Internet Of Things Platform Research Articles

Internet of Things in Disaster Management

In recent years, natural failures including floods, landslides, earthquakes, volcanic eruptions, and wildfires have come to be increasingly Ubiquitous, These events, frequently worsened with of human activities like deforestation, immoderate chemical use, heavy use of gasoline production, and international warming, those problems result in sizable loss of existence and belongings damage global, fortunately, technological improvements have furnished valuable tools for know-how and mitigating the risks associated with these screws-ups. The Internet of Things (IoT), with its various competencies, has appeared as a promising solution for catastrophe management. Wi-fi sensor networks (WSNs) and IoT platforms can establish centralized facts systems to reveal environmental situations. Through sensor information evaluation, we will discover capability risks in real time. When unusual readings are detected, the device can cause signals, ship SMS messages, and make calls to exact contacts. These signals offer statistics about the threat factor, its location, and suggested movements. Using uploading actual-time statistics to cloud servers and issuing alerts through various communication channels, this machine ambitions to reinforce communities with critical data, thereby enhancing their disaster response and preparedness

Application of Internet of Things (IoT) on Monitoring EC and pH Values for Fertigation System

This paper introduces a novel Internet of Things (IoT) based fertigation control system designed to enhance traditional agricultural practices in Malaysia, particularly in rural areas. The system integrates a fertigation approach, combining fertilization and irrigation, with smart devices and a NodeMCU ESP32 microcontroller to automate the tracking of data from liquid fertilizer mixtures. By utilizing EC and pH sensors, the system accurately monitors the nutrient concentration and pH levels of the fertilizer mix, with data displayed on an IoT platform for real-time observation and control. This approach mitigates common issues such as overfertilization and inefficient nutrient use, offering significant improvements over manual data collection methods. The system's performance was validated, showing successful monitoring and control of the fertilization process, leading to enhanced plant health and crop quality. Key findings indicate that the system's integration with IoT technology provides a more efficient, automated solution for managing fertilizer applications, reducing labor and time while ensuring optimal plant nutrient levels. The study also highlights the potential for future development, including the integration of additional sensors and advanced data analytics to optimize fertigation practices further. The implementation of this IoT-based system represents a significant advancement towards sustainable agriculture, offering practical benefits for Malaysian farmers and setting a foundation for further innovation in fertigation technology.

Rancang Bangun Pakan Ikan Otomatis dan Kontroling Suhu Serta Monitoring pH Air Berbasis IoT

This research proposes the design of an automatic fish feeding system equipped withtemperature control and water pH monitoring based on the Internet of Things (IoT). Thissystem aims to increase efficiency in aquarium or fish pond management by integratingvarious control and monitoring functions in real-time. In this system, the automatic fishfeeder uses a programmable dispenser mechanism to provide feed in predeterminedamounts and times. Temperature control is carried out using temperature sensors andheating or cooling actuators, to maintain water temperature conditions within the optimalrange. In addition, water pH monitoring is carried out through a pH sensor connected tothe system to ensure the water quality remains ideal for fish health. Data from these sensorsis sent in real-time to the IoT platform, enabling remote monitoring and control via webbasedor mobile applications. The results of implementing this system show increasedefficiency in feeding, increasing operational efficiency, reducing human error, andproviding quality fish, as well as maintaining more stable and optimal environmentalconditions. The implementation of this system shows great potential in modern aquacultureapplications by utilizing IoT technology for better and responsive management.

