Things Technology Research Articles

Bibliometric Analysis on the Application of IoT in Smart Manufacturing System in Industry 4.0

The rapid integration of Internet of Things (IoT) technologies in smart manufacturing systems has been a pivotal development within the framework of Industry 4.0. This study conducts a comprehensive bibliometric analysis to examine the evolution, current trends, and emerging research areas related to IoT applications in smart manufacturing. By analyzing data from a decade of scholarly publications, this research identifies key themes, influential authors, and collaborative networks that have shaped the field. The findings reveal that foundational technologies such as the internet, cloud computing, and big data are central to the development of smart manufacturing, while newer innovations like digital twins and blockchain are gaining prominence. The study also emphasizes the importance of strategic collaboration among researchers and industry professionals in advancing IoT adoption. The practical implications suggest that manufacturers should prioritize both foundational digital infrastructure and emerging IoT applications to enhance efficiency, security, and sustainability. This analysis provides a roadmap for future research and practical implementation, highlighting the critical areas where IoT can further revolutionize smart manufacturing systems.

Bus Tracking System With IoT Integration

The rapid advancement in Internet of Things (IoT) technologies has paved the way for innovative solutions in public transportation systems. This paper presents the development and implementation of a Bus Tracking System with IoT Integration aimed at enhancing the efficiency and reliability of public bus services. The proposed system leverages GPS and GSM/GPRS technologies to provide tracking and monitoring of buses, ensuring accurate and timely updates to passengers and transportation authorities. The system architecture comprises a NodeMCU microcontroller equipped with ESP8266 Wi-Fi module interfaced with GPS and various sensors to collect and transmit data. The collected data includes the bus's current location, speed, number of stops, and occupancy levels, which are then sent to a central server via HTTP protocols. The server processes this data and updates a web-based application accessible to passengers and administrators, providing them with real-time bus locations and estimated arrival times.

Greenhouse farming: Hydroponic vertical farming- Internet of Things (IOT) Technologies: An updated review

This review paper of literature highlights the importance of greenhouse urban farming technology, hydroponics, aeroponics, aquaponics, vertical farming and applications of Internet of Things (IOT) technologies. The greenhouse farming is a well known modern agriculture technology for optimal plant growth. Hydroponics, a soilless cultivation technique using nutrient solutions under controlled conditions, is used for growing vegetables, high-value crops, and flowers. Vertical farming is a popular trend in hydroponics that involves stacking multiple layers of plants in a vertical arrangement. This farming method saves space, reduces water usage, and increases yields per square foot of growing area. In comparison to conventional greenhouse cultivation, hydroponics requires less fertilizer, pesticides, and water due to the precise control over their distribution. The term “Internet of Things (IOT)” is a system of interconnected computing devices, sensors, objects, microcontrollers, and cloud servers that can transmit data across a network and control other devices remotely without human intervention. A better management of nutrient solution in hydroponic systems requires optimum pH, electrical conductivity (EC), or ions concentration. The three main greenhouse farming gases (GHGs) emissions in hydroponics hi-tech urban farming are nitrous oxide, methane and carbon dioxide (CO2). To measure the levels of CO2 in hydroponics, the more intermediate to advanced grower can use a CO2 monitoring and controllers system. Hydroponic vertical farming is in infancy in India. Although hydroponic vertical farming units for production of crops like strawberry, lettuce and other leafy vegetables, foliage and flowers are functioning in major metros of India. However, the organized hydroponic vertical farms for production of food crops are not available in India. Therefore, only a few successful hydroponic vertical farms have been built in India mainly due to the initial high price tag on construction and the cost of maintaining them afterwards. Another disadvantage is carbon footprint of hydroponic vertical farming is very high, and discourages its applications.

