Performance Monitoring System Research Articles

Periodic Transmissions in Random Access Networks: Stressed Period and Delay

Most IoT systems use random access protocols for wireless communication. This paper considers an IoT node that generates periodic traffic to be delivered to a destination over a random access network; each packet of the node is expected to be delivered before its deadline. We say that the node is in a stressed period if, within a time interval, its successive packets miss their deadlines. In many systems, the worst-case performance is significantly affected by stressed periods. Characterizing the stochastic properties of stressed periods is thus of fundamental importance. In this paper, we use a fluid flow model to approximate the evolution of the buffer occupancy (i.e., backlog) at the transmitting node. We derive a relationship between buffer occupancy and delay and formally define a stressed period via a time interval in which the buffer occupancy exceeds a certain threshold. With this model, we analyze the dynamics of the buffer occupancy evolution and obtain the probability distributions of stressed period duration and delay. Real network experiments show that our model can well approximate the distributions of stressed period duration and delay in practical WiFi networks. The theoretical results of this paper can be used to analyze the robustness and worst-case performance of IoT monitoring and control systems built on random access networks.

Nationwide Demand Modeling for an Urban Air Mobility Commuting Mission

In this paper, we present a comprehensive and reproducible urban air mobility (UAM) demand model centered around publicly available data and open source tools capable of demand estimation at the national level. A discrete mode-choice demand model is developed using longitudinal origin–destination employment statistics flow data, American community survey economic data, and the Open Source Routing Machine (OSRM) to identify the utility of a UAM commuter service relative to other modes of transportation. Using the implemented model, we identify New York City, San Francisco, and Los Angeles as cities with the highest potential commuter demand, and Seattle as the city most resilient to increases in delay time. A sensitivity study of demand is performed and shows that strong demand exists for short trips with low total delay times and for longer trips with a low ticket price per kilometer, with the former showing resilience to increases in operational costs and the latter showing resilience to increases in delays. The demand model is supported by a speed-flow model, which fuses highway performance monitoring system data with OpenStreetMap data to provide traffic-adjusted road segment speeds to OSRM. The speed-flow model has the capability of providing congestion data for road segments across the United States without the use of commercial data sets or routing services and is shown to improve routing duration accuracy in congested regions.

Proctoring and Monitoring-Based Examination System

Abstract: Due to COVID-19, Online Examination hasgained the most popularity over the previous year across all educational sectors. However, the institutions are having a lot of trouble with the proctoring procedures. There must be a solution found if the way we live is to become the new norm.In this project, we put up a plan to create an AI-based integrated system that can aid in avoiding exam cheating, as well as various methods and technologies that would allowthe proctor to be absent from the exam at all times. Our AI- based algorithm will be able to recognize any irregularities in an exam. Our system guarantees a moral examination atmosphere free of unethical behavior. Our technology monitors the activities of students who attend school in distant locations. Our method can aid in the detection and prevention of exam-related cheating. You can conduct your entire exam without using any paper. By switching to our technology, we can significantly reduce the cost of managing and administering exams. Our solution is compatible with the conventional exam method. The emergence of the online examination system (OES) is the best example of these reforms, and the monitoring system is intended to assure the fairness and impartiality of the OES. The growth of modern educational technology has pushed changes in teaching patterns and examination patterns. The paper describes the OES and monitoring system's structure and operation, highlights the major issues with communication and security, and offers remedies. The network examination criteria have been met by the system. In this project, we've suggested a way to stop exam cheating by creating an AI-basedintegrated system. We've also provided several methods and tools that eliminate the need for the proctor to be present during the exam. Any unfairness in a test will be detectable by our AI-based approach. Here, we offer methods and resources that eliminate the requirement for the proctor to be present for the entire exam. Any unfairness in a test will be detectable by our AI-based approach. Here, we offer methods and resources that eliminate the requirement for the proctor to be present for the entire exam. Neural networks and machine learning are the foundation of this. Our AI- based technology will be able to recognize any unethical exam practices. Our tests showed that the suggestedsystem is superior to the ones already in use

Development of an Academic Performance Monitoring System Using Least Squares Regression for Predicting Student Academic Performance Status on Professional Courses

This study deals with the development of an academic performance monitoring system using least squares regression to predict a student’s academic performance status on professional courses. The proposed system monitors the student’s academic performance to promote education and achieve learning outcomes. The study aims to design and create a Graphical User Interface (GUI) and its functions, to utilize MATLAB for forecasting and a least squares regression model to show and predict the relationship between two factors, and to evaluate the system’s predicting accuracy using Percentage Forecast Error (PFE) and Mean Absolute Percent Error (MAPE) to compare actual and forecasted data for every semester. The sample size includes 182 students of BS Information Technology, of whom thirteen (13) were successfully chosen using simple random sampling with their GPAs for the purpose of predicting future academic performance.

