Real-time Data Collection Research Articles

Earthquake Early Warning Systems by Using Cloud and IoT

Earthquake early warning systems play a crucial role in minimizing the impact of seismic events on human life and infrastructure. This research paper explores the integration of Cloud Computing and Internet of Things (IoT) technologies to enhance the effectiveness of earthquake early warning systems. The proposed system aims to provide real-time data collection, analysis, and dissemination of alerts through a distributed network, leveraging the scalability and flexibility of cloud-based platforms. This paper discusses the architecture, components, and benefits of the proposed system, along with a case study to illustrate its potential in earthquake-prone regions.

A Survey on-Advancing Urban Sustainability through Smart City Integration

The three essential elements of smart city development that are highlighted in this project are air pollution monitoring, video surveillance, and optimised street lighting. Energy economy and safety are improved when intelligent sensors and controls are integrated into street lights. Proactive security measures are guaranteed by advanced video surveillance combined with AI analytics. IoT-enabled air pollution monitoring allows for the real-time collection of data for focused interventions. Savvy streetlamps outfitted with sensors and versatile controls offer energy investment funds and upgraded wellbeing by changing lighting levels in view of constant circumstances. Video reconnaissance frameworks, enabled by man-made intelligence calculations, give proactive observing and reaction abilities, guaranteeing public wellbeing and security. Moreover, air contamination observing utilizes IoT sensors to gather constant information on air quality boundaries, empowering designated intercessions to alleviate contamination levels. When these systems are integrated, sustainable urban growth is promoted, resource utilisation is optimised, and quality of life is enhanced.

Design and rocket deployment of a trackable pseudo-Lagrangian drifter-based meteorological probe into the Lawrence/Linwood EF4 tornado and mesocyclone on 28 May 2019

Abstract. A custom lightweight, miniaturized, and trackable meteorological probe was launched by a model rocket into the inflow region of an EF4, long-tracked tornado south of Lawrence, Kansas, on 28 May 2019 and sampled tornado core flow. The rocket reached apogee at 439 m a.g.l., releasing the “pseudo-Lagrangian drifter” by parachute directly into the tornado vortex. The probe reached a three-dimensional (3D) speed of 85.1 m s−1 in the first revolution around the tornado, measured an altitude-corrected pressure deficit of −113.5 hPa at 475 m a.s.l., and sampled a tornadic updraft speed of 65.0 m s−1. The probe then transitioned to an environment exhibiting a more tilted ascent above an altitude of 4300 m a.s.l. at speeds up to 84.0 m s−1 to a maximum altitude of 11 914 m a.s.l. 1 Hz pressure, temperature, relative humidity, GPS, acceleration, gyroscope, and magnetometer data for the flight were transmitted in real time to a ground station until 10 680 m a.s.l. and the probe landed 51 km northeast of the launch position. The probe was recovered without damage, which is attributed to the pseudo-Lagrangian drifter design, and then higher-resolution and complete 10 Hz data were downloaded for the flight. This novel deployment method and design facilitate data collection in real time from within tornadoes, the mesocyclone, and downdraft without requiring the probes to be recovered or for researchers to enter the circulation to deploy equipment.

ЗАСТОСУВАННЯ СМАРТ-КОМПЛЕКСІВ У ПІДГОТОВЦІ МАЙБУТНІХ ВЧИТЕЛІВ

The article revealed the features of smart education as a leading concept for the development of professional training of prospective teachers. The main components of smart education, such as a smart student, smart pedagogy and smart environment, were characterized. The main principles of smart education and the features of using smart technologies were defined. The authors substantiated the peculiarities of the implementation of smart education in the conditions of the COVID-19 pandemic and the war in Ukraine. The functions of the smart system (site management system) and the smart complex in the process of studying the disciplines of the pedagogical cycle, their content and technological components were determined. The possibilities of smart complexes for students and teachers in the educational process of training prospective teachers were described. The criteria of smart complexes were singled out, among them: automation, sequence, assessment, real-time data collection, self-organization. The main advantages of smart complexes were determined, including: instant response to external changes, openness; expansion due to the integration of new functionality; easy access to educational material, mobility; ensuring compatibility between software for different operating systems; lack of dependence on time and place; constant updating of the content, the possibility of self-control and assessment of students’ knowledge. The authors considered the distance learning systems for creating smart complexes for studying the disciplines of the pedagogical cycle in the process of training prospective teachers. The results of the students’ survey on the use of smart complexes in the educational process were analyzed. Based on the obtained results, the advantages of smart education were determined: time saving, clarity, efficiency of use in distance learning conditions, etc., and the problems of using smart technologies in the educational process were described. The ways of further research work on the introduction of smart education into the educational process of pedagogical higher education were outlined.