Measurement System for Concrete Bridge Deck Overlays using 3D Scanning and Internet of Things Technology

Measurement System for Concrete Bridge Deck Overlays using 3D Scanning and Internet of Things Technology

An IoT-Based Smart Street Lighting System to Lower Energy Consumption

Smart Lighting System is an automated and intelligent lighting control system that can be deployed across multiple locations or in one location, utilizing various IoT connection protocols, devices, and sensors. The system is able to intelligently adjust to changes in ambient light conditions, traffic patterns, and pedestrian activity through the integration of Internet of Things (IoT) technologies. IoT-enabled sensors are installed in every street light, creating a network that connects to a central control unit. Using real-time data, this system dynamically modifies street light brightness to maximize energy efficiency and improve safety. It provides a simulation and optimization tool for street light placement, considering factors such as street length, light distance, and the number of lights ahead. This tool aids in visualizing the power consumption implications of different configurations, promoting efficient urban planning. Demonstrates the energy-saving potential of the proposed system. As the street light intervals and the number of lights ahead decreases, the optimal street light power significantly reduces, leading to substantial energy savings. The system's ability to dim or turn off lights during low-traffic or well-lit periods contributes to reduced operational costs and enhanced sustainability.

IoT enhanced deep water culture hydroponic system for optimizing Chinese celery yield and economic evaluation

This research investigates the integration of a deep-water culture hydroponic system with Internet of Things (IoT) technology to enhance Chinese celery cultivation. Four experimental setups were implemented in 2 × 6 meter greenhouses, each replicated thrice: combined light and temperature control, temperature control only, light control only, and natural conditions. The 45-day experiment focused on extended light exposure (660 nm, 6:00-11:00 p.m.) and temperature regulation (fog sprayer above 35°C). An IoT-based system using Blynk and ESP32 maintained optimal water conditions, with monthly sensor calibration and regular maintenance ensuring reliability. Data collected every 5-7 days were analyzed using the Friedman test. Results showed the greenhouse with both controls achieved a 13.91% higher yield than natural conditions, with temperature regulation being more critical. Economic analysis indicated 14% higher profitability for the controlled greenhouse, with a 27-month breakeven point. Potential challenges like sensor failures were addressed through redundancy measures. This study highlights IoT-controlled hydroponics' potential for enhancing agricultural productivity and sustainability while acknowledging maintenance needs and scalability challenges. The findings provide valuable insights into modern farming practices, demonstrating improved yield and economic viability through precise environmental control and automated management in hydroponic systems.

FIDWATCH: Federated incremental distillation for continuous monitoring of IoT security threats

The fast evolutions of Internet of Things (IoT) technologies have been accelerating their applicability in different sectors of life and becoming a pillar for sustainable development. However, this revolutionary expansion led to a substantial increase in attack surface, raising many concerns about security threats and their possible consequences. Machine learning has significantly contributed to designing intrusion detection systems (IDS) but suffers from critical limitations such as data privacy and sovereignty, data imbalance, concept drift, and catastrophic forgetting. This collectively makes existing IDSs an improper choice for securing IoT environments. This paper presents a federated learning approach called FIDWATCH to continuously monitor and detect a broad range of IoT security threats. The local side of FIDWATCH introduces contrastive focal loss to enhance the ability of the local model (teacher) to discriminate between diverse types of IoT security threats while putting an increased emphasis on hard-to-classify samples. A fine-grained Knowledge Distillation (KD) is introduced to allow the client to distill the required teacher's knowledge into a lighter, more compact model termed the pupil model. This greatly assists the competence and flexibility of the model in resource-constrained scenarios. Furthermore, an adaptive incremental updating method is introduced in FIDWATCH to allow the global model to exploit the distilled knowledge and refine the shared dataset. This helps generate global anchors for improving the robustness of the mode against the distributional shift, thereby improving model alignment and compliance with the dynamics of IoT security threats. Proof-of-concept simulations are performed on data from two public datasets (BoT-IoT and ToN-IoT), demonstrating the superiority of FIDWATCH over cutting-edge performance with an average f1-score of 97.07% and 95.63%, respectively.