Experience of operation monitoring system of the cat-bone bridge through the Petrovskaya Channel in the alignment of the Western Speed Diameter Highway in St. Petersburg

Aim: Analysis of the current monitoring system of the engineering structures of a cable-stayed bridge across the Petrovsky Canal in the alignment of the Western High-Speed ​​Diameter highway in St. Petersburg.
 Methods and Materials: Statistical data from the databases of the existing system of monitoring engineering structures is used to obtain the results. The tasks, set within the framework of the study, are accomplished by applying the theoretical methods of scientific knowledge: the analytical method, the mathematical statistics theory, induction.
 Results: Analysis of the current monitoring system of engineering structures is presented with a description of one of the main problems the impossibility of synchronizing readings from all sensors during considered period of time, which leads to the inability to correctly assess the stress-strain state of the bridge structure as a whole. Discovered problem solutions are proposed, further research prospect is determined in part of the imperfection of monitoring systems for long-span facilities.
 Conclusion: The obtained study results can be used in designing and installation of monitoring systems of the engineering structures for cable-stayed bridges, as well as for modernization and optimization of existing monitoring systems to improve the quality of assessing technical condition of structures assessment.

Distributed Compressive Sensing for Wireless Signal Transmission in Structural Health Monitoring: An Adaptive Hierarchical Bayesian Model-Based Approach.

Signal transmission plays an important role in the daily operation of structural health monitoring (SHM) systems. In wireless sensor networks, transmission loss often occurs and threatens reliable data delivery. The massive amount of data monitoring also leads to a high signal transmission and storage cost throughout the system's service life. Compressive Sensing (CS) provides a novel perspective on alleviating these problems. Based on the sparsity of vibration signals in the frequency domain, CS can reconstruct a nearly complete signal from just a few measurements. This can improve the robustness of data loss while facilitating data compression to reduce transmission demands. Extended from CS methods, distributed compressive sensing (DCS) can exploit the correlation across multiple measurement vectors (MMV) to jointly recover the multi-channel signals with similar sparse patterns, which can effectively enhance the reconstruction quality. In this paper, a comprehensive DCS framework for wireless signal transmission in SHM is constructed, incorporating the process of data compression and transmission loss together. Unlike the basic DCS formulation, the proposed framework not only activates the inter-correlation among channels but also provides flexibility and independence to single-channel transmission. To promote signal sparsity, a hierarchical Bayesian model using Laplace priors is built and further improved as the fast iterative DCS-Laplace algorithm for large-scale reconstruction tasks. Vibration signals (e.g., dynamic displacement and accelerations) acquired from real-life SHM systems are used to simulate the whole process of wireless transmission and test the algorithm's performance. The results demonstrate that (1) DCS-Laplace is an adaptative algorithm that can actively adapt to signals with various sparsity by adjusting the penalty term to achieve optimal performance; (2) compared with CS methods, DCS methods can effectively improve the reconstruction quality of multi-channel signals; (3) the Laplace method has advantages over the OMP method in terms of reconstruction performance and applicability, which is a better choice in SHM wireless signal transmission.

An environment safety monitoring system for agricultural production based on artificial intelligence, cloud computing and big data networks

Monitoring the agricultural production environment is crucial for optimal crop growth and resource efficiency. Cloud Computing, Artificial Intelligence (AI), and Big Data have revolutionized traditional agriculture, promising improved output and product quality. The popularity of these technologies drives their application in safety monitoring. This system facilitates data collection and transmission among equipment, overcoming challenges of traditional systems like investment, costs, and maintenance. In this paper, cloud computing-based AI optimization technology and big data network were proposed to monitor the safety of the agricultural production environment, and the shortcomings of traditional distance vector hop (DV hop) positioning algorithms were analyzed in depth. RSSI (Received Signal Strength Indication) technology improved the traditional DV Hop location method. The paper analyses direct and indirect transmission for data transmission between WSN and cloud nodes and favors indirect transmission because it consumes less invalid energy. Finally, the article compares several evaluations of alternative algorithms for monitoring system performance, including data transmission reliability, data reception rate, and data delay. The experimental results in this paper showed that in the data reception rate test, the data reception rate of System 2 was 97% at the lowest and 99% at the highest, both exceeding 95%.