Enhancing Traffic Management in Smart Cities: A Cyber-Physical Approach

The challenges of managing traffic in rapidly urbanizing cities with increasing populations. It highlights the need for advanced technologies and frameworks in smart cities to develop intelligent traffic management systems. The proposed approach is a cyber-physical framework that integrates physical infrastructure with digital infrastructure for real-time data collection, analysis, and decision-making. By using sensors, IoT devices, and data analytics, the system captures and processes traffic-related information to predict traffic flow, optimize signal timings, and recommend alternate routes. The system also communicates with connected vehicles to provide real-time updates and alerts, improving driver safety and reducing congestion. The cyber-physical approach allows seamless integration with other smart city systems for efficient coordination and resource allocation. Implementing this system offers benefits such as reduced travel times, improved air quality, enhanced road safety, and increased overall efficiency in urban transportation. It can adapt and evolve by learning from new data inputs and incorporating emerging technologies, ensuring sustainability and scalability. Overall, this paper presents a comprehensive framework for enhancing traffic management in smart cities, transforming urban transportation, and improving residents' quality of life while making cities more sustainable and resilient.

Secure Task Distribution With Verifiable Re-Encryption in Mobile-Crowdsensing-Assisted Emergency IoT System

Extreme events (such as earthquakes, hurricanes, etc.) pose a dual challenge to the reliability and serviceability of IoT systems. With regard to this challenge, by publishing some tasks and then encouraging the public to assist in real-time data collection through their mobile terminals (namely, the mobile crowdsourcing-assisted IoT systems), is expected to play an important role in secondary disaster prevention and personnel rescue in extreme events. However, it has weaknesses in terms of security, flexibility, and efficiency. As an elegant solution, identity-based broadcast proxy re-encryption (PR-IBBE) enables flexible access authorization sharing and efficient broadcast distribution of encrypted tasks via the cloud. However, their security relies on fully-trusted or semi-trusted cloud assumptions, which are hard to be implemented in real-world scenarios. And the cloud is more vulnerable in an emergency event since there is a lack of effective management. Motivated by that, we propose the verifiable PR-IBBE (VPR-IBBE) scheme, which realizes a cross-domain identity-based broadcast task file secure authorization access, and empowers the verifiability and reputability of re-encrypted ciphertext under the untrusted cloud setting. This mechanism ensures that the relevance between the re-encrypted ciphertext and the original ciphertext can be publically verified, so the cloud can defend itself if there is a malicious accusation of forging the re-encrypted ciphertext. Through rigorous formal security proofs, we demonstrate that VPR-IBBE attains the indistinguishability of ciphertext against selective identity chosen ciphertext attack (IND-sID-CPA), and is also resistant to the collusion attack between the untrusted cloud and the cooperative performer. Theoretical comparison and experimental results demonstrate the practicability of our VPR-IBBE scheme, as well as the superiority over representative related works.

Application of a mobile health data platform for public health surveillance: A case study in stress monitoring and prediction.

