Things Technology Research Articles

Secure-by-Design Real-Time Internet of Medical Things Architecture: e-Health Population Monitoring (RTPM)

The healthcare sector has undergone a profound transformation, owing to the influential role played by Internet of Medical Things (IoMT) technology. However, there are substantial concerns over these devices’ security and privacy-preserving mechanisms. The current literature on IoMT tends to focus on specific security features, rather than wholistic security concerning Confidentiality, Integrity, and Availability (CIA Triad), and the solutions are generally simulated and not tested in a real-world network. The proposed innovative solution is known as Secure-by-Design Real-Time IoMT Architecture for e-Health Population Monitoring (RTPM) and it can manage keys at both ends (IoMT device and IoMT server) to maintain high privacy standards and trust during the monitoring process and enable the IoMT devices to run safely and independently even if the server is compromised. However, the session keys are controlled by the trusted IoMT server to lighten the IoMT devices’ overheads, and the session keys are securely exchanged between the client system and the monitoring server. The proposed RTPM focuses on addressing the major security requirements for an IoMT system, i.e., the CIA Triad, and conducts device authentication, protects from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, and prevents non-repudiation attacks in real time. A self-healing solution during the network failure of live e-health monitoring is also incorporated in RTPM. The robustness and stress of the system are tested with different data types and by capturing live network traffic. The system’s performance is analysed using different security algorithms with different key sizes of RSA (1024 to 8192 bits), AES (128 to 256 bits), and SHA (256 bits) to support a resource-constraint-powered system when integrating with resource-demanding secure parameters and features. In the future, other security features like intrusion detection and prevention and the user’s experience and trust level of such a system will be tested.

Development and Application of IoT Monitoring Systems for Typical Large Amusement Facilities.

The advent of internet of things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues for real-time surveillance and assessment of the operational conditions of intricate mechanical systems. Nowadays, mechanical system monitoring technologies are extensively utilized in various sectors, such as rotating and reciprocating machinery, expansive bridges, and intricate aircraft. Nevertheless, in comparison to standard mechanical frameworks, large amusement facilities, which constitute the primary manned electromechanical installations in amusement parks and scenic locales, showcase a myriad of structural designs and multiple failure patterns. The predominant method for fault diagnosis still relies on offline manual evaluations and intermittent testing of vital elements. This practice heavily depends on the inspectors' expertise and proficiency for effective detection. Moreover, periodic inspections cannot provide immediate feedback on the safety status of crucial components, they lack preemptive warnings for potential malfunctions, and fail to elevate safety measures during equipment operation. Hence, developing an equipment monitoring system grounded in IoT technology and sensor networks is paramount, especially considering the structural nuances and risk profiles of large amusement facilities. This study aims to develop customized operational status monitoring sensors and an IoT platform for large roller coasters, encompassing the design and fabrication of sensors and IoT platforms and data acquisition and processing. The ultimate objective is to enable timely warnings when monitoring signals deviate from normal ranges or violate relevant standards, thereby facilitating the prompt identification of potential safety hazards and equipment faults.

A Hybrid Machine Learning Approach for Intrusion Detection and Mitigation on IoT Smart Healthcare

Strong cybersecurity solutions are becoming more and more important as Internet of Things (IoT) technology integration in healthcare settings develops. This study offers a method for feature extraction, selection, and attack classification by fusing the discriminative capacity of feedforward neural networks (FNNs) with the adaptability of fuzzy logic systems. In delicate healthcare database of IoT wearable devices, to reduce false alarm and guaranteeing intrusion detection dependability are the main priorities. The suggested method uses a feature extraction, selection technique, training and testing based on FNN, which allows the model to adjust to the dynamic and varied character of medical data. During the assessment stage, a dataset including a range of healthcare IoT scenarios, including different kinds of attacks, is used to train and evaluate the model, the ToN_IoT dataset was used. Fuzzy logic improves the system's resilience in identifying pertinent features by managing uncertainties and imprecise input. Fuzzy logic is one of the best technique for handling uncertainty, its linguistic representation and rule reasoning helps in better identification and classification. The findings indicate a noteworthy decrease in the frequency of false alarms when juxtaposed with conventional intrusion detection systems. Results obtained from the model are 99.2, 98.8, 99.5, 99.1 & 0.008 for accuracy, precision, recall, F1-Score and False alarm respectively. Promising outcomes in protecting IoT healthcare environments are demonstrated by the suggested system, opening the door to better patient data privacy and system resilience against cyberattacks.