Impact of 3PL Outsourcing on Supply Chain Management in Manufacturing Companies in Norway: Case Study Norsk Hydro

3PL outsourcing is a practice in which a company outsources its logistics and supply chain management functions to a third-party logistics provider. By outsourcing logistics activities to a 3PL provider with advanced technology solutions, organizations can improve their supply chain management and achieve greater efficiency and effectiveness. Supply chain management is the coordination and management of all activities involved in the production and delivery of goods and services from raw materials to end customers. It includes activities like procurement, production, transportation, warehousing, and distribution. The research used the descriptive research design. The target population was 30 Manufacturing Companies in Norway.  The research did sampling of 40 participants that were chosen from the target population of 30 Manufacturing Companies in Norway. Questionnaires were utilized to gather the data. The study concluded that the partnership between manufacturers and 3PL providers has become increasingly critical in Norway's competitive manufacturing industry. This collaboration helps to streamline processes, enhance visibility, and optimize inventory management, leading to greater supply chain performance.  To ensure successful outsourcing, it is essential to conduct a thorough analysis of the supply chain, identify the right 3PL provider, establish clear communication and collaboration, develop a performance monitoring system, and regularly review and assess the outsourcing arrangement. It was recommended that before deciding on whether to outsource logistics functions to a 3PL provider, it is crucial to conduct an in-depth analysis of the current supply chain to identify areas that need improvement. The manufacturing company should establish a clear communication plan with the 3PL provider, outlining expectations, key performance indicators (KPIs), and regular reporting requirements. Effective communication and collaboration are crucial to the success of 3PL outsourcing. Keywords: 3PL Outsourcing, Supply Chain Management, Manufacturing Companies, Norway

IoT-based smart monitoring and management system for fish farming

Fish farming is still controlled and managed in the traditional way where water quality and fish feeding are manually controlled. There is a need to use computer and communication technology in fish farms for remote monitoring and control. This paper deals with the design and implementation of an internet of things (IoT) based system for real-time monitoring, control and management of fish farming. The design of such a system is based on measuring different types of variables and using the information to control fish growth and increase productivity. Each fish pond is a node in a wireless sensor network. The node contains an embedded microcontroller connected to a set of sensors and actuators and a wireless communication module. Two fuzzy controllers are designed to control the water quality in the ponds as well as the environment using five sensors in each pond plus three environmental sensors. Practical results indicate the accuracy of the measurement system compared to the results obtained from commercial devices used on the farm. These results also showed that the proposed approach achieves the best performance of the real-time monitoring and control system in fish ponds.

IOT based Manhole Detection and Monitoring System

Abstract: With the increasing Urban population, the need for Internet of Things (IoT) based smart city applications, such as monitoring manhole covers (MC), becomes crucial. However, current MC monitoring systems lack full automation, have a short lifespan, and face design challenges from an IoT device perspective. In this study, we explore the use of compressive sensing (CS) to reduce power consumption in the analog-to-digital converter (ADC) of an IoT-based MC monitoring system, aiming to extend its lifetime. We propose two low-power analog chaotic oscillators as an alternative to the digital pseudo-random number generator (PRNG) commonly used in CS-based ADC's. The proposed systems are validated through temperature measurements, showing promising results. This research addresses circuit design challenges and improves system performance for long-term and efficient operation of IoT-based MC monitoring systems.

A Fire suppression monitoring system for smart building

A fire suppression system (FSS) monitoring system is a system to monitor the FSS devices’ status since FSS is a critical system to respond to fire disasters. The monitoring system collects data on important parameters which are water pressure, main power status, and backup power status. The FSS monitoring system is built with an IoT capability where data are collected from the FSS module and sent to the IoT platform through Wi-Fi based Internet connection. Then the data will be displayed in a dashboard application. A QoS assessment framework is referred to and performed to check the performance of the FSS monitoring system, namely the TIPHON framework, which consists of five parameters: bandwidth, throughput, packet loss, delay, and jitter. The overall score for the FSS system using the TIPHON standard is 3.2 or categorized as “good”.