Research on jujube storage and preservation system based on Internet of Things (IoT)

With the surge in Xinjiang jujube production, there has been a concurrent increase in storage losses, posing an ongoing challenge for farmers to preserve the freshness of the jujubes. To tackle this issue, a novel storage and freshness control system for jujubes, integrating Internet of Things technology, has been developed. The control system is centered around a programmable logic controller (PLC) and utilizes an HMI as the interface for human–computer interaction. It is structured with distinct layers, including equipment, transportation, and application layers. By integrating with the Internet of Things cloud platform, data transmission between the device layer and the cloud server is ensured through the use of the TCP/IP protocol over a 4G network. A mobile app enables remote control functionality for monitoring and managing the storage and preservation environment. Experimental results demonstrate the system's stable performance, enabling remote monitoring and convenient operation by management personnel during the jujube storage and preservation process. This contributes to enhancing automation levels in jujube storage, extending its storage cycle while maintaining quality standards, thus providing valuable insights into intelligent fruit and vegetable storage practices in Xinjiang.

Monitoring battery charging using Node-RED

The need for a smart grid has been spurred by the growth of distributed generation, the aging of the current grid infrastructure, and the desire to alter networks. The development and enhancement of smart grid technology is significantly facilitated by the Internet of Things (IoT) technology. Batteries play a crucial role in the energy storage of electrical systems, including smart microgrids and electric vehicles. To enhance performance and prolong battery life, a Battery Monitoring System (BMS) is required to manage the energy storage process dynamics within the battery. Battery life prediction contributes to the consistent and efficient operation of battery-powered devices. This research presents battery charging monitoring using Nodered. Apart from that, the battery charging feature uses a cut-off concept. The cut off on the battery is to prevent the battery from over-discharging which can damage the battery and shorten its lifespan. This research carried out validation using 3 case studies on battery charging. Validation of measurements uses a comparison between the INA 219 sensor and a multimeter. From experiments, current testing from 3 case studies, it was found that the average difference in current sensor error was 0.19%.

Mechanical Design and Manufacturing and Automation Equipment Management Based on the Internet of Things

With the advent of the industry 4.0 era, the application of the Internet of Things technology in mechanical design and manufacturing and automation equipment management is increasingly extensive. This paper first summarizes the application status of the Internet of Things in this field, and puts forward the optimization strategy of mechanical equipment management based on the Internet of Things, including the construction of data integration and sharing platform, security protection system construction, talent training and team building, as well as standardization and standardization promotion. Through the implementation of these strategies, it is expected to significantly improve the efficiency and quality of mechanical design and manufacturing and its automation equipment management, and promote the development of the industry to the direction of intelligent and network.

Design of a Smart Donation Box Based on IoT

This research, entitled "IoT-based Intelligent Charity Box Design", aims to develop a charity box system that utilizes the Internet of Things (Iot) technology to enhance public participation in charity. In this context, the charity box not only serves as a container for storing donations, but is also equipped with features that can automatically monitor and calculate the amount of money inserted. The system uses the Wemos D1 Mini as a microcontroller connected to the Internet, allowing users to access data in real time through a website. In addition, this smart charity box is equipped with a PIR sensor that detects motion and triggers sound on the DFPlayer, as well as a TCS3200 color sensor that counts the paper money entered. Thus, the research not only offers technological solutions, but also seeks to remind the public of the importance of charity in a more interactive and efficient way. The results of this research are expected to benefit the management of charity funds and raise social awareness in the community.

Research on Industry 4.0 smart grid monitoring and energy management based on data mining and Internet of Things technology

With the increasing global focus on sustainability and energy efficiency, the concept of Industry 4.0 is becoming an important driver of smart grid technology advancement. Traditional power grids are faced with the challenges of high energy consumption and low management efficiency, and it is urgent to realize intelligent transformation through emerging technologies. This study aims to explore the application of data mining and Internet of Things technology in smart grid monitoring and energy management, with a particular focus on thermal energy management, in order to improve energy utilization efficiency and achieve dynamic regulation. A smart grid monitoring platform integrating data mining and Internet of Things technology is constructed. The data of power and heat energy are collected by sensors, and the data mining algorithm is used to analyze the energy consumption behavior of users and the running state of equipment. A prediction model is also introduced to realize dynamic prediction and optimal scheduling of heat demand. The experimental results show that the thermal energy management system based on the platform can significantly improve the energy utilization rate, data mining technology effectively identifies the peak energy use and potential energy saving opportunities, and the Internet of Things technology realizes real-time monitoring and feedback, enhancing the flexibility and response ability of the system. Through accurate energy monitoring and effective management strategies, it can not only realize the optimal allocation of resources, but also promote the development of enterprises in the direction of low-carbon environmental protection.