Implementing Internet of Things for Real-Time Monitoring and Regulation of Off-Season Grafting and Post-Harvest Storage in Citrus Cultivation: A Case Study from the Hilly Regions of Nepal

Citrus fruit cultivation, especially mandarin oranges, is crucial to the economy of Nepal’s hilly regions due to their ideal geoclimatic conditions. Despite its economic importance, the sector faces several challenges, such as inadequate grafting techniques, low-quality saplings, and ineffective post-harvest storage. This paper explores these issues and proposes innovative solutions through the use of Internet of Things (IoT) technology. To address these challenges, we identified key areas for improvement. First, we focused on extending grafting practices during the off-season to ensure a higher success rate and better-quality saplings. Second, we examined different post-harvest storage methods to determine their effectiveness in terms of shelf life, decay loss, and quality of fruit. In addition to exploring post-harvest strategies, this paper provides preharvest recommendations for farmers, emphasizing methods to enhance fruit quality and longevity through effective pre-storage practices. Our IoT-based approach introduces off-season grafting in polyhouses and advanced monitoring for post-harvest storage. The results are promising: We achieved grafting success rates of 91% for acid lime and 92% for local mandarin orange varieties. Additionally, our research compared different post-harvest storage methods for mandarin oranges, including room, cellar, and cold chamber. We assessed these methods based on shelf life, physiological weight loss, and the total soluble solids (TSS) to titratable acidity (TA) ratio. The cold chamber proved to be the most effective method, offering superior conditions for storing mandarin oranges. The IoT-based monitoring system played a crucial role in maintaining optimal temperature, humidity, and gas content within the cold chamber, resulting in reduced post-harvest losses and extended shelf life. These findings highlight the transformative potential of IoT technology in mandarin orange cultivation and post-harvest storage.

Field performance of 15 rain gardens in different cities in Taiwan

Rain gardens are widely used for low impact development (LID) or as a nature-based solution (NbS). They help to reduce runoff, mitigate hot temperatures, create habitats for plants and insects, and beautify landscapes. Rain gardens are increasingly being established in urban areas. In Taiwan, the Ministry of Environment (MoE) initiated a rain garden project in Taipei city in 2018, and 15 rain gardens have since been constructed in different cities. These Taiwanese-style rain gardens contain an underground storage tank to collect the filtrated rainwater, which can be used for irrigation. Moreover, the 15 rain gardens are equipped with sensors to monitor temperature, rainfall, and underground water levels. The monitoring data were transmitted with Internet of Things (IoT) technology, enabling the capture and export of real-time values. The water retention, temperature mitigation, water quality, and ecological indices of the rain gardens were quantified using field data. The results from the young rain gardens (1–3 years) showed that nearly 100 % of the rainfall was retained onsite and did not flow out from the rain gardens; however, if the stored water was not used and the tanks were full, the rainwater from subsequent storms could not be stored, and the tanks overflowed. The surface temperatures of the rain garden and nearby impermeable pavement differed by an average of 2–4 °C. This difference exceeded 20 °C in summer at noon. The water in the underground storage tanks had very low levels of SS and BOD, with averages of 1.6 mg/L and 5.6 mg/L, respectively. However, the E. coli concentrations were high, and the average was 6283 CFU/100 mL; therefore, washing or drinking water is not recommended. The ecological indices, i.e., the Shannon and Simpson indices, demonstrated the good flora status of the rain gardens after one year. Although the weather differed by city, the performance of the rain gardens in terms of water retention, temperature mitigation, rainwater harvesting, and providing biological habitats was consistent. However, maintenance influences rain garden performance. If the stored water is not frequently used, the stored volume is reduced, and the stored water quality degrades.

Enhanced heart disease prediction in remote healthcare monitoring using IoT-enabled cloud-based XGBoost and Bi-LSTM

The advancement of medical technology has brought about a significant transformation in remote healthcare monitoring, which is crucial for providing customized care and ongoing observation. This is especially important when it comes to controlling long-term illnesses like high blood pressure, which raises the risk of heart disease considerably, especially in older people. This methodology achieves greater accuracy by combining regular medical monitoring and Electronic Clinical Data (ECD) from complete medical records with physical data from patients' routine medical monitoring. This innovative technique enhances the area of cardiac disease prediction. A technique that uses cutting-edge machine learning models and IoT technology to meet this demand. In particular, we use the powerful Extreme Gradient Boosting (XGBoost) algorithm to effectively examine big datasets and extract important characteristics to improve prediction accuracy. The deep learning model Bidirectional Long Short-Term Memory (Bi-LSTM) is used to further enhance prediction skills to extract complex temporal patterns from patient data. It outperformed naive Bayes, decision trees, and random forests with our approach, achieving a greater prediction accuracy of 99.4 %. With the combination of Internet of Things technologies and sophisticated machine learning models, this paper offers a novel approach to remote healthcare monitoring.