Management Program Implementation of Performance Monitoring and Control Systems Benefits as Quality Control and Teacher Competence in Banyuwangi District

Teachers who get certification are entitled to a professional allowance. The government hopes that with this allowance, teachers can improve their welfare and performance. But unexpectedly, after receiving the professional allowance, teachers are not compensated with good quality and competence. Related to this, the Banyuwangi Regency government made improvements through the Education Office Branch. A Management System for Monitoring and Control of Performance and Allowances (SRITANJUNG) management program was launched to control teacher quality and competency. This study aims to build an implementation model for the Sritanjung Program as a quality control and appropriate teacher competency. Factors that support or inhibit will be sought and try to find a solution. This study uses a descriptive qualitative method by collecting data in the field through interviews and questionnaires to all parties involved. The results showed that 1) the implementation of the program as a quality control and teacher competence in the process there are problems with legality, attitude and character of supervisors and principals, target targets/teachers, and tools. While the supporting factors for program implementation as control are: (a) the existence of implementer intentions and agreements, (b) the existence of a legal umbrella, (c) application support, and (d) the availability of implementor resources that are ready. While the inhibiting factors are (a) policy; assignment legality for school supervisors and principals, (b) tools; teacher's computer equipment, frequent errors, incomplete features, and internet signal, (c) systems and processes; lack of clear information about the program, and (d) from human resources; not all teachers are familiar with applications, honesty of information from teachers, weak mastery of information technology and computers, allocation of allowances and busyness of teachers. Meanwhile, the program implementation model for quality control and teacher competence that has been built and is appropriate is the Co-Han Model, which is presumably the initial solution. The Co-Han model is a combination of the old program implementation model and some elements of the COCO control model. This new model involves elements of attitude and characteristics of implementers and targets. Involving the elements of attitude and character in the form of goals, commitment, and abilities is considered very important. With a good attitude and character, it is hoped that the program can be implemented properly too, so that the goals will be easily achieved. The combination of management as a system, as well as commitment and ability as a character will greatly influence program objectives. This is considered a very effective, necessary and new combination in a program implementation as control.

Feasibility of implementing a performance monitoring system for health information technology students during the internship

Introduction: In universities of medical sciences, due to the key role of human resources in people's lives, their training during the internship period is of particular importance. Considering the role of effective assessment of the quality of students' performance, this research has been carried out with the aim of feasibility of implementing a system for monitoring the performance of health information technology students during internship.
 Methods: This research is descriptive-cross-sectional and among applied studies that was conducted in 1401. The data collection tool was a researcher-made questionnaire with 5 sections including background information and technical, organizational, human and legal dimensions, and the research population was the professors of the health information technology department at the country. The questionnaire was sent via email and finally, the data of 20 completed questionnaires were analyzed using SPSS software version 16 by descriptive statistics method.
 Results: The highest average scores for the possibility of implementing the performance monitoring system during internship was related to the legal dimension with 82.3%. And then there were the technical dimension with 80.6%, the organizational dimension with 79.6% and finally the human resource dimension with an average of 76.6%.
 Conclusion: It is possible to implement the performance monitoring system of health information technology students during internship from technical, organizational, legal and human resources aspects and considering the importance of the internship period and the effective role of performance monitoring tools in the management of activities, it is suggested that necessary measures be taken to implement this system.

Response outcome gates the effect of spontaneous cortical state fluctuations on perceptual decisions.

Sensory responses of cortical neurons are more discriminable when evoked on a baseline of desynchronized spontaneous activity, but cortical desynchronization has not generally been associated with more accurate perceptual decisions. Here, we show that mice perform more accurate auditory judgments when activity in the auditory cortex is elevated and desynchronized before stimulus onset, but only if the previous trial was an error, and that this relationship is occluded if previous outcome is ignored. We confirmed that the outcome-dependent effect of brain state on performance is neither due to idiosyncratic associations between the slow components of either signal, nor to the existence of specific cortical states evident only after errors. Instead, errors appear to gate the effect of cortical state fluctuations on discrimination accuracy. Neither facial movements nor pupil size during the baseline were associated with accuracy, but they were predictive of measures of responsivity, such as the probability of not responding to the stimulus or of responding prematurely. These results suggest that the functional role of cortical state on behavior is dynamic and constantly regulated by performance monitoring systems.

