Internet Of Things Research Articles

Smart farming based on IoT to predict conditions using machine learning

<span>Smart farming is a type of technology that utilizes the internet of things (IoT) to provide information on agricultural and environmental conditions as well as perform automation. Some of these ecological conditions can be used and analyzed in machine learning (ML) data management. This study focuses on utilizing ML algorithms to find the best prediction; typically used methods include linear regression, decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). In the application of smart farming, research on IoT and artificial intelligence (AI) is still uncommon since most IoT cannot make predictions like AI. Because basically, some IoT can't make predictions as AI does. In this Study, predictions were made by looking at the regression results in the form of root mean square error (RMSE) and absolute error. The results show a strong and weak correlation between features (positive or negative). The best prediction results are obtained by XGBoost when predicting temperature (RMSE 6.656 and absolute error 3.948) and (soil moisture 17.151 and absolute error 11.269). However, using different parameters (RMSE RF and absolute error DT) on RF and DT resulted in good and distinct results. Linear regression, on the other hand, produced unsatisfactory and poor result.</span>

Evolutionary Stress Detection Framework through Machine Learning and IoT (MLIoT-ESD)

Background: Life nowadays is full of stress due to lifestyle changes and the modernera race. Almost everyone around us is suffering from stress and anxiety. Mostly, stress identification is done by medical practitioners in a very late stage in which suitable help measures cannot be provided and hence result in suicides or early age deaths due to cardiac arrest, etc. One major reason behind the delay is the time required in stress identification by traditional approaches, and above that, the amount of time and financial support expected is always not feasible to be available. Hence, in this paper, we proposed an evolutionary research framework for stress identification by the usage of both machine learning and IoT. Here, we also conducted a pilot study on 83 records available over the decade since 2014 using PRISMA guidelines, and a bibliographic network visualization was also performed using VOS viewer. Objectives: This study aimed to develop a stress detection framework using Machine Learning and the Internet of Things (IoT) as technology advanced over a decade. Methods: More than 80 research papers from honorable repositories like Scopus and Web of Science were gathered according to the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020, and the VOSviewer tool was further applied to construct the bibliographic depictions. Various datasets and methods used over ten years with their performance were also discussed. Results: This research was conducted to gather various types of stressors, the impact of various Machine Learning and IoT algorithms and concepts on various datasets and their respective results. Conclusion: Various available datasets and results with multiple algorithms were discussed in a crisp tabular form for better understanding. A methodology based on an amalgamation of Machine Learning and IoT was also proposed due to various research gaps available so that stress detection could be done in a cost-effective way.

Reconfigurable data intensive service for low latency cyber-physical systems and IoT communication

<span>The fourth industrial revolution is realized through the many developments in cyber-physical systems (CPS) made possible by the widespread use of the internet of things (IoT). CPS sensor networks must enable mobile and wireless CPSs with their specific flexibility and heterogeneity needs without compromising quality of service (QoS). The research article focuses on reconfigurable data communication hardware for numerous IoT-supporting infrastructures and performance estimation using delay, power, throughput, and packet delivery ratio (PDR) for different IoT node configurations. Tree topology-based network configuration from cloud data to sensor fog organizers, sensor network directors, and IoT-embedded sensors is supported. Functional simulation is performed in iFoGSim, Xilinx ISE, and Modelsim 10.0 with a maximum of 64 variable nodes programmed for data communication and interplay verification with a minimum delay of 9.1 ns, maximum frequency of 319 MHz, power of 7.5 mW, throughput of 0.280, and maximum PDR=1. The simulation is applicable for fog computing and CPS processed from different alters in specific topologies.</span>

A novel smart irrigation framework with timing allocation using solenoid valves and Arduino microcontroller

Irrigation in agriculture is the most common way of providing water to agricultural land or fields at normal stretches through channels and embedded platforms with the internet of things (IoT), to upgrade rural development. In this paper, the arrangement of the various types of irrigation systems and embedded platforms for agriculture was studied. The embedded platform can be designed in a suitable framework that can assist the irrigation system in growing more water-required crops. In this work, three relay switches, two solenoid valves, and one water pump source were connected to Arduino ESP32. The free version of Sinric Google Cloud was utilized significantly to control three devices namely, two solenoid valves using two relay switches and a water pump source using one relay switch. The experiment was executed in a prototype manner with timing allocation by considering two agricultural fields where water was supplied either in one field at a time and showed more prominent results to save time, replacement of manual valves, man intervention, power, and suitable quantity of water for more water-required crops namely, arecanut and coconut.