Public health surveillance involves the collection, analysis and dissemination of data to improve population health. The main sources of data for public health decision-making are surveys, typically comprised of self-report which may be subject to biases, costs and delays. To complement subjective data, objective measures from sensors could potentially be used. Specifically, advancements in personal mobile and wearable technologies enable the collection of real-time and continuous health data. In this context, the goal of this work is to apply a mobile health platform (MHP) that extracts health data from the Apple Health repository to collect data in daily-life scenarios and use it for the prediction of stress, a major public health issue. A pilot study was conducted with 45 participants over 2 weeks, using the MHP to collect stress-related data from Apple Health and perceived stress self-reports. Apple, Withings and Empatica devices were distributed to participants and collected a wide range of data, including heart rate, sleep, blood pressure, temperature, and weight. These were used to train random forests and support vector machines. The SMOTE technique was used to handle imbalanced datasets. Accuracy and f1-macro scores were in line with state-of-the-art models for stress prediction above 60% for the majority of analyses and samples analysed. Apple Watch sleep features were particularly good predictors, with most models with these data achieving results around 70%. A system such as the MHP could be used for public health data collection, complementing traditional self-reporting methods when possible. The data collected with the system was promising for monitoring and predicting stress in a population.

IoT-based system of prevention and control for crop diseases and insect pests.

Environmentally friendly technologies for the prevention and control of crop diseases and insect pests are important to reduce the use of chemical pesticides, improve the quality of agricultural products, protect the environment, and promote sustainable development of crop production. On the basis of Internet of Things (IoT) technology, we developed a prevention and control system for crop diseases and insect pests with two main components: a plant protection device (the hardware) and an information management system (the software). To be suitable for both facility- and field-based production scenarios, we incorporated two types of plant protection devices, utilizing ozone sterilization and light-trap technologies. The devices were equipped with various sensors to realize real-time collection and monitoring of data on the crop production environment. The information management system has an IoT-based architecture and includes a mobile device app to enable remote control of the plant protection devices for intelligent management of plant protection data. The system can achieve efficient management of large-scale equipment applications and multi-device collaborative work to prevent and control pests and diseases. The developed system has operated successfully for several years in China and has been applied to cucumber, tomato, rice, and other crops. We demonstrate the effectiveness and practicality of the system in a greenhouse facility and in the field.

Integrating Peer Support Workers into Mental Health Programs

Objective: Canadian Mental Health Association - Calgary Region (CMHA Calgary) works to reduce the impact of mental illness and addiction in the community. This is actioned through mental health programming focused on education, prevention, and early intervention. The organization places high value on the inclusion of lived experience in mental health programming in the form of Peer Support (PS). CMHA Calgary’s five-year strategic plan included a goal to formally integrate Peer Support Workers (PSWs) into all established programs with thought and intention. Research Design and Methods: CMHA Calgary integrated peers into mental health programs using a collaborative and developmental approach. The project team developed an evaluation framework as a guide to collect feedback and understand the impact of the pilot initiative. Results: This approach allowed for real-time responses and data collection, which lead to rapid action to improve the approach before it was spread to other programs. The project allowed for the development of program materials for future application. Conclusions: This project provided CMHA Calgary with tangible, actionable information on how to integrate PSWs into all of their programs.

Research and Appropriate Implementation on Vehicle Tracking System using IoT

The development and implementation of vehicle tracking systems based on Internet of Things (IoT) technology have attracted considerable interest due to the rising demand for effective and secure monitoring solutions. This study investigates the investigation and development of a vehicle monitoring system that utilizes Internet of Things (IoT) capabilities to provide real-time tracking, data collection, and analysis. By combining existing literature, IoT principles, and modern technologies, this study seeks to present a comprehensive and practical method for implementing an effective vehicle tracking system that satisfies the requirements of contemporary fleet management. This document provides a thorough investigation and practical application of a vehicle tracking system utilizing Internet of Things (IoT) technology. The research covers a comprehensive examination of relevant articles and resources regarding vehicle tracking systems and Internet of Things (IoT) applications. It explores into the fundamental principles of IoT and its significance in the context of vehicle tracking. The implementation phase encompasses the creation of a bespoke vehicle tracking system that integrates GPS modules, microcontrollers, wireless communication, and cloud-based data storage to enable real-time tracking and monitoring. The integration of advanced data analytics and machine learning techniques enhances the capabilities of the system, resulting in valuable insights, optimized routing, and predictive maintenance. This integration contributes to improved fleet management. The ethical implications pertaining to the protection of data privacy, security, and compliance to regulatory requirements are carefully taken into account. The results of this study make a valuable contribution to the field of Internet of Things (IoT)-based vehicle tracking systems. Additionally, it provide a practical framework that can be utilized by organizations in search of effective and scalable solutions.

