IoT Technology Research Articles

PKM Optimalisasi Sumber Belajar Rumah Tanam Herbal Medicine Berbasis Teknologi Iot Di Sekolah Alam Rumbai

Rumbai Nature School is located on Jl Budi Sari, Rumbai District, Pekanbaru City. Rumbai Nature School is located in the same area as the Muhammadiyah University of Riau, namely in Pekanbaru City. The IOT technology-based medicinal plant planting program is one form of learning resource that can be used as a means for children to learn about various types of medicinal plants. Medicinal plants or what are known as biopharmaceuticals are types of plants that have medicinal functions and properties and are used to cure or prevent various diseases. The method used in this service is designed so that activities can be carried out properly in order to optimize the IOT technology-based medicinal plant greenhouse at Rumbai Nature School. This service program is dedicated to providing educational guidance to improve students' knowledge about plants that have medicinal properties, both in terms of complaints about medicinal plants and regarding the use of medicinal plants. The presence of this service activity increases the awareness of school residents about how to utilize medicinal plants and utilize IoT technology to see the development and growth of ideal medicinal plants. Therefore, training is needed to introduce the use of medicinal plants and IoT technology

Enhancing Building Information Modeling Effectiveness Through Coopetition and the Industrial Internet of Things

The construction industry plays a crucial role in the global economy but faces significant challenges, including inefficiencies, high costs, and environmental impacts. Although Building Information Modeling (BIM) has been widely adopted as a solution to these issues, its practical impact remains limited. This study investigates how manufacturers can enhance their contributions to improve BIM’s effectiveness, proposing that coopetition practices—combining competition and cooperation—can positively influence these contributions, thereby enhancing the benefits of BIM. To explore this hypothesis, an Experimental Coopetition Network was implemented in the Portuguese ornamental stone (POS) sector, utilizing Industrial IoT technology to facilitate collaboration among selected companies. The study assessed the impact of coopetition practices on key performance indicators related to BIM, including on-time delivery, labor productivity, and CO2 emissions. The findings demonstrate significant improvements in scheduling, operational efficiency, and environmental sustainability, validating the hypothesis that coopetition practices can enhance manufacturers’ contributions to BIM. These results suggest that coopetition practices contribute to better project outcomes, increased competitiveness, and sustainability within the construction industry. Despite the promising results, the study acknowledges limitations such as the scope of the sample size and observation periods, indicating areas for future research. This research contributes to the theoretical framework of coopetition, aligning with the United Nations Sustainable Development Goals (SDGs), and provides valuable insights for industry practitioners and policymakers seeking to implement more sustainable construction practices.

Building integrated assessment model for IoT technology deployment in the Industry 4.0

Building integrated assessment model for IoT technology deployment in the Industry 4.0

AI-Based Environmental Monitoring System with Farm Automation

This project presents an innovative approach to environmental monitoring in agricultural settings using AI-based solutions integrated with IoT technology. The system leverages data collected from various sensors such as soil moisture, gas sensors, and other environmental indicators to monitor the conditions in real-time. By utilizing an Arduino microcontroller, ESP module, and ThingSpeak server, the project is able to capture, store, and transmit sensor data effectively. The highlight of the project is the AI-based weather prediction module implemented using a Convolutional Neural Network (CNN) in Python, providing critical insights into weather conditions and allowing for data-driven decision-making. The system also includes a user-friendly web interface to display real-time monitoring and predictions, enabling farmers to better manage their resources and plan ahead for changes in environmental conditions

Deep learning-based spectrum sensing and modulation categorization for efficient data transmission in cognitive radio