Air quality monitoring system based on low power wide area network technology at public transport stops

<span>Mass migration from rural areas to urban areas has caused problems of traffic congestion, high industrial concentration and inequity in the distribution of housing in the world's capitals, generating a significant threat to sustainable development and public health due to air pollution air. In the Peruvian context, the importance of real-time monitoring of air quality is highlighted according to the standards established by the government. Several studies propose real-time environmental monitoring systems using internet of thing (IoT) technologies, electrochemical and optical sensors to measure pollutants, highlighting the need for data analysis. The objective of the paper is to show the implementation of IoT devices called sensor nodes, with long range wide area network (LoRaWAN) transmission technology for continuous monitoring of polluting gas concentrations. In addition, they are integrated into a central node called gateway to perform real-time monitoring through a web application. As an initial result, IoT devices demonstrated their effectiveness for real-time monitoring. Despite being a prototype-level result, the next stage involves its deployment at public transport stops in Lima. Overcoming the limitations of the solution, this paper establishes the foundation for future research on pollution and public health.</span>

CoFe2O4-BaTiO3 Core-Shell-Embedded Flexible Polymer Composite as an Efficient Magnetoelectric Energy Harvester

Flexible magnetoelectric (ME) generators gained immense interest due to the broad applications in wearable and Internet of Things (IoT)-based devices. The key to achieving high energy conversion performance of 0-3 type ME composite films is the prevention of filler aggregation in the polymer matrix and accessing the full potential of intrinsic properties of filler. To achieve high performance, a flexible ME composite film was fabricated by homogeneous distribution of magnetostrictive CoFe2O4-BaTiO3 core-shell (CBCS) fillers into piezoelectric polyvinylidene fluoride (PVDF) polymer. The ME composite film generates an enhanced energy conversion efficiency by optimizing the shell thickness of CBCS and maximizing the electroactive β-phase at the BaTiO3 shell-PVDF interfacial region. The observed ME coefficient of the film reached up to 710 mV/cm∙Oe. Multiphysics simulations based on the finite element analysis were adopted to investigate the role of BaTiO3 shell thickness on the performance of ME film. This study paves the way to achieve higher filler loading content in the ME composite films to develop an efficient, flexible ME generator for eco-friendly permanent power sources.

Timing issues on power side-channel leakage of advanced encryption standard circuits designed by high-level synthesis

In recent years, field programmable gate array (FPGA) have been used in many internet of things (IoT) devices and are equipped with cryptographic circuits to ensure security. However, they are exposed to the risk of cryptographic keys being stolen by side-channel attacks. Countermeasures against side-channel attacks have been developed, but they are becoming more of a threat to IoT devices due to the diversity of attacks. Therefore, it is necessary to understand the basic characteristics of side-channel attacks. Therefore, this study clarifies the relationship between two timing issues, the clock period of the circuit and the power sampling interval, and the amount of side-channel leakage. We design seven advanced encryption standard (AES) circuits with different clock periods and conduct empirical experiments using logic simulations to clarify the correlation between the two timings and the amount of side-channel leakage. T-test is used to evaluate the leakage amount, which is evaluated based on four metrics. From the results, we argue that the clock period and sampling interval do not interfere with each other in the side-channel leakage amount.

Precision medicine in hepatology: harnessing IoT and machine learning for personalized liver disease stage prediction

<span>In this research, we used a dataset from Siksha ‘O’ Anusandhan (S’O’A) University Medical Laboratory containing 6,780 samples collected manually and through internet of things (IoT) sensor sources from 6,780 patients to perform a thorough investigation into liver disease stage prediction. The dataset was carefully cleaned before being sent to the machine learning pipeline. We utilised a range of machine learning models, such as Naïve Bayes (NB), sequential minimal optimisation (SMO), K-STAR, random forest (RF), and multi-class classification (MCC), using Python to predict the stages of liver disease. The results of our simulations demonstrated how well the SMO model performed in comparison to other models. We then expanded our analysis using different machine learning boosting models with SMO as the base model: adaptive boosting (AdaBoost), gradient boost, extreme gradient boosting (XGBoost), CatBoost, and light gradient boosting model (LightGBM). Surprisingly, gradient boost proved to be the most successful, producing an astounding 96% accuracy. A closer look at the data showed that when AdaBoost was combined with the SMO base model, the accuracy results were 94.10%, XGBoost 90%, CatBoost 92%, and LightGBM 94%. These results highlight the effectiveness of proposed model i.e. gradient boosting in improving the prediction of liver disease stage and provide insightful information for improving clinical decision support systems in the field of medical diagnostics.</span>

Internet based highly secure data transmission system in health care monitoring system