Predicting mechanical properties lower upper bound for cold-rolling strip by machine learning-based artificial intelligence

The mechanical properties serve as crucial quality indicators for cold-rolled strips. For a long time, the mechanical properties mechanism and data-driven models can't comprehensively consider sufficient factors to achieve high-accuracy prediction due to the "data-isolated island" between production lines. In this research, we introduce a multi-process collaborative platform based on the industrial internet system. This platform is designed to enable real-time collection of diverse and heterogeneous data from both upstream and downstream processes of cold rolling. On this basis, a novel mechanical properties interval prediction model is proposed using the sparrow search algorithm to optimize fast learning network under the LUBE framework. We trained the model by using a dataset collected from a large steel plant. Based on the rolling theory and Pearson correlation coefficient, 25 features are selected as the inputs of the prediction model. The experimental results and comparison show that the proposed model is feasible and outperforms other machine learning models, such as the artificial bee colony algorithm optimized extreme learning machine and back propagation neural network model.

STUDY OF THE CURRENT STATE OF DEVELOPMENT OF CONTROL AND AUTOMATION SYSTEMS OF HYDRAULIC MACHINES

Future hydraulic systems will heavily incorporate AI and ML, enhancing control precision and enabling predictive maintenance. AI-driven algorithms will increasingly be used for predictive maintenance, improving the reliability and lifespan of hydraulic systems. This integration will lead to smarter, more efficient operation, and extended equipment lifespans. The integration of IoT technology is crucial for real-time data collection and analysis, facilitating remote monitoring and control that leads to improved operational efficiency and maintenance management. The use of cloud platforms for data analysis and storage is likely to increase, enabling more sophisticated data processing and accessibility. Advanced sensors and predictive maintenance contribute significantly to safety by anticipating system failures. Reliability is also enhanced through reduced downtime and smarter maintenance schedules. The growing convergence of advanced robotics with hydraulic systems is steering these systems towards increased autonomy. Enhanced safety features will be a priority, driven by smarter sensors and control algorithms. The growing complexity of hydraulic systems underscores the need for specialized training and education in system design, maintenance, and operation. Future hydraulic machines are expected to be more customizable and flexible, with modular designs that cater to specific industry needs. A significant challenge is the adaptability of AI and ML models in dynamic, real-world conditions. Continuous learning methods are being developed to address this. In summary, the future of hydraulic machine control systems and automation looks towards greater intelligence, connectivity, efficiency, and adaptability. These trends will be underpinned by ongoing technological advancements, particularly in AI, ML, IoT, and sustainable practices.

Influence of risk and trust on beef producers’ use of precision livestock farming

Precision livestock farming (PLF), which utilizes digital technologies for real-time data collection to improve various farming operations, is an emerging interdisciplinary field of study that could aid US and global livestock production. Currently, dairy, hog, and poultry producers are utilizing PLF technologies for real-time decision making, however, use by beef cattle producers has been less widespread. Using data collected from an online survey of beef cattle producers in Tennessee, we examined factors associated with the use of various PLF technologies. Logistic regression models revealed beef cattle producers’ decisions regarding technology use were influenced by their individual risk preferences and attitudes towards farm data privacy. Producers with greater trust in farm data privacy were more likely to use software management systems and drones while those more willing to take risks were more likely to use drones. Overall, results suggest widespread use of these technologies will require that they be affordable, relevant to production, and capable of improving on-farm profits. Findings from this study can inform the development, deployment, and marketing of PLF technologies related to beef cattle production.

Real-time Data Collection through Wearable Devices to Quantify Attributes Related to Health and Monitor Human Activity

Wearable devices are widely used to monitor physiological activity, meeting the rising demand for quality elderly healthcare. This study utilizes wearable technology to gather real-time data on heart rate, oxygen saturation, and hydration levels for insights into the health and daily functioning of the elderly. The discussion covers the information wearable technology can capture, its applications in tracking health issues and promoting healthy aging, and the challenges of gathering, analyzing, and protecting privacy-related data. The abstract suggests future research directions for enhancing wearable technologies in senior healthcare and youth.