Deep learning-based reconstruction of missing long-term girder-end displacement data for suspension bridge health monitoring

The flexibility of a suspension bridge usually results in significant longitudinal displacement at the end of the main girder under the joint action of environmental factors and traffic loads. Fluctuation amplitude and accumulation of girder-end displacement (GED) are primary data for the performance evaluation of suspension bridge elements, including expansion joints, supports, and dampers. However, the performance of the bridge structural health monitoring (SHM) system is frequently deteriorated by the long-term continuous GED data loss or anomaly. In this study, this issue of the suspension bridge SHM system was resolved by proposing a deep learning-based framework for reconstructing the missing GED data. Under this framework, a long short-term memory (LSTM) network-based regression model was first built between the ambient temperature data and the thermal-induced low-frequency components of GED data. Next, a U-net-based spectral bandwidth expansion model was applied for the step-by-step generation of vehicle/wind-induced high-frequency GED data terms based on the available low-frequency ones. Finally, a statistical correction strategy was employed to improve the prediction accuracy for the high-frequency GED data fluctuation amplitudes. The trained model for the reconstruction of the missing GED data uses only the air temperature input values in the data loss period. This method is especially lucrative when GED data in all sensor channels are missing. The reliability and efficiency of the proposed model were demonstrated by a case study. Specifically, the problem of reconstructing the missing GED data in the SHM system of the Jiangyin Yangtze River Bridge in China was considered. Using the relative error of cumulative GED as the performance indicator, a reconstruction precision within 10% was obtained.

Safety, usability, and performance of a wireless left atrial pressure monitoring system in patients with heart failure: the VECTOR-HF trial.

In heart failure (HF), implantable hemodynamic monitoring devices have been shown to optimize therapy, anticipating clinical decompensation and preventing hospitalization. Direct left-sided hemodynamic sensors offer theoretical benefits beyond pulmonary artery pressure (PAP) monitoring systems. We evaluated the safety, usability, and performance of a novel left atrial pressure (LAP) monitoring system in HF patients. The VECTOR-HF study(NCT03775161) was a first-in-human, prospective, multicenter, single-arm, clinical trial enrolling 30 patients with HF. The device consisted of an interatrial positioned leadless sensor, able to transmit LAP data wirelessly. After three months, a right heart catheterization (RHC) was performed to correlate mean pulmonary capillary wedge pressure (PCWP) with simultaneous mean LAP obtained from the device. Remote LAP measurements were then used to guide patient management. The miniaturized device was successfully implanted in all 30 patients, without acute Major Adverse Cardiac and Neurological Events (MACNE). At 3 months, freedom from short-term MACNE was 97%. Agreement between sensor-calculated LAP and PCWP was consistent, with a mean difference of -0.22±4.92mmHg, the correlation coefficient and the Lin's Concordance Correlation Coefficient values were equal to 0.79 (P<0.0001) and 0.776 (95%CI=0.582-0.886), respectively. Preliminary experience with VLAP-based HF management was associated with significant improvements in NYHA functional class (32% of patients reached NYHA II class at 6 months, P<0.005; 60% of patients at 12 months, P<0.005) and 6-minute walk-test distance (from 244.59±119.59m at baseline to 311.78±129.88m after 6 months, P<0.05, and 343.95±146.15m after 12 months, P<0.05). The V-LAP™ monitoring system proved to be generally safe and provided a good correlation with invasive PCWP. Initial evidence also suggests possible improvement in HF clinical symptoms. This article is protected by copyright. All rights reserved.

Mathematical modeling of resource allocation for cognitive radio sensor health monitoring system using coevolutionary quantum-behaved particle swarm optimization

Independent interactive medical applications cooperate closely with user systems through independent information and data exchange, which realizes digital and intelligent communication for health monitoring system. Due to extensive connectivity and application services, heavy data traffic and limited energy consumption reduce the quality of application services. In this paper, a mathematical model for efficient energy harvesting resource allocation based on cognitive radio sensor network (CRSN) infrastructure is constructed to address energy limitation issues in health monitoring system applications. Because of the highly dynamic nature of available network resources, energy efficiency and communication reliability can be improved. Since the proposed optimization problem is a complex and NP hard problem, coevolutionary theory is adopted, and then a stochastic optimization algorithm based on polynomial approximation and quantum particle swarm optimization is proposed. Simulation results show that the proposed CQPSO algorithm has better performance in terms of re-transmission probability, system throughput, and energy consumption compared to other similar algorithms. Efficient resource allocation improves the performance of the health monitoring system, more effectively and real-time monitoring of human physiological and motion information, providing testers with high-precision quantitative information, and achieving early monitoring and intervention of common diseases.