An automated intrusion detection system in IoT system using Attention based Deep Bidirectional Sparse Auto Encoder model

Nowadays, the Internet of Things (IoT) is a smart network connected to the Internet for transmitting gathered data with verified protocols. Attackers frequently use communication protocol defects as the basis for their attacks. Better protection measures are required since attacks affect the reputations of service providers. Both machine learning (ML) and deep learning (DL) methods have been developed in a number of research works to detect network intrusions. However, the system's security is limited by the rising number of new threats. Critical problems in IoT platforms, cyber-physical systems, wireless networks, and fog computing are caused by such attacks. The development of various cyber-security attacks reinforces the need for a strong intrusion detection system (IDS) in the IoT platform. The proposed study introduced a robust deep-feature learning mechanism for automatically detecting network intruders in the IoT platform. Initially, input data are gathered from the given dataset. Pre-processing helps reduce any noise in the data and improves the data quality using cleaning, outlier removal, and min-max normalization. The proposed Attention-based Deep Bidirectional Sparse Auto Encoder (AD-BiSA) model is the most important feature retrieved using the attention-based deep Bi-LSTM model. The different IoT network threats are categorized using a sparse Autoencoder approach. The chaotic Seagull Optimization (CSGO) algorithm decreases the loss and enhances the weight in the proposed DL technique. The UNSW NB15_IDS and NSL-KDD datasets achieve accuracy rates of 99.71% and 98.97%, respectively, for the proposed technique. The proposed method achieves better performance than existing approaches.

Design and development of immersive 3D virtual simulation experiment teaching platform for internet of things

Design and development of immersive 3D virtual simulation experiment teaching platform for internet of things

A Survey on IoT Programming Platforms: A Business-Domain Experts Perspective

The vast growth and digitalization potential offered by the Internet of Things (IoT) is hindered by substantial barriers in accessibility, interoperability, and complexity, mainly affecting small organizations and non-technical entities. This survey paper provides a detailed overview of the landscape of IoT programming platforms, focusing specifically on the development support they offer for varying end-user profiles, ranging from developers with IoT expertise to business experts willing to take advantage of IoT solutions to automate their organization processes. The survey examines a range of IoT platforms, classified according to their programming approach between general-purpose programming solutions, model-driven programming, mashups and end-user programming. Necessary IoT and programming backgrounds are described to empower non-technical readers with a comprehensive field summary. In addition, the paper compares the features of the most representative platforms and provides decision insights and guidelines to support end-users in selecting appropriate IoT platforms for their use cases. This work contributes to narrowing the knowledge gap between IoT specialists and end users, breaking accessibility barriers and further promoting the integration of IoT technologies in various domains.

RANCANG BANGUN MODEL MONITORING UNDERGROUND TANK SPBU DENGAN MENGGUNAKAN SENSOR ULTRASONIK BERBASIS INTERNET OF THINGS (IOT)

The management of underground tanks at gas stations is a crucial aspect to ensure the availability and safety of fuel. However, conventional methods for monitoring fuel levels in tanks are often ineffective and prone to human error. To address this issue, this research designs and develops a model for an underground tank monitoring system using ultrasonic sensors Internet of Things (IoT) integrated technology. The ultrasonic sensors are used to measure the fuel level in the tank in real-time, and this data is then transmitted to an IoT platform for analysis and monitoring through a user interface that can be accessed remotely. This system is expected to enhance operational efficiency and safety in the management of underground tanks at gas stations and provide early warnings in the event of abnormal conditions, such as leaks or excessively low fuel levels. The implementation results demonstrate that this system can provide accurate and reliable information, enabling better decision-making in fuel stock management

70 - “Digital Twin” Intelligent Management of Large Medical Equipment Based On Internet of Things Platform

70 - “Digital Twin” Intelligent Management of Large Medical Equipment Based On Internet of Things Platform