<span>The health care systems in our contemporary countries are advancing rapidly in terms of maturity and professionalism. In an effort to alleviate the current burden on the public health system and boost the popularity of regular health self-checks, this method has been developed for producing prediagnoses that are easier to use, quicker, and more accurate. To ascertain how well the heart is circulating oxygen throughout the body, a pulse test, a painless examination that measures an individual's degree of oxygen saturation, is used. It can be used to evaluate the state of any patient with a disease, particularly those with pulmonary problems. Diseases in these patients could need ongoing observation and care. Our system comes to the rescue in order to resolve this problem. This portable system is simple to use and may be taken anywhere by the subject. The internet of things (IoT) will update the pertinent parameters. This health monitoring system's controller is made up of an adaptor, a saturation of peripheral oxygen (SPO2 ) sensor (a blood oxygen meter), a temperature sensor, a heart rate sensor, a WiFi module, and a liquid crystal display (LCD).</span>

Augmented Reality Control based Energy Management System for Residence

Introduction: The roots of virtual reality (VR) go back to the work of Ivan Sutherland in the 1960s. At the time, virtual reality showed promise in universities and labs, peaking in the early 1990s at a stable level. The latest form of IoT is merging with the Internet of Everything (IOE). Using this technology, users can access any device, device, and machine in the environment through any middleware application or system. In recent years, smart speakers have become a common commercial method for controlling household appliances, solving some of the problems of remote control. For example, speech recognition is difficult in noisy environments and is minimized using noise cancellation. It also takes a long time to determine the current state of the device as the user need to request or issue a command to the smart speaker and need to wait for a response. The evaluation of prototype system found, using a device with gestures and interacting with a virtual 3D device instead of a real device was acceptable in terms of usability. In this article, the Unity and Vuforia are used to create an AR-based IoT virtual switch that can control any device connected to a LAN controller via HTTP requests. Methods: The system is made experimental with the Augmented Reality (AR), the virtual communication system is designed instead of physical mode. It provide the real time based image for active control of Energy management. It consist of virtual switches, it supports the AR based power control system. Results: The proposed system are made evaluated and tested under various load conditions. The virtual image consist of virtual switches to make real time control of power equipment. The proposed system achieve the latency of about 0.06 sec. Conclusion: The advancement in day today activities required with AR based energy management system with proper control measures. The designed system is supportive for such mechanism. The further system is proposed to make more energy saving concept for enhancement in proposed work.

IoT-enabled smart cities towards green energy systems: a review

<span>Integration of internet of things (IoT) in smart city management to improve various functions and living standards due to increasing population growth has dramatically evolved ubiquitous and essential services at various stages of urbanization. Hence, smart cities need to be eco-friendly by improving various sectors like education, health, and transport to provide an urban and sustainable quality of life through solving complicated green energy networks, controlling toxic pollution risks, and public safety. Linking optimized green energy systems with the production and automation of advanced applications is crucial to compose implementation strategies for smart city services. This paper aims to conduct a review on eco-friendly plans and infrastructure of IoT-enabled smart cities by exploiting green energy approaches. This study performs critical observations, ideas, and analyses of recent research in the context of our mentioned research theme. This paper points out the technical and functional challenges of an optimal performance-based green IoT-enabled smart city infrastructure. In this sense, this study organizes observations of relevant initiatives, technologies, and experiences in IoT-enabled smart cities, as well as how to embed it with green energy. Moreover, it can provide significant directions to intellectuals and authorities to develop IoT-enabled smart city applications for prospective research.</span>

Design of IoT-based monitoring system for temperature and dissolved oxygen levels in catfish aquaculture pond water

<span>One of the fish in Indonesian waters that has been successfully bred and cultivated is the catfish (Pangasius sp.). In catfish farming, there are several water quality factors that need to be considered, such as temperature and dissolved oxygen levels. Based on the existing description, it is very important to pay attention to the water quality of aquaculture ponds, especially temperature and dissolved oxygen levels for fish survival. This study aims to create an internet of things (IoT) based monitoring system for temperature and dissolved oxygen levels in catfish aquaculture pond water based on NodeMCU ESP8266. Monitoring system is using SEN0237 gravity analog dissolved oxygen sensor, DS18B20 sensor module, NodeMCU ESP8266, 20×4-character liquid-crystal display (LCD), micro secure digital (SD) card module, internet modem. Data from measurements of temperature and dissolved oxygen levels are stored online in the Adafruit.io database in the .csv format and on a micro secure digital (SD) card in the device in the .txt format. The lowest value of dissolved oxygen levels and temperature are 3.4 mg/L or 3.4 ppm and a temperature of 27.9 °C, respectively. Meanwhile, the highest value of dissolved oxygen levels and temperature are 4.6 mg/L or 4.6 ppm and temperature of 30.9 °C, respectively.</span>