A Deep Federated Learning-Based User Credit Evaluation Model Under Financial Internet of Things Scenarios

With the rapid development of Internet of Things (IoT) technology and the digital transformation of the financial industry, the financial IoT is becoming an important trend in the future financial field. In the era of financial IoT, a large amount of data information is recorded by sensors and devices, which poses new challenges and opportunities for user credit evaluation. In this context, conventional deep learning-based user credit evaluation methods often cannot meet privacy needs. The paper combines the privacy security ability of federated learning with deep learning, and proposes a deep federated learning-based user credit evaluation model under financial IoT scenarios. First, a particle swarm optimization-based backpropagation neural network algorithm is formulated to extract user credit evaluation features, in order to obtain user behavior patterns and credit features. Then, the XGBoost algorithm is employed to output credit evaluation results. As for the training process, the thought of federated learning is integrated to distribute the model to individual participants. In such mode, the model can be updated and trained without exposing user data to the central cloud. The experiments are conducted on real-world data to assess the proposal’s performance. The results show that the proposed credit evaluation framework can achieve better accuracy, under the guarantee of user privacy.

Spatial Augmented Reality for Expanding the Reach of Individuals with Tremor beyond Their Physical Limits.

Tremor is a prevalent neurological disorder characterized by involuntary shaking or trembling of body parts. This condition impairs fine motor skills and hand coordination to varying degrees and can even affect overall body mobility. As a result, tremors severely disrupt the daily lives and work of those affected, significantly limiting their physical activity space. This study developed an innovative spatial augmented reality (SAR) system aimed at assisting individuals with tremor disorders to overcome their physical limitations and expand their range of activities. The system integrates eye-tracking and Internet of Things (IoT) technologies, enabling users to smoothly control objects in the real world through eye movements. It uses a virtual stabilization algorithm for stable interaction with objects in the real environment. The study comprehensively evaluated the system's performance through three experiments: (1) assessing the effectiveness of the virtual stabilization algorithm in enhancing the system's ability to assist individuals with tremors in stable and efficient interaction with remote objects, (2) evaluating the system's fluidity and stability in performing complex interactive tasks, and (3) investigating the precision and efficiency of the system in remote interactions within complex physical environments. The results demonstrated that the system significantly improves the stability and efficiency of interactions between individuals with tremor and remote objects, reduces operational errors, and enhances the accuracy and communication efficiency of interactions.

Optimization of diagnostic processes using decision trees in ECG data analysis - lets web system analysis engine

The article presents the capabilities of the LETS Web system, which uses Internet of Things (IoT) technology to analyze medical data to optimize diagnostic processes. The article focuses on implementing decision tree algorithms that analyze electrocardiogram (ECG) data to identify cardiac conditions. The study used three variants of decision trees that differed in structure and ECG parameters. Each variant was tested for its ability to accurately classify cardiac health conditions ranging from simple arrhythmias to complex arrhythmic changes. The study showed that modifications to the structure of the decision trees significantly affected their effectiveness. The most advanced variant of the tree, using multivariate data analysis, showed the highest efficiency in diagnosing complex conditions. The effectiveness of the different variants of decision trees varied, confirming the importance of selecting the suitable diagnostic model for the specifics of the data and clinical goals.

Machine learning and IoT system for real-time cough detection and classification

Our research investigates the application of machine learning and Internet of Things (IoT) technologies in healthcare, focusing on detecting and classifying coughing episodes. Leveraging deep learning architectures and a comprehensive IoT infrastructure, we developed an automated system capable of monitoring audio signals from a microphone array module to detect coughs and classify their types accurately. The study utilized the COUGHVID dataset for model training and evaluation, employing rigorous preprocessing techniques to ensure data integrity. Through comparative analysis, we identified MobileNet as the optimal model for cough detection, achieving promising results in accuracy, area under the ROC curve (AUC), and F1 score. Furthermore, our emphasis on privacy safeguards and remote medical examination facilitation underscores the practical implications of our research in enhancing healthcare delivery. Our study contributes to advancing technology-enabled healthcare solutions, offering valuable insights and solutions for improving patient care and outcomes.