Optimizing Catfish Farming Through IoT-Driven Water Quality Management

General Background: Catfish play a significant role in the global fishing industry, necessitating effective farming practices to enhance productivity and sustainability. Specific Background: However, challenges such as maintaining optimal water quality in aquaculture ponds, particularly concerning ammonia levels and temperature fluctuations, pose threats to catfish health and production efficiency. Knowledge Gap: Previous research has often focused on single-parameter monitoring, lacking comprehensive systems that integrate real-time data for both ammonia and temperature management. Aims: This study aims to develop an advanced Internet of Things (IoT) system for real-time monitoring and control of ammonia levels and water temperature in catfish ponds, facilitating optimal farming conditions. Results: The implemented system utilizes two sensors to measure ammonia and temperature, integrated with the Blynk IoT platform for remote monitoring. The findings indicate that when water temperature exceeds 26 °C, the water pump activates to regulate temperature, while ammonia levels above 5 NH3 trigger the aerator to enhance oxygen levels and reduce ammonia toxicity. Novelty: This research innovatively combines multiple sensors and modern IoT applications, offering a more robust solution for aquaculture management compared to previous studies. Implications: The monitoring system effectively maintains water quality, enhancing catfish farming sustainability and efficiency, emphasizing the need for technology integration in aquaculture to effectively tackle environmental challenges. Highlights: Real-time Monitoring: Continuous tracking of ammonia and temperature levels. Automated Control: Activates pumps and aerators based on set thresholds. Enhanced Sustainability: Supports healthier catfish and optimizes production efficiency. Keywords: Catfish Farming, IoT, Water Quality Monitoring, Ammonia Control, Temperature Management

Internet of things platforms adoption in agriculture: comparative theoretical models

PurposeThe purpose of this study is to determine the farmers’ behavioural intention (BI) to adopt Internet of things platforms (IoT-P) in the agriculture context by comparing two well-known theories: the united theory of acceptance and use of technology (UTAUT), and the decomposed theory of planned behaviour (DTPB) with the integration of innovation resistance theory (IRT).Design/methodology/approachPurposive sampling was used to get responses from 267 potential farmers to examine their IoT-P adoption intention in Pakistan. The PLS-SEM, PLS model evaluation criterion and PLS model selection criterion were considered to determine the significance of path co-efficient, explanatory power, predictive power and more parsimonious model.FindingsThe findings demonstrate that DTPB is the best model with the extension of functional barriers (FBs) and psychological barriers (PBs). It has more predictive relevance and explanatory power. The results show that farmers’ attitude (ATT), based on the evaluation of three attributes (i.e. perceived usefulness (PU), perceived ease of use (PEOU) and compatibility (COMP)), is the strong predictor of farmers’ BI to adopt IoT-P. In addition, self-efficacy (SEF) and facilitating conditions (FC) peer influence (PI) and superiors’ influence (SPI) are required for adoption of IoT-P devices. Finally, FB and PB significantly and negatively influence the farmers’ BI to adopt IoT-P.Originality/valueThis research is the first to consider the two technology adoption models with the integration of IRT for explaining farmers’ BI in the context of agriculture.

Prototype Smart Baby’s Room Berbasis Iot Di PT Ozami Inti Synergy Untuk Orang Tua Sibuk

This research aims to design and develop a prototype of the Smart Baby's Room based on the Internet of Things (IoT) at PT Ozami Inti Synergy,as a solution for busy parents. This tool is designed to monitor baby's condition and surrounding environment in real-time through a mobile application. Key features include monitoring temperature, humidity, air quality, as well as sound and motion detection of the baby. Data from these sensors is transmitted to an IoT platform, allowing parents to receive notifications and remotely monitor the conditions remotely. This implementation is expected to improve baby's safety and comfort, as well as providing peace of mind for parents who cannot always be at home. parents who cannot always be at home. This prototype was tested to ensure its reliability and effectiveness in a controlled environment.

A solar-powered multi-functional portable charging device (SPMFPCD) with internet-of-things (IoT)-based real-time monitoring—An innovative scheme towards energy access and management

In the absence of portable charging devices, sectors such as transportation, communication, and emergency services deal with various challenges towards electric power needs while compromising on (1) operational efficiency, (2) insufficient portable charging solutions, and (3) limited versatility. This highlights the critical need for reliable and multi-functional power solutions. To provide a portable charging solution across diverse sectors, this paper proposes an innovative development of a solar-powered multi-functional portable charging device (SPMFPCD) with internet- of-thing (IoT)-based monitoring capabilities. The proposed scheme introduces a comprehensive model integrating advanced technologies which include a highly efficient solar panel, charge controller, sensors, and IoT module. The proposed system facilitates versatile charging solutions for a wide range of power requirements with real-time monitoring and data analysis through the IoT platform. Moreover, the proposed work explores the applications of the SPMFPCD in (1) emergency medical scenarios, (2) outdoor adventures, (3) disaster management, and 4) public spaces. Performance evaluation was made by proposing case studies to validate the (1) economic viability, (2) power management, and (3) environmental impact of widespread deployment of SPMPFCD in public spaces. Furthermore, detailed analysis of battery energy storage system (BESS) and photovoltaic (PV) integration for load management, seasonal dynamics, and renewable energy integration (REI) contribute to a comprehensive understanding of the proposed solution.