Improving the performance of IoT devices that use Wi-Fi

<p>Providing quality service to users of the internet of things (IoT) entails addressing two crucial aspects: one related to security and the other concerning the limited resources of IoT devices. We will face a challenge while using timesensitive applications within a network that utilizes a high-performance Wi-Fi technology with exceeding energy consumption. Due to this research challenge, we propose a new algorithm, IoT-quality of service (QoS), designed to achieve a true balance between enhancing the security aspects of IoT devices and improving network-hardware performance. Thus, the algorithm efficiently manages the limited energy resources by monitoring energy levels, communication quality, and queuing delay at access points. This is accomplished by utilizing a streamlined identity management system capable of achieving authentication and access authorization with reduced loading for IoT devices. The research hypothesis underwent validation through a comparative analysis of its performance against the conventional model of a Wi-Fi-based IoT device. This evaluation was conducted utilizing the NS3 simulator and was based on a predetermined set of parameters influencing the examined performance metrics, including power consumption, throughput, delay, and response time. The findings exposed the superiority of the proposed algorithm.</p>

TIG-CL: Teacher-Guided Individual- and Group-Aware Contrastive Learning for Unsupervised Person Reidentification in Internet of Things

TIG-CL: Teacher-Guided Individual- and Group-Aware Contrastive Learning for Unsupervised Person Reidentification in Internet of Things

HSESR: Hierarchical Software Execution State Representation for Ultralow-Latency Threat Alerting Over Internet of Things

HSESR: Hierarchical Software Execution State Representation for Ultralow-Latency Threat Alerting Over Internet of Things

Predicción de lectura en instrumento patrón para una empresa de metrología a través de un dashboard empleando algoritmos de regresión

Nowadays, metrology has employed advanced techniques such as artificial intelligence, the internet of things and data analysis, all with the aim of accurately and precisely measuring different physical magnitudes. All this is known today as metrology 4.0, which has as its main objective to ensure the reliability and accuracy of measurements in fields such as industry. In this context, a web application is proposed for a company dedicated to the calibration of instruments, in which regression algorithms are applied for prediction in their calibration instruments. This will not only provide better quality and more reliable results to customers, but will also optimize the company's processes and improve the efficiency of decision making related to instrument calibration. It is expected that, through the use of regression algorithms, the company will predict when the standard instrument will give bad readings. This will make it possible to send the instrument for calibration in advance, avoiding errors and guaranteeing measurement accuracy. Although concrete results are not yet available, a significant improvement in the accuracy and reliability of predictions is anticipated, as well as in the company's operational efficiency.

Internet of Things in Smart Cities: Comprehensive Review, Open Issues, and Challenges

Internet of Things in Smart Cities: Comprehensive Review, Open Issues, and Challenges

Vibrational coupling-based multilayer flexible triboelectric nanogenerator for wide-amplitude omnidirectional wave energy harvesting

Wave energy harvesters are viewed as potential foundations for self-powered wireless sensor devices. In this study, a multilayer flexible triboelectric nanogenerator (MFLU-TENG) is proposed and evaluated, which combines the stretching properties of springs and the deformation properties of flexible materials to enable wide-wavelength energy harvesting. It captures wave energy even at very low amplitudes, requiring a minimum wave amplitude of 5 cm for energy recovery. The study explores how geometric parameters of the MFLU-TENG affect vibration behavior and output performance. Optimal performance is achieved when varying the thickness of the PETG cushioning layer, resulting in a peak power density of up to 7.7 mW/m2. Three MFLU-TENGs connected in parallel successfully power 15 LEDs. And four parallel groups of MFLU-TENGs charged a 100µF capacitor to 5.3 V in 198 s and successfully powered the temperature and humidity sensor. And the MFLU-TENGs charge the 100uf capacitor to 4.0v and power the clock in 153 s. These findings underscore the MFLU-TENG’s potential as a cost-effective and efficient wave energy harvesting device, using renewable wave energy to power sensors or microelectronic devices in the Internet of Things.

Internet of things and long range-based bridge slope early detection systems

<p><span>This research proposes an internet of things and long range (LoRa)-based bridge slope status monitoring and warning system that is wireless, low-cost, and user-friendly, with continuous data sent. Bridge inspection officers can easily obtain bridge slope data via a web browser on a cell phone. The design uses Arduino integrated development environment software and an ITGMPU accelerometer sensors, TTGO ESP32, cellphones, successfully identified tilt angle variations from 0.11° to 15.2° were the research's outputs, and and they were continuously transmitted to the bridge inspection officer's mobile phone. Measurements of throughput, quality of service (QoS), and latency characteristics have been made to assess the internet network's performance. The network system performance statistics show an average measured network delay of 1.2 seconds, a throughput of 85 bps, and a QoS of 0%. Consequently, the system performs well and the internet network performance falls into the very good range.</span></p>

Guest Editorial: IoT Security and Provisioning in Cyber-Enabled Niche Critical Infrastructure

Guest Editorial: IoT Security and Provisioning in Cyber-Enabled Niche Critical Infrastructure