An IoT vehicle management framework based on optimized license plate recognition

With the continuous development of urban traffic and the increasing number of vehicles, vehicle management has become increasingly crucial. The Harmony-based IoT vehicle management system, leveraging Internet of Things (IoT) technology, offers an effective solution. This paper introduces the concept and principles of the Harmony-based IoT vehicle management system, focusing on optimized license plate recognition. Firstly, the principles and optimization methods of license plate recognition technology are elaborately described, encompassing aspects such as image processing, feature extraction, and pattern recognition. Subsequently, the paper explores the application of advanced YOLOv7 license plate recognition technology in the Harmony-based IoT vehicle management system, incorporating discussions on interlinking with devices like gate control systems.

Identifying popular, rejected and neglected children in Chinese preschool: An exploratory study on the educational application of spatial positioning data

Amidst the rapid advancement of Internet of Things (IoT) technology and the burgeoning field of Multimodal Learning Analytics (MMLA), this study employs spatial positioning technology as a case study to investigate the potential of multimodal data in assessing children's social development. This study combines the spatial positioning data of preschool children collected during free play sessions in natural educational settings and the spatial metrics constructed based on observational studies to establish and validate a sociometric status Decision Tree classification model. The findings suggest that the model can overall accurately identify children with three distinct sociometric statuses, albeit with some variability in efficacy across different sociometric groups and age groups. Notably, the model demonstrates a high hitting rate in identifying the potentially neglected children, providing valuable support for educators in understanding and fostering children's developmental needs. This study also highlights the advantages of emerging technology and multimodal data application in child development assessment.

Design and implementation of embedded hardware in furniture control based on DSP

The continuous progress of embedded technology and Internet of Things technology has accelerated the arrival of the era of home intelligence. By designing a smart home control system based on embedded DSP technology, it can detect temperature, humidity, flammable gas leakage, smoke, fire, and other indicators in the home environment, and carry out intelligent control of the detected security risks. The core of the system is the smart home management terminal, which is responsible for the network interconnection inside and outside the home. Terminal hardware includes a high-speed DSP chip, various interface modules, and so on. The software adopts the C/OS-II operating system to realize multi-task processing. This paper briefly summarizes DSP technology and embedded technology, and proposes the composition of intelligent home control system based on embedded DSP technology, as well as the hardware and software design and implementation of the system. Through the smart home system, users can control home conditions and obtain public service information anytime and anywhere, which is highly practical.

Smart Nutrient Management in Hydroponics: IoT-Driven Optimization for Enhanced Crop Yield and Resource Efficiency

Hydroponics is a potential option to farming in dirt because it gives you more control over how much water and nutrients are used. However, managing nutrients optimally in hydroponic systems is still a difficult task. This research suggests a smart nutrition management system that uses Internet of Things (IoT) technologies to get the most out of resources and increase food growth. The framework has sensors built in that can check external factors like pH, electrical conductivity, and nutrient amounts in the hydroponic solution in real time. These sensors send constant streams of data, which lets the exact amounts of nutrients be changed to meet the needs of different plants at different times of growth. Machine learning techniques are used to look at monitor data and guess what the best food mixes will be. The system changes with the times by using past data and weather models. This keeps resources from going to waste and increases food yield. The IoT system also allows tracking and control from afar, which makes upkeep and strategic actions easier. Automated alerts let farmers know when conditions aren't as good as they should be, so they can make changes quickly to avoid crop damage or nutrition shortages. Experiments are done in a controlled growing setting to see how well the planned system works. Compared to standard ways, the results show big gains in food growth and resource economy. It can also be scaled up or down, and it can be changed to work with different types of crops and environments.