Intelligent Street Lighting: An IoT-based System for Adaptive Brightness and Fault Management

The research aims to develop an intelligent fault detection and control system for street lights using an STM32 microcontroller and Internet of Things (IoT) technologies. The system uses various sensors and communication methods to manage lighting more effectively and maintain infrastructure better. Light-Dependent Resistors (LDRs), which turn lights on automatically at nightfall and off at dawn, are essential to the system's operation. Moreover, motion is detected by the infrared (IR) sensors on the street, allowing the lighting to save energy by dimming during times of low traffic and brightening when movement is detected. By using control logic and sensor readings, a relay functions as an electronically controlled switch to turn the lights on and off. To process sensor data, the STM32 microcontroller is used in the main functionality. Energy savings are facilitated by the automated on/off switching based on ambient light levels, enabled by the LDR sensor data. In addition, the device includes a GPS module for precise street light location tracking. An IoT platform transmits this location data as well as operational status and real-time sensor readings. Maintenance staff can access regional data on light quality and sensor readings through an LCD display. This extensive data enables maintenance team to focus better, use resources efficiently, and enable remote monitoring and fault identification (e.g., burned-out bulbs). This Internet-of-Things (IoT) system provides an economical and environmentally friendly approach to efficient street light management by integrating automation, remote control, and real-time monitoring.

Digitally powered solution delivery: The use of IoT and AI for transitioning towards a solution business model

Digitalization has played a key role in enabling the delivery of service-based solutions by facilitating customer-oriented needs-finding and personalized problem-solving capabilities. Artificial intelligence (AI) and Internet-of-Things (IoT) are decisive technologies in this process. This study evaluates how the use AI and IoT platforms impact solution delivery business models among servitized and non-servitized businesses. By running regression models on a sample of 213 Spanish businesses for 2023, it was found that the use of IoT platforms, rather than AI platforms, significantly assist companies down the analyzed solution delivery dimensions (i.e., customer embeddedness, operational adaptiveness, offering integratedness, and organizational networkedness). Additionally, results indicate that the combined use of IoT platforms when servitizing leads to greater customer embeddedness, whereas the use of AI platforms among servitized firms significantly contributes to improving operational adaptiveness.

Acceptance of an IoT System for Strawberry Cultivation: A Case Study of Different Users

The Internet of Things (IoT) has been impacting multiple industries worldwide for over a decade. However, less developed countries have yet to make the transition to these technologies. South America is among the regions with the least IoT influence in all sectors, indicating a need for studies to explore IoT acceptance among various users in this region. This study analyzes two different users of a monitoring and irrigation system for strawberry (Fragaria × ananassa) farming. Monitored variables include soil moisture, and ambient temperature and humidity, with irrigation performed via water pumping from a reservoir. The system is based on the M5Core2 development kit for the local station and the IoT platform ThingSpeak for remote access. It features a web user interface consisting of an application developed in HTML using a plugin on ThingSpeak. Thus, the system can be used locally via a touchscreen and remotely through a web browser. Measurements are cross-verified with commercial meters to ensure their reliability, and users are asked to fill out a Technology Acceptance Model (TAM) for IoT to gauge their acceptance level. Additionally, an interview is conducted that explores four critical factors, aimed at understanding their experience and interaction with the system after a period of usage. The findings confirm the validity of the monitored variables and demonstrate a global acceptance rate of slightly over 80%, albeit with varying user acceptance perspectives. Specifically, the technical user exhibits greater acceptance than the crop administrator, evidenced by a mean discrepancy of 1.85 points on the TAM scale.