Development of an IoT-Enabled Air Pollution Monitoring and Air Purifier System

Air Pollution is one of the severe hazards which affect the health quality of all living beings and the environment itself, especially in urban areas. It creates significant health issues in many countries and harms human health if its concentration exceeds tolerable levels. Monitoring these pollutants and their concentrations is a crucial preventative measure to alert the public to the air quality in the area. The primary objective of this paper is to design a smart air pollution prototype to purify the air with the help of a PM sensor, gas sensors, analog to digital converter (ADC), Raspberry Pi 3 B+ microcontroller, and multiple filters (Step-by-step filtering of particles from sizes 200 to 0.3 µm will be done by different filters). The gas sensors, MQ2 and MQ135, measure the presence/concentration of the LPG, smoke, CO, CO2, and NH4 before and after the filtering process. The PMSA003 sensor detects Particulate Matter (PM) in the range of 0.3 microns to 10 microns. ADC and Raspberry Pi 3 B+ convert the outputs of gas sensors and PM sensors into concentrations of particles. These results are transmitted to the Thingspeak Cloud platform through the WiFi Module. The sensor outputs can be viewed in any system/mobile in the Thingspeak Cloud platform. The concentration of different gases and the measured size concentration of various particles are plotted before and after the filtration, and the results are compared. It is found that the designed filter effectively filters all the particles above 0.3 µm. The performance and efficiency test of both air quality monitoring and air purifier system is done.

Monitoring the Performance of Lecturers Using Behaviorally Anchor Rating Scale and Management by Objectives Method

Mutiara Mahakam Samarinda Institute of Health Sciences (STIKES-MM Samarinda) has a system for monitoring and evaluating the performance of lecturers or education staff. This system measures performance achievements in terms of teaching, research, and community service. . Nevertheless, since every segment of the system is not yet fully computerized, this then raises several obstacles in the process of monitoring and evaluating the performance, length of time to obtain the final assessment results and the low accuracy level of the assessment. This study aims to seeks solutions to these obstacles and offers an educator performance monitoring system that combines the Behaviorally Anchor Rating Scale (BARS) and Management by Objectives (MBO) methods to be assessed quantitatively based on the rating scores in measuring the two methods. The BARS method was focused on evaluating behaviour that would affect overall performance with an average score of 4.14%, while the MBO method was focused on evaluating according to Tri Dharma of higher education, namely teaching, research and community service. The assessment system was then implemented to evaluate the performance of lecturers and education staff. Subsequently, the data obtained were analyzed to get the final result of the assessment. In particular for data from the MBO method, the analysis was carried out using step with and without KRA. This exploratory research succeeded in presenting the final results of the performance assessment of each lecturer who was assessed for both the value of the BARS and MBO methods. Data analysis from the MBO method , when calculated with and without KRA and KRA, showed some significant differences in MBO. For all lecturers, the difference in scores, if the average was 3.48%, then this assessment was more inclined to the BARS assessment, which had a better rating than MBO.

Comparison of Accuracy of Freestyle Libre Pro and Medtronic iPro2 Continuous Glucose Monitoring Systems in Pregnancy.

There are limited data on head-to-head performance of Freestyle Libre Pro (FSL-Pro) and blinded Medtronic iPro2 CGMS in pregnancy. In this prospective observational study, women with hyperglycemia in pregnancy (n=42) underwent simultaneous FSL-Pro and Medtronic iPro2 sensor insertion and SMBG using Contour Plus meter (reference). The overall MARD for iPro2 and FSL-Pro systems were 8.0 ± 9.2% and 19.0 ± 12.8%, respectively. At hypoglycemic range, both sensors performed less accurately (MARD: 18.1% and 16.8%, respectively), while iPro2 showed higher accuracy at euglycemic (8.2% and 19.3%, respectively) and hyperglycemic (6.8% and 18.0%, respectively) ranges. On Bland-Altman analysis, iPro2 and FSL-Pro underestimated glucose by 0.01 and 1.09 mmol/L, respectively. The ISO criteria were fulfilled for 83.4% and 36.2% of all values, respectively. To conclude, iPro2 was more accurate, however, both sensors demonstrated inaccuracy at hypoglycemic range, highlighting the need for refinements in the current generation of sensors to address this problem.