A revocable and comparable attribute-based signature scheme from lattices for IoMT

The Internet of Medical Things (IoMT) refers to the utilization of the Internet of Things (IoT) technology in the healthcare industry. Access control is particularly important for IoMT due to the sensitive nature of patient data, such as electronic health records (EHR). Although fine-grained access control can be achieved by attribute-based signature (ABS), existing ABS schemes lacks the ability to provide range comparison access policy in which patients can check whether the attribute value is within a certain range. In addition, the dynamic change of doctors’ permissions and the development of quantum technology also require that ABS can provide user revocation and resist quantum attacks. In order to solve these problems and make ABS schemes for IoMT more useful and more secure, we come up with a novel revocable and comparable ABS (RC-ABS) from lattices for IoMT, enabling fine-grained access control, attribute range comparison, and user revocation. First of all, we provide a proof of the unforgeability of our scheme in the standard model (SM) under the selective user revocation list semi-adaptive attribute adaptive message attack, which can resist attacks by semi-honest adversaries and prevent adversaries from attacking the weaknesses of hash functions in IoMT. Furthermore, our ABS scheme leverages the small integer solution problem (SIS) to effectively defends against quantum algorithm attacks. Finally, detailed performance analysis demonstrates that our solution not only possesses enhanced security features and functionalities, such as revocability, comparability, and resistance against quantum attacks, but also maintains a constant computation cost of user revocation, making it more efficient compared to other revocation schemes.

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings.

A wireless, remotely operable and easily customizable robotic flower system

Abstract Understanding the complex interactions between external and internal factors that influence pollinator foraging behaviour is essential to understand ecosystem functioning, design agricultural practices or develop effective conservation strategies. However, it remains challenging to collect large and reliable data sets with reasonable personnel and workload. In this study, we present a wireless and cost‐effective robotic flower equipped with internet of things (IoT) technology that automatically offers nectar to visiting insects while monitoring visitation time and duration. The robotic flower is easy to manipulate and settings such as nectar refill rates can be remotely altered, making it ideal for field settings. The system transmits data completely wirelessly and autonomously, is mobile and easy to clean. The prototype settings allow for approximately 2 weeks of uninterrupted data collection for each battery charge. As a proof‐of‐concept application, a foraging preference dual choice experiment with bumblebees was performed. On average, more than 7000 flower visits per colony were registered daily with a set‐up consisting of 16 robotic flowers. The data show a gradual preference shift away from the pre‐trained low concentration, confirming the hypothesis of favouring sugar water with higher concentration. The robotic flower provides accurate and reliable data on insect behaviour, significantly reducing the price and/or labour costs. Although primarily designed for (bumble)bees, the system could be easily adapted for other flower‐visiting insects. The robotic flower is user‐friendly and can be easily adapted to address a wide range of research questions in pollination ecology, conservation biology, biocontrol and ecotoxicology, and allows for detailed studies on how nectar traits, flower colour and shape or pollutants affect foraging behaviour.

Integrating IoT Technology with a Systems Engineering Approach to Improve the GHG Emission Accounting in the Waste Management Industry

AbstractThis work presents how to automate emission accounting and analysis in the waste management industry. The methodology adopted is based on the combined use of Internet of Things (IoT) technology and a Systems Engineering approach. The presented methodology has been tested in an industrial case. In the case, there were multiple systems available to collect environmental data. However, the accessibility and the interpretability of this environmental data were observed as a challenge. After gathering the data in a centralized database, the automation of the Green House Gasses (GHG) emission management and accounting was performed. Findings show that the operational emissions of the industry partner mainly occur from energy and fuel consumption. By measuring and categorizing energy usage, the industry partner identified several potential improvements for reducing emissions. Lowering energy usage can consequently decrease the associated carbon footprint. Finally, the authors suggest some useful insights for companies with the aim of improving the effectiveness and efficiency of industrial GHG emissions accounting.

Integrating IoT Technology with a Systems Engineering Approach to Improve the GHG Emission Accounting in the Waste Management Industry

AbstractThis work presents how to automate emission accounting and analysis in the waste management industry. The methodology adopted is based on the combined use of Internet of Things (IoT) technology and a Systems Engineering approach. The presented methodology has been tested in an industrial case. In the case, there were multiple systems available to collect environmental data. However, the accessibility and the interpretability of this environmental data were observed as a challenge. After gathering the data in a centralized database, the automation of the Green House Gasses (GHG) emission management and accounting was performed. Findings show that the operational emissions of the industry partner mainly occur from energy and fuel consumption. By measuring and categorizing energy usage, the industry partner identified several potential improvements for reducing emissions. Lowering energy usage can consequently decrease the associated carbon footprint. Finally, the authors suggest some useful insights for companies with the aim of improving the effectiveness and efficiency of industrial GHG emissions accounting.