Abstract One prominent feature of cognitive radio (CR) involves spectrum sensing (SS), which allows licensed primary users to remain unaffected by secondary users’ ability to discover and exploit unoccupied frequency bands. Spectrum sensing enhances the use of spectrum in CR devices, increasing their adaptability and efficiency in wireless communication systems. The rise of wireless equipment and the advent of IoT technologies compound this need for flexibility. Over time, the fixed allocation of frequencies has led to inefficiencies and underutilization as bandwidth needs increase. Deep learning and artificial intelligence have improved spectrum sensing by increasing detection probability of primary users’ presence under noisy environments, enabling cognitive radio systems to respond intelligently to fluctuations in RF environments. This article is concerned with deep learning techniques for spectrum sensing and modulation categorization with CBRT structure, which combines convolutional neural networks (CNNs), bidirectional recurrent neural networks (BRNNs), and transformer networks (TNs) to improve spectrum sensing. CNNs are responsible for performing spectrum feature extraction; BRNNs are used to capture temporal dependencies; and TNs are good at long range dependencies. Better performance for this model is aimed by integrating the three architectures described. In the proposed work, six digital modulation schemes were considered, for spectrum sensing. The sensing of spectrum in this model is performed using the RadioML2016.10B open-source dataset and performance metrics like the Jaccard Index (JI), Fowlkes’s Mallows Index, and F1 Score. Modulation classification has been performed using MIGOU-MOD open-source dataset. The proposed model exhibits good detection probability and low sensing error, unlike other methods at lower SNR.

HARNESSING BLOCKCHAIN WITH ENSEMBLE DEEP LEARNING-BASED DISTRIBUTED DOS ATTACK DETECTION IN IOT-ASSISTED SECURE CONSUMER ELECTRONICS SYSTEMS

Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.

Characterization of Stepper Motor Rotation for Automatic Self-Cleaning Litter Box Using IOT Technology

Characterization of Stepper Motor Rotation for Automatic Self-Cleaning Litter Box Using IOT Technology

Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture

AbstractAquaculture plays a vital role in global food production, and maintaining optimal water quality is essential for the health and growth of aquatic species. This research addresses the need for an efficient, adaptive solution by introducing an innovative water quality monitoring and maintenance system for aquaculture ponds. Unlike conventional systems, our approach uniquely integrates fuzzy logic with IoT technologies to optimise the precision and adaptability of pond management. The system stands out with its continuous monitoring of critical parameters such as temperature, pH, dissolved oxygen (DO), weather conditions and salinity and its ability to autonomously adjust operational controls, such as aerators and water pumps, based on dynamic environmental changes. This ensures ideal water conditions without manual intervention, providing a reliable and effective solution for aquaculture pond management. This work’s novelty lies in applying fuzzy Logic to handle the complexity and variability of aquaculture environments, allowing for nuanced control decisions that improve water quality management. The system’s efficiency was demonstrated through a 72-h operational test, where it maintained optimal DO and salinity levels, showcasing its reliability and effectiveness in real-world conditions. The fuzzy logic model has demonstrated a commendable accuracy rate of 98%. These results validate the system’s performance and underscore its practical benefits, meeting the demands of aquaculture production and significantly enhancing operational efficiency by enabling remote monitoring and rapid issue identification. This research contributes a robust technological solution for aquafarmers, offering a promising advancement in aquaculture management by improving productivity and ensuring the health and growth of aquatic species.

A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks

With the development of IoT technology, central cloud servers and edge-computing servers together form a cloud–edge communication network to meet the increasing demand for computing tasks. The data transmitted in this network is of high value, so the ability to quickly and accurately predict the traffic load of each link becomes critical to ensuring the security and stable operation of the network. In order to effectively counter the potential threat of flood attacks on network stability, we combine the Bi-directional Gated Recurrent Unit (BiGRU) model with the Dung Beetle Optimizer (DBO) algorithm to design a DBO-BiGRU short-term traffic load prediction model. Experimental validation on a public dataset shows that the proposed model has better prediction accuracy and fit than the mainstream models of RNN, LSTM, and TCN.