Applied Research on Internet of Things Technology Enabling Intelligent Logistics

Applied Research on Internet of Things Technology Enabling Intelligent Logistics

A Comparative Study of Machine Learning Models for Predicting Meteorological Data in Agricultural Applications

This study aims to address the challenges of climate change, which has led to extreme temperature events and reduced rainfall, using Internet of Things (IoT) technologies. Specifically, we monitored the effects of drought on maize crops in the Republic of Croatia. Our research involved analyzing an extensive dataset of 139,965 points of weather data collected during the summer of 2022 in different areas with 18 commercial sensor nodes using the Long-Range Wide Area Network (LoRaWAN) protocol. The measured parameters include temperature, humidity, solar irradiation, and air pressure. Newly developed maize-specific predictive models were created, taking into account the impact of urbanization on the agrometeorological parameters. We also categorized the data into urban, suburban, and rural segments to fill gaps in the existing literature. Our approach involved using 19 different regression models to analyze the data, resulting in four regional models per parameter and four general models that apply to all areas. This comprehensive analysis allowed us to select the most effective models for each area, improving the accuracy of our predictions of agrometeorological parameters and helping to optimize maize yields as weather patterns change. Our research contributes to the integration of machine learning and AI into the Internet of Things for agriculture and provides innovative solutions for predictive analytics in crop production. By focusing on solar irradiation in addition to traditional weather parameters and accounting for geographical differences, our models provide a tool to address the pressing issue of agricultural sustainability in the face of impending climate change. In addition, our results have practical implications for resource management and efficiency improvement in the agricultural sector.

Supervised classification and fault detection in grid-connected PV systems using 1D-CNN: Simulation and real-time validation

Photovoltaic (PV) systems are prone to various faults, including short-circuit, open-circuit, partial shading, and inverter bypass diode issues, which reduce power output and can damage components. This study presents an innovative fault detection and online monitoring technique for grid-connected PV (GCPV) systems, combining Internet of Things (IoT) technology with a one-dimensional convolutional neural network (1D-CNN) deep learning approach. The method involves developing a temperature-dependent PV system model using series resistance and ideality factor, capturing real-time data from a 15kWp GCPV system with optimally placed sensors to minimize sensor count while maintaining data accuracy, and validating the model through MATLAB/Simulink simulations and real-time experiments under various fault scenarios. The collected data is used to train the 1D-CNN model to classify different fault types. The model is then implemented on an IoT platform for real-time monitoring and fault detection, displaying system status and alerts via a dashboard. The proposed system achieves a high fault detection accuracy of 98.15 % and 93.12 % during cyberattacks, with an uncertainty of ±4 %, significantly enhancing fault detection reliability and efficiency compared to existing methods. The IoT dashboard provides an effective tool for monitoring system performance and issuing alerts under abnormal conditions.

Lightweight ring-neighbor-based user authentication and group-key agreement for internet of drones

As mobile internet and Internet of Things technologies continue to advance, the application scenarios of peer-to-peer Internet of Drones (IoD) are becoming increasingly diverse. However, the development of IoD also faces significant challenges, such as security, privacy protection, and limited computing power, which require technological innovation to overcome. For group secure communication, it is necessary to provide two basic services, user authentication and group key agreement. Due to the limited storage of IoD devices, group key negotiation requires lightweight calculations, and conventional schemes cannot satisfy the requirements of group communication in the IoD. To this end, a new lightweight communication scheme based on ring neighbors is presented in this paper for IoD, which not only realizes the identity verification of user and group key negotiation, but also improves computational efficiency on each group member side. A detailed security analysis substantiates that the designed scheme is capable of withstanding attacks from both internal and external adversaries while satisfying all defined security requirements. More importantly, in our proposal, the computational cost on the user side remains unaffected by the variability of the number of members participating in group communication, as members communicate in a non-interactive manner through broadcasting. As a result, the protocol proposed in this article demonstrates lower computational and communication costs in comparison to other cryptographic schemes. Hence, this proposal presents a more appealing approach to lightweight group key agreement protocol with user authentication for application in the IoD.