Integration of digital lean principles and line balancing in apparel manufacturing

Purpose: The integration of digitalization into the apparel manufacturing sector has become a strategic imperative, providing companies with a unique competitive edge. This study explores the application of ITEX PMD, an Internet of Things (IoT) data collection device, and evaluates the ITEX digital line balancing program within the context of digital lean management. By leveraging real-time data tracking, big data utilization, and advanced data analysis techniques, companies aim to enhance flexibility, agility, and operational efficiency in their manufacturing processes. Design/methodology/approach: This research delves into operational definitions for men's hoody sweatshirts, detailing the types of machines used, specifying standard task times, and elucidating the precedence relations of operations. The study utilizes ITEX PMD as a real-time production data collection device, generating periodic line reports through the associated software program. Findings: The ITEX digital line balancing program shows promise in assisting shop floor managers to optimize sewing lines with efficiency and seamlessness, contributing to overall operational excellence. The program provides another layer of visibility into the factory, reducing non-value-added activities, and improving efficiency. The ITEX Soft algorithm facilitates an ideal assembly line layout, ensuring a balanced workload among workstations, aligned with the continuous flow principle in the sewing line. Originality/value: This shop floor and software solution not only enhance visibility but also offer a practical means to reduce non-value-added activities, thereby improving overall efficiency. The ITEX Soft algorithm emerges as a valuable tool, contributing to a more balanced workload among workstations and optimizing assembly line layout. This study sheds light on the potential of digital lean principles in reshaping manufacturing processes and fostering operational excellence in the apparel industry.

Optimized ANN for LiFePO4 battery charge estimation using principal components based feature generation

Electric vehicles (EVs) have gained prominence in the present energy transition scenario. Widespread adoption of EVs necessitates an accurate State of Charge estimation (SoC) algorithm. Integrating predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions, marking a crucial milestone in sustainable electric vehicle technology. In this research study, machine learning methods, particularly Artificial Neural Networks (ANN), are employed for SoC estimation of LiFePO4 batteries, resulting in efficient and accurate estimation algorithms. The investigation first focuses on developing a custom-designed battery pack with 12 ​V, 4 Ah capacity with a facility for real-time data collection through a dedicated hardware setup. The voltage, current and open-circuit voltage of the battery are monitored with computerized battery analyzer. The battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry Pi. Principal components are derived for the collected battery data set and analyzed for feature engineering. Three principal components were generated as input parameters for the developed ANN. Early Stopping for the ANN was also implemented to achieve faster convergence of the ANN. While considering eleven combinations for ten different optimizers loss function is minimized. Comparative analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an RMSE value of 0.4083 and an R2 Score of 0.9998. The proposed algorithm was also implemented for two different types of datasets, a UDDS drive cycle and a standard cell-level dataset. The results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup.

Predictive simulation of construction site noise emissions from heavy equipment

PurposeConstruction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.Design/methodology/approachThe paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.FindingsThe results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.Originality/valueWhile previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.

Cycling subjective experience: A conceptual framework and methods review

When evaluating cycling subjective experiences (CSE), mobility researchers have questioned the depictions of cycling as an efficient, fast, and solitary mobility mode. By reframing cycling in terms of its emotional impact on the cyclist, research to date has explored dimensions such as fun, relaxation, and sociability of cycling experiences. Yet, these insights have not been integrated into a holistic understanding of CSE. Addressing this gap, this paper asks: what is the CSE exactly and how do we measure it? We selected and analysed in-depth 50 articles with the aim of unpacking the innate characteristics of CSE. The paper makes a contribution to the research on CSE by presenting a novel framework that clarifies the relationships within existing literature, and identifies measurement methods aligned with this framework. The three interrelated core aspects of CSE are 1) sensory interpretation, 2) affective states, and 3) cognitive construction. Additionally, the three identified methods are 1) retrospective, 2) interceptive, and 3) mobile methods. Notably, retrospective surveys and interviews emerged as the dominant methods employed in the field. However, there has been a growing interest in mobile methods, which enable the collection of real-time and context-specific data, thereby enhancing the generalizability of research findings. Through our analysis we have found that the positive values of cycling experiences have been largely ignored in our sample of studies, revealing a bias of researchers to focus on mobility as a disutility. Based on our findings, we urge planners and scholars to rethink their implicit efforts to mitigate the negative effects of cycling experiences and look for opportunities to optimize for positive cycling experiences.