Developing a Cloud and IoT-Integrated Remote Laboratory to Enhance Education 4.0: An Approach for FPGA-Based Motor Control

Remote laboratories are essential in addressing access and quality challenges in technical education. They enable students from various locations to engage with real equipment, overcome geographic and economic constraints, and provide solutions during crises, such as pandemics, when in-person learning is limited. As a key element of Education 4.0, remote labs promote technical skill development, enhance engineering education, and support diverse learning approaches. This study presents a remote laboratory based on Field Programmable Gate Arrays (FPGAs), developed using a waterfall methodology integrating IoT and Cloud Computing technologies to facilitate close interaction between hardware and software. The lab focuses on controlling DC, servo, and stepper motors, allowing students to apply theoretical concepts such as digital signals, pulse-width modulation (PWM), and data representation in bits in a practical setting. The testing phase involved 50 robotics and mechatronics engineering students who participated in hands-on sessions for one month, followed by a structured survey evaluating their experience, interaction, and the educational relevance of the platform. The survey shows high student satisfaction, highlighting the platform’s strengths and identifying areas for improvement. The results also underscore the system’s potential to significantly enhance the educational experience in remote environments, aligning with the United Nations Sustainable Development Goals (SDGs).

Robotics and Automation Roadmap: Thailand Perspectives

Robotics and automation are the key industries supporting Thailand’s strategic initiatives toward Thailand 4.0. This paper presents the development of an industry roadmap guiding the future research and development of related technologies in Thailand. The roadmap project was recently conducted during 2021–2022. The strategic targets for the future development of robotics and automation in Thailand were set to increase productivity and reduce adoption cost for the short term (2022–2023); create market opportunities for AI and industrial IoT technologies with data platform for the medium term (2024–2026); and prepare the Industry 5.0 system supporting emerging applications of digital twins and metaverse for the long term (2027–2030).

A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

Integration of sophisticated technologies such as Internet of Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing systems (AMS). However, implementation of data-driven decision making in AMS still remains challenging due to data heterogeneity, real-time processing demands, and integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption of DDDM in AMS to enhance its decision-making capabilities. This framework consists of six stages: manufacturing stage, sensing stage, data stage, knowledge stage, decision stage, and application stage. The proposed framework leverages big data analytics to extract actionable insights from diverse datasets, integrates CPS to create a seamless interaction between physical and digital systems, and employs IoT technologies for real-time data acquisition and monitoring. The framework is validated through a comprehensive case study involving a CNC milling machine dataset, demonstrating significant improvements in operational efficiency, decision accuracy, and response time. The case study involves detailed data collection steps, preprocessing, and analysis, showcasing the framework’s practical implementation and effectiveness. The results show that the proposed framework addresses existing challenges and provides a scalable solution for DDDM implementation in AMS.

Foresight Study of IoT Technology in Preventing Road Crash: A Case of Car Alcohol Detector

Alcohol detector in car is the application of IoT technology that senses alcohol level consumed by the driver’s breath and prevents them from driving. Road crash that caused by drunk driver recorded the number of deaths increase and it is uncontrollable and continuously happened. The aim of this study is to identify the key drivers and determine the futures implication wheel of road crash prevention by the implementation of alcohol detector installation in car. The STEEPV analysis is used to identify key drivers and for questionnaires development. A total of 223 respondents from Klang Valley participated in the survey with a response rate of 58.07%. Based on impact-uncertainty analysis, the top two drivers are “save human lives” and “safety feature increase car safety.” The implications of saving human lives are reducing the number of deaths, improving market needs, avoiding traffic congestion, and increasing the value of human life. Whereas the implications of safety feature that increasing car safety are that it increases production activity of new car, preventing road crash, increasing car cost as well as business capabilities. Overall, this study is important for future researchers and the automotive industry by contributing a significant idea for future needs technology in car.

AI-powered IoT and UAV systems for real-time detection and prevention of illegal logging