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.

Prototype smart integrated fire detection based on deep learning YOLO v8 and IoT (internet of things) to improve early fire detection

The high incidence of fires in Indonesia in 2018-2023 is 5,336 fire incidents have caused many deaths and enormous material losses. This system is designed to identify early signs of fire through object detection and sensor technology, which is integrated with the Blynk IoT platform for real-time sensor monitoring and Telegram for instant notifications to users. The waterfall prototype method was designed through observation, system design, program code creation, tool testing, and tool implementation. This research uses Deep Learning YOLOv8 technology and IoT using ESP 32 as a microcontroller. Based on the training datasets, it produces precision=0.95872; recall=0.91; mAP50=0.97; mAP50-95 =0.66. The system uses the integration of a multisensor KY-026 flame sensor, DHT 22 temperature and humidity sensor, and MQ-2 sensors can detect CO, LPG, and smoke gas. All these multisensors can be monitored on Blynk IoT and Telegrambot in real time.

Remote Vibration FFT Analyzer –Using Mobile App IOT and MATLAB Application

A new system to check machine vibrations from far away has been created. It uses sensor and IoT technology and MATLAB to show data. It's better than old ways because it uses new technology to watch things right away. Here's how it works: M5StickC device on the machine. This thing has a 3-axis IMU digital sensor that measures the acceleration. Then, it sends this info to phones using Bluetooth. This makes it a small, flexible IoT platform that collects reliable data. After that, an app sends this stuff to a cloud platform. There, a MATLAB program does something called Fast Fourier Transform (FFT) analysis. This gives you pictures of the signals in remotely showing how they change over time and how often they happen. Methods like remote vibration tests can spot equipment problems such as unbalance, gears and bearing issues on. This real-time data review and evaluation boost productivity and make things more accurate while cutting down on repair costs and time when machines aren't working. It allows factories to use predictive upkeep, which makes equipment more reliable and accurate. This means less downtime and cheaper maintenance overall. Key Words: IoT (Internet of Things), Fast Fourier Transform (FFT), Predictive maintenance

Designing an IoT platform using Wireless Sensor Network

The Internet of Things (IoT) is not only a well-researched topic but also a growing industry. Although the main idea is to connect devices to the internet, there are many ways to do this since IoT systems are application-oriented. This paper presents a wireless sensor network (WSN)based IoT platform designed for wide are aapplications. The platform consists of one or more wireless sensor networks, gateways, web servers, and databases that provide a reliable connection between field sensors and Internet databases. WSN is based on the IEEE 802.15.4e Time Slot Channel Hopping (TSCH) protocol because it has the high performance. Subsequent to the system designed for many subscribers, new synchronization plans and effective transmission functions are also planned to increase network capacity and reduce energy consumption. Therefore, the platform concept can meet the needs of high-end applications and the battery life of low-end applications

Enhancing IoT Routing Security and Efficiency: Towards AI-Enabled RPL Protocol

In the rapidly evolving landscape of the Internet of Things (IoT), the security and efficiency of network routing are paramount. The standard Routing Protocol for Low-Power and Lossy Networks (RPL) faces challenges in the form of various cyber-attacks, impacting its reliability and performance. To address this problem, we propose a Novel Algorithm for Network Traffic Analysis and Response (NANTAR), a novel algorithm that optimizes the security and efficiency of RPL routing using AI-based techniques. Through evaluation against traditional RPL, NANTAR has demonstrated remarkable improvements in key performance metrics: a 20% increase in throughput, latency reductions of 20-30%, and more efficient resource utilization. Notably, NANTAR maintains robust network performance with scaling, evidenced by 15-20% lower false positive and negative rates. The algorithm’s efficacy is further highlighted by its high detection rates in the face of diverse attacks and its power consumption efficiency, which is crucial for IoT devices. NANTAR’s adaptability and seamless integration across various IoT platforms affirm its potential as a solution for securing IoT ecosystems, with results indicating significant improvements in key operational metrics.