incompatible communication. It is imperative to solve the problem of communication protocol heterogeneity. In this study, a multi-protocol fusion IoT called MPFIoT was designed, implemented, and applied to environmental monitoring in a community. A hierarchical design was adopted in MPFIoT. It was divided into four layers: a data perception layer, a wireless transmission layer, a gateway layer, and an application layer. First, in the data perception layer, various sensors were used in the community to collect diverse environmental information. Second, in the wireless transmission layer, four wireless communication protocols were applied: Wi-Fi, LoRa, ZigBee, and Bluetooth. Third, the gateway layer had two sub-layers: the protocol fusion sub-layer was designed to reduce the degree of heterogeneity between protocols, and the control command sub-layer was used to control nodes via the control command frame. Finally, the application layer communicated with the gateway layer through the TCP/IP protocol. Thus, MPFIoT with four layers was developed, and its functions and performance were tested. The test results indicated that multiple types of environmental data can be collected and transmitted by MPFIoT. The packet loss ratio was less than 2% and the transmission delay was approximately 500 ms, which satisfied the application requirements.

Design and Implementation of Real-Time Kitchen Monitoring and Automation System Based on Internet of Things

Automation can now be found in nearly every industry. However, home automation has yet to reach Pakistan. This paper presents an Internet of Things smart kitchen project that includes automation and monitoring. In this project, a system was developed that automatically detects the kitchen temperature. It also monitors the humidity level in the kitchen. This system includes built-in gas detection sensors that detect any gas leaks in the kitchen and notify the user if the gas pressure in the kitchen exceeds a certain level. This system also allows the user to remotely control appliances such as freezers, ovens, and air conditioners using a mobile phone. The user can control gas levels using their phone with this system. In this paper, the ESP32, DHT11 Sensor, 5 V Relay X 8, and MQ-135 gas sensors create a smart kitchen by controlling the temperature, managing humidity, and detecting gas leakage. The system was built on an Arduino board that is connected to the Internet. The hardware was integrated and programmed using an Arduino, and a user Android application was developed. The project’s goal is to allow any Android smartphone to remotely control devices. This method is commonly used in homes, businesses, and grocery stores. Users will be able to control all of their instruments from anywhere, including switches, fans, and lights. Furthermore, simulation was performed using Matlab2016b on multiple houses. In the simulation, not only was the kitchen considered, but also two, four, and six houses. Each house has two bedrooms, one living room, one guest room, two bathrooms, and one kitchen. The results revealed that using this system will have a scientifically significant impact on electricity consumption and cost. In the case of the houses, the cost was USD 33.32, 32.64, 22.32, and 19.54 for unscheduled, two, four, and six houses, respectively. Thus, it was observed that the cost and power are directly proportional to each other. The results reveal that the proposed solution efficiently reduces the cost as compared to that of unscheduled houses.

Soil water and solute transport in an Andosol apple orchard including the dormancy period in a snowy cold region

Early spring corresponds to the snowmelt season in apple orchards of the northern hemisphere. This has a significant effect on the initial growth of shoots and flower buds of apple trees. Understanding the soil environment of the orchard during the non-growth period as monitored by farmers is important for managing year-round apple production. However, the details of mass transport, such as that of soil water and solute, during the non-growing season in the Andosols remain unclear. This study conducted soil environment monitoring in an Andosol apple orchard in Aomori Prefecture, Japan, during the non-growing season in winter to examine the year-round changes in the soil environment, using a field monitoring system that is necessary for understanding the soil environment. Volumetric water content, soil bulk electrical conductivity, and soil temperature were measured, and the cumulative changes in total soil moisture (ΣΔTSM) were calculated from the monitored volumetric water content. Results showed that although ΣΔTSM tended to increase (i.e., dry out) annually with periodicity, the variation was small compared to that of apple orchards with gravelly brown forest soil. These results could significantly influence fertilization management of apple orchard soil during the summer drought season and early spring when the roots of apple trees begin taking up nutrients. In particular, it was revealed that if sufficient nutrients remain in the soil pore water in early spring, the negative impact of excessive fertilization on the surrounding environment can be reduced. Therefore, this study constitutes an innovative step in the implementation of field monitoring system to understand the details of mass transport in the Andosol apple orchard soil.

Design and Implementation of Coal Mine Safety Monitoring System Based on GIS

In order to further improve the safety production management capacity of coal enterprises and minimize the rate of coal mine safety accidents, based on the purpose of improving safety production efficiency, this paper constructs a coal mine safety detection system based on GIS technology. At the same time, on the basis of fixed-point positioning and remote monitoring at the PC end, this paper implements the whole process detection and monitoring of coal mine safety production with the help of mobile terminal intelligent system. The system software uses Java language to code it. In the first round of test, one function omission, three serious system errors, 14 general errors, and 21 minor errors are found. The accident passing rate of the second test is greater than 99.88%, and all performance indexes of the system meet the performance requirements, which proves the feasibility of the system.

Electromagnetic spectrum monitoring of LTE channels based on SDR portable sensor: preliminary analysis

This paper presents preliminary research for the implementation and testing of a Software Defined Radio (SDR) sensor with electromagnetic spectrum monitoring capabilities for measuring signals specific to the Long-TermEvolution (LTE) mobile communications standard. The proposed sensor is developed on the ADALM Pluto SDR platform - and is designed to acquire a bandwidth of at least 20MHz. To determine the spatial extent of the measurements, the sensor also includes a GPS (Global Positioning System) module. The hardware control of the sensor is performed both in stationary variant using a laptop and in mobile mode using a low-cost module - Raspberry Pi. Present findings suggest that the designed SDR solution represents a reliable and low-cost alternative for real-time spectrum monitoring of wideband signals like the case of 4G-specific signals. The cost of the whole implemented monitoring system is about EUR 330.

Time Synchronization for Smart Grids Applications: Requirements and Uncertainty Issues

Smart grid implementation requires that multiple distributed systems are synchronized within and between substations. This is needed to coordinate the Intelligent Electronic Devices (IEDs) as well as to align in time the measurement data associated with the same event, but collected in different points of a broad geographical area. The time synchronization accuracy requirements in power systems are application-specific and are classified in the IEC Standard 61850-5:2013. Depending on such requirements, alternative solutions can be used. For instance, sub-microsecond time synchronization is essential for the implementation of Wide Area Monitoring, Protection, and Control (WAMPAC) systems. In fact, time synchronization directly and indirectly affects Phasor Measurement Units (PMUs) accuracy. The PMUs can measure the magnitude, the phase, the fundamental frequency and the Rate of Change of Frequency (ROCOF) of ac voltage and current waveforms at times synchronized to the Universal Coordinated Time (UTC). Among such quantities, the phase data are those most strongly affected by time synchronization. If time synchronization accuracy is within ±1 µs, its impact on synchrophasor phase measurement is in the order of a fraction of mrad, i.e., low enough to have a negligible impact on the state estimation uncertainty of most of transmission and distribution systems. However, the need to interpolate the missing phasor values when data from PMUs with a different reporting rate are collected and aligned in time may unexpectedly boost synchrophasor phase estimation uncertainty.