Smart personal protective equipment for intelligent construction safety monitoring

Purpose The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers at a job site are paramount as they face both immediate and long-term risks such as falls and musculoskeletal disorders. To mitigate these dangers, sensor-based technologies have emerged as a crucial tool to promote the safety and well-being of workers on site. The implementation of real-time sensor data-driven monitoring tools can greatly benefit the construction industry by enabling the early identification and prevention of potential construction accidents. This study aims to explore the innovative method of prototype development regarding a safety monitoring system in the form of smart personal protective equipment (PPE) by taking advantage of the recent advances in wearable technology and cloud computing.Design/methodology/approach The proposed smart construction safety system has been meticulously crafted to seamlessly integrate with conventional safety gear, such as gloves and vests, to continuously monitor construction sites for potential hazards. This state-of-the-art system is primarily geared towards mitigating musculoskeletal disorders and preventing workers from inadvertently entering high-risk zones where falls or exposure to extreme temperatures could occur. The wearables were introduced through the proposed system in a non-intrusive manner where the safety vest and gloves were chosen as the base for the PPE as almost every construction worker would be required to wear them on site. Sensors were integrated into the PPE, and a smartphone application which is called SOTER was developed to view and interact with collected data. This study discusses the method and process of smart PPE system design and development process in software and hardware aspects.Findings This research study posits a smart system for PPE that utilises real-time sensor data collection to improve worksite safety and promote worker well-being. The study outlines the development process of a prototype that records crucial real-time data such as worker location, altitude, temperature and hand pressure while handling various construction objects. The collected data are automatically uploaded to a cloud service, allowing supervisors to monitor it through a user-friendly smartphone application. The worker tracking ability with the smart PPE can help to alleviate the identified issues by functioning as an active warning system to the construction safety management team. It is steadily evident that the proposed smart PPE system can be utilised by the respective industry practitioners to ensure the workers' safety and well-being at construction sites through monitoring of the workers with real-time sensor data.Originality/valueThe proposed smart PPE system assists in reducing the safety risks posed by hazardous environments as well as preventing a certain degree of musculoskeletal problems for workers. Ultimately, the current study unveils that the construction industry can utilise cloud computing services in conjunction with smart PPE to take advantage of the recent advances in novel technological avenues and bring construction safety management to a new level. The study significantly contributes to the prevailing knowledge of construction safety management in terms of applying sensor-based technologies in upskilling construction workers' safety in terms of real-time safety monitoring and safety knowledge sharing.

Complementary use of visual and olfactory cues to assess capture of Bactrocera dorsalis (Hendel): Implementation and field verification via an IoT-based automatic monitoring system.

This study examined the effect of combining visual and olfactory cues to attract oriental fruit flies (OFFs). Six different colored light-emitting diodes (LEDs) served as a visual attractant and methyl eugenol served as olfactory bait to lure male flies. An internet of things (IoT)-based pest monitoring system, consisting of sensor nodes, a gateway, and automatic counting traps, was deployed in the field to automatically collect environmental data and pest counts. The results of the calibrated experiments indicated that green, yellow, or red LEDs exhibited better performance in attracting flies than white, purple, or blue LEDs or no LEDs. With an accurate combination of visual and olfactory cues, the proposed IoT-based pest monitoring system may be an effective tool in agricultural pest management, given its advantages for efficiently capturing OFFs in a labor and time saving manner, providing accurate information regarding increases in pest populations, and enabling long-term, real-time data collection.