Illegal logging poses a significant threat to environmental conservation, contributing to deforestation, ecosystem disruption, and economic losses. Traditional detection methods, such as ground patrols and satellite imagery, are often ineffective, expensive, and time-consuming. This paper introduces a novel Unmanned Aerial Vehicles (UAV)-based IoT system for chainsaw noise detection and prevention. The system uses low-cost IoT nodes with microphones and microcontrollers to detect chainsaw sounds via a Convolutional Neural Network (CNN) model. These nodes reduce power consumption, allowing the battery to last at least 8 years. Detected chainsaw sounds are transmitted to a LoRaWAN gateway and forwarded to a cloud server. The cloud server processes the data and dispatches a drone equipped with a camera to the site. The drone streams live video to the cloud, where AI algorithms analyze the footage for human presence. If detected, the drone activates sound and light alarms to deter illegal logging. Experimental results show that IoT nodes can detect chainsaw sounds up to 100 m, with the CNN model achieving an accuracy of 99.37 % and a loss of 0.0185. Comparisons with Logistic Regression, Decision Tree, Random Forest, and SVM demonstrated the superior performance of CNN. This UAV-IoT system offers a fast, reliable, scalable, and real-time solution for detecting and preventing illegal logging. Leveraging advanced AI and IoT technologies, this system enhances environmental monitoring and conservation efforts. It represents a significant step forward in the fight against illegal logging, providing a practical, effective, and sustainable solution.

IOT-Enabled Smart Healthcare: Developing Predictive Models for Chronic Disease Management Using Wearable Sensors

Abstract: Real-time monitoring and prediction models will be used to assess the efficiency of the IoT-enabled smart healthcare solutions in managing chronic diseases. From this study, in addition to the quantitative data, including the performance of the model, patient engagement, and cost savings, qualitative information obtained from the patients and healthcare professionals will be employed using a mixed-methods research approach. This collected rich data from surveys, performance measurements, real-time monitoring data, and cost analyses with 50 health professionals and 200 chronic disease patients. The results of the study indicate that predictive models, more particularly the Neural Network, enhanced the ability to identify cases of chronic illness with an accuracy of 92.4%. Extremely high patient satisfaction rates: 9.0 out of 10. Real-time monitoring shows hospital admissions falling by as much as 50% among those suffering with respiratory disease. Cost-benefit analysis also documented financial sustainability, with overall annual cost reduction at 43.3%. Soon after implementation, usage metrics also revealed that the average user uses it three times a day, while patient-provider interactions increased by 150%. This study highlights how IoT technologies would benefit patients' clinical conditions, improve chronic disease management, and save healthcare expenses.

A Proof-of-Concept Open-Source Platform for Neural Signal Modulation and Its Applications in IoT and Cyber-Physical Systems

This paper presents the design, implementation, and characterization of a digital IoT platform capable of generating brain rhythm frequencies using synchronous digital logic. Designed with the Google SkyWater 130 nm open-source process design kit (PDK), this platform emulates Alpha, Beta, and Gamma rhythms. As a proof of concept and the first of its kind, this device showcases its potential applications in both industrial and academic settings. The platform was integrated with an IoT device to optimize and accelerate research and development efforts in embedded systems. Its cost-effective and efficient performance opens opportunities for real-time neural signal processing and integrated healthcare. The presented digital platform serves as a valuable educational tool, enabling researchers to engage in hands-on learning and experimentation with IoT technologies and system-level hardware–software integration at the device level. By utilizing open-source tools, this research demonstrates a cost-effective approach, fostering innovation and bridging the gap between theoretical knowledge and practical application. Furthermore, the proposed system-level design can be interfaced with various serial devices, Wi-Fi modules, ARM processors, and mobile applications, illustrating its versatility and potential for future integration into broader IoT ecosystems. This approach underscores the value of open-source solutions in driving technological advancements and addressing skills shortages.

Indoor Air Quality System: A Design of Monitoring CO and CO2 Substances for Elderly Home Using IoT

The use of an IoT-based monitoring system for CO and CO2 gases in elderly homes is a necessary innovation for remotely monitoring air quality. This work was successful in establishing a remote monitoring system that can measure CO and CO2 gases utilizing IoT technology combined with Blynk software. The MQ-7 sensors detect CO gas, while the MQ-135 sensors detect CO2. Data from these sensors is delivered to a central server, which then sends it to the I2C 16X2 LCD panel and Blynk software, allowing data visualization via an internet connection. The study's findings indicate that this system can detect and monitor CO and CO2 gas levels with a high degree of precision. This device can also monitor air quality in real time using the Blynk software. As a result, users may accurately monitor air quality (CO and CO2 gases) in elderly homes.