Smart solar maintenance: IoT-enabled automated cleaning for enhanced photovoltaic efficiency

This innovative project aims to increase the effectiveness and user experience of solar panel systems by introducing a state-of-the-art dust and speck removal system. Leveraging cutting-edge technology, the system demonstrates a remarkable 32% increase in power output compared to dirty solar panels. The approach is characterized by its reliance on the universe as the system controller, reducing the need for manual intervention and minimizing the workforce required for panel cleaning. The proposed timed system utilizes water and wipers, facilitated by internet of things (IoT) technology, microcontrollers, and sensor modules for efficient and automated operation. An Android application provides user control and notifications about ongoing processes. The system’s adaptability for various settings is emphasized, offering a portable solution. The smart IoT based automatic solar panel cleaning ensures reliable performance, underscoring the project’s commitment to improve scalability, cost-efficiency, performance, integrity, and consistency.

The influence mechanism analysis on the farmers’ intention to adopt Internet of Things based on UTAUT-TOE model

Internet of Things (IoT) technologies are widely recognized as one of the most important infrastructures for economic development and technological innovation. By analyzing the influencing factors of vegetable farmers’ intention to adopt agricultural IoT, it helps to formulate effective IoT promotion policies and accelerate the realization of agricultural modernization. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology-Organization-Environment (TOE) theory, this study constructed for the first time a mechanism model of the influence of vegetable farmers’ intention to adopt IoT, expanding the scope of current research on agricultural IoT and revealing the intrinsic influence mechanism of farmers’ adoption of IoT technologies. In this study, 357 quantitative data were obtained by a questionnaire survey, and structural equation modeling was used to test the direct and indirect effects of each factor on vegetable farmers’ intention to adopt IoT. The results show that almost all variables in TOE have significant direct impacts on the intention, while no variables in UTAUT have significant direct impacts. Among variables in TOE, government support and complexity are the two most important elements influencing the intention. Although the interactions among variables in TOE and UTAUT are also found, the indirect effects of variables are non-significant. Therefore, it is proposed to reduce the complexity of operation and use of IoT technologies; improve rural information infrastructure and compatibility of IoT platforms and devices; and governments should increase subsidies, and incentives to promote the use of IoT in agriculture and agricultural practices.

Performance evaluation of multiclass classification models for ToN-IoT network device datasets

Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.

IoT Embedded Smart Monitoring System with Edge Machine Learning for Beehive Management

The need of an automated support system that helps beekeepers maintain and improve beehive population was always a very stressing aspect of their work considering the importance of a healthy bee population. This paper presents a proof of concept, further referred as a PoC solution, based on the Internet of Things technology which proposes a smart monitoring system using machine learning processes, diligently combining the power of edge computing by means of communication and control. Beehive maintenance is improved, having an optimal state of health due to the Deep Learning inference triggered at the edge level of devices which processes hive’s noises. All this is achieved by using IoT sensors to collect data, extract important features and a Tiny ML network for decision support. Having Machine Learning inference to be performed on low-power microcontroller devices leads to significant improvements in the autonomy of beekeeping solutions.

AIoT-enabled decentralized sensor fault diagnosis for structural health monitoring

Structural health monitoring (SHM) systems collect and analyze data recorded from civil infrastructure to detect changes in the structural condition and to predict potential damage. However, sensor faults in SHM systems may lead to erroneous structural assessments, thus to inefficient maintenance operations, which could jeopardize the integrity of the structures. Artificial intelligence (AI) algorithms based on analytical redundancy have proven being effective foundations to implement sensor fault diagnosis but are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. Moreover, operating with centralized servers increases the likelihood of single points of failure, which compromises the robustness of SHM systems. To overcome the limitations and to enhance the robustness of SHM systems, this paper proposes a decentralized approach towards fault-tolerant SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Fault diagnosis algorithms based on AI are embedded into sensor nodes deployed for SHM, using Internet of Things technologies to be computed at the edge of civil infrastructure, avoiding large amounts of data to be sent to central servers. In addition, the sensor nodes embed algorithms that collect, process, and analyze the sensor data to assess the condition of the structures being monitored. The result is a smart (i.e., decentralized, fault-tolerant, and AIoT-based) SHM system that is calibrated in a laboratory experiment and then deployed on a bridge for field validation. It is expected that the approach presented in this paper will improve the robustness of SHM systems in assessing the structural condition of civil infrastructure.