Next-generation public health surveillance: extreme heat event prediction and monitoring system

Abstract Background Extreme Heat Events (EHEs) are a growing threat to public health. The increase in the frequency of occurrence of once rarer EHEs and the rise in their average temperatures are dangerously drastic for public health outcomes. Despite this, existing public health surveillance (PHS) systems fail to leverage IoT and AI technologies to facilitate real-time, continuous monitoring of EHE indicators and its associated health risks. This study closes this gap by proposing a comprehensive PHS system that can, in real-time monitor and predict EHE indicators to provide timely alerts to public health authorities. Methods The EHE PHS system collects EHE detection metrics, including indoor temperature and humidity levels, from IoT sensors and thermostats to map them across Canada, primarily focusing on low-income communities. The system also integrates historical climate data (surface temperature, humidity, wind speed and direction, and air quality indicators) from Environment Canada. The system uses the data to train initial prediction models including CNN, LSTM and GNN. The validated model will be integrated into the system, enabling real-time EHE prediction. The output will be displayed in user-friendly visualizations on the EHE PHS system’s dashboard capabilities. Results The system allows for the analysis of real-time data through dashboards and provides alerts when certain indicators (e.g. excessive or prolonged heat) detrimental to public health outcomes are detected. The alerts enable valuable lead time for public health authorities to implement proactive measures, such as issuing heat advisories and deploying resources to vulnerable areas. Conclusions The EHE PHS system will serve as a blueprint for global public health researchers, utilizing IoT and AI technologies for proactive crisis prevention. This knowledge will inform the development of heat-resilient policies and set a precedent for global public health crisis prevention. Key messages • The proposed EHE PHS system uses AI and IoT technologies, to enhance EHE prediction, risk identification, and real-time monitoring for effective public health interventions. • By integrating advanced predictive models and environmental data, the system facilitates early detection of EHE risk, which can significantly mitigate the health impact on vulnerable populations.

Advancements and Challenges in Antenna Design and Rectifying Circuits for Radio Frequency Energy Harvesting.

The proliferation of smart devices increases the demand for energy-efficient, battery-free technologies essential for sustaining IoT devices in Industry 4.0 and 5G networks, which require zero maintenance and sustainable operation. Integrating radio frequency (RF) energy harvesting with IoT and 5G technologies enables real-time data acquisition, reduces maintenance costs, and enhances productivity, supporting a carbon-free future. This survey reviews the challenges and advancements in RF energy harvesting, focusing on far-field wireless power transfer and powering low-energy devices. It examines miniaturization, circular polarization, fabrication challenges, and efficiency using the metamaterial-inspired antenna, concentrating on improving diode nonlinearity design. This study analyzes key components such as rectifiers, impedance matching networks, and antennas, and evaluates their applications in biomedical and IoT devices. The review concludes with future directions to increase bandwidth, improve power conversion efficiency, and optimize RF energy harvesting system designs.

Cyber-Physical Systems and IoT: Transforming Smart Cities for Sustainable Development

Smart cities CPS/ IoT are impacting sustainable development in modern cities. This research explore how CPS and IoT integration into infrastructure systems in cities can improve sustainability in aspects such as energy, waste, and environment. Finally, taking quantitative data on energy and pollution saving and resource utilization from the smart cities around the world, the paper quantifies the case analysis. The research method entails retrieving data from existing smart city projects and then using statistical tools to evaluate the differences in sustainability targets with and without CPS and IoT integration. The main discoveries indicate that places implementing CPS and IoT technologies have recorded reduction in energy use by up to 25 % and enhanced waste management by up to 30% thus exhibiting a reduced carbon footprint. The originality of this research is based on the application of CPS- IoT in analysing their practical implications for urban sustainability with relevant recommendations for policymakers, urban planners and technologists. This study not only serves as the research for the academic literature but also advises cities into how they would be able to build upon their sustainable initiatives through the adoption of advanced technology. The outcomes underscore the possibility of the strategic application of CPS and IoT to enhance the creation of smarter urban contexts.