ThingSpeak Internet Of Things Research Articles

An Effective and Affordable Internet of Things (IoT) Scale System to Measure Crop Water Use

Scales are widely used in many agricultural applications, ranging from weighing crops at harvest to determine crop yields to regularly weighing animals to determine growth rate. In agricultural research applications, there is a long history of measuring crop water use (evapotranspiration [ET]) using a particular type of scale called weighing lysimeters. Typically, weighing lysimeters require very accurate data logging systems that tend to be expensive. Recent developments in open-source technologies, such as micro-controllers and Internet of Things (IoT) platforms, have created opportunities for developing effective and affordable ways to monitor crop water use and transmit the data to the Internet in near real-time. Therefore, this study aimed to create an affordable Internet of Things (IoT) scale system to measure crop ET. A scale system to monitor crop ET was developed using an Arduino-compatible microcontroller with cell phone communication, electronic load cells, an Inter-Integrated Circuit (I2C) multiplexer, and analog-to-digital converters (ADCs). The system was powered by a LiPo battery, charged by a small (6 W) solar panel. The IoT scale system was programmed to collect data from the load cells at regular time intervals and send the data to the ThingSpeak IoT platform. The system performed successfully during indoor and outdoor experiments conducted in 2023 at the Clemson University Edisto Research and Education Center, Blackville, SC. Calibrations relating the measured output of the scale load cells to changes in mass resulted in excellent linear relationships during the indoor (r2 = 1.0) and outdoor experiments (r2 = 0.9994). The results of the outdoor experiments showed that the IoT scale system could accurately measure changes in lysimeter mass during several months (Feb to Jun) without failure in data collection or transmission. The changes in lysimeter mass measured during that period reflected the same trend as concurrent soil moisture data measured at a nearby weather station. The changes in lysimeter mass measured with the IoT scale system during the outdoor experiment were accurate enough to derive daily and hourly crop ET and even detect what appeared to be dew formation during the morning hours. The IoT scale system can be built using open-source, off-the-shelf electronic components which can be purchased online and easily replaced or substituted. The system can also be developed at a fraction of the cost of data logging, communication, and visualization systems typically used for lysimeter and scale applications.

Internet of things (IoT)-based on real-time and remote boiler fuel monitoring system: a case of Raha Beverages Company Limited, Arusha-Tanzania

RAHA Beverages Company (RABEC) is one of the banana wine production companies that utilizes fuel in steam production in Arusha-Tanzania, where fuel data conditions such as temperature, pressure, discharge, fuel level, and gas leakage with humidity were a challenge to monitor, which provoked boiler malfunction and plant breakdown. Today, RABEC manually uses a dropping stick into the fuel tank to monitor fuel data conditions which is time-consuming and gives inaccurate readings, inefficiency, fuel economy discrepancy, and accidents. This study aimed to design and develop an IoT-based fuel monitoring system. The flow meter, ultrasonic level, thermistor fuel temperature, humidity, and pressure sensors were used to gather fuel information where the GSM module was employed to send fuel data messages to the operator’s phone. An AT mega 328 microcontroller was used to process and analyse the fuel data and send them to the Thing Speak IoT platform using Wi-Fi connectivity. The results showed that when the fuel level was less than the threshold value, an operator was alerted by a refilling message via GSM technology. A pressure of 0.1psi, a fuel temperature of 120 ℃, and 80% of humidity, the system notifies the operator by an alert message to check the injector pressure and if the fuel–air mixture is perfect. In addition, these data were observed on the LCD and ThingSpeak webpage. To conclude, the developed system proved the best performance with a 99.98% of success rate with high accuracy, security, and efficiency rate compared to the current monitoring system.

AICropCAM: Deploying classification, segmentation, detection, and counting deep-learning models for crop monitoring on the edge

Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with >43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations.

Design and Implementation of Internet of Things (IoT) Platform Targeted for Smallholder Farmers: From Nepal Perspective

Nepal, a lower-middle-income country in South Asia, predominantly features smallholder farming communities operating on modest land holdings. These smallholders often adhere to traditional farming methods, relying on familial labor, which has become increasingly inefficient in contemporary agricultural landscapes. To enhance their productivity and efficiency, smallholder farmers require affordable and accessible Internet of Things (IoT)-based systems. However, the prevailing IoT solutions in the market primarily cater to large-scale commercial enterprises, rendering them unsuitable for the specific needs and constraints faced by smallholder farmers. In response to this gap, we have introduced a cost-effective, customizable, scalable, and dependable IoT platform tailored expressly for smallholder farmers. This platform empowers them to visualize, monitor, and control real-time data pertaining to their crops, livestock, and other agricultural assets. To ascertain the efficacy and suitability of our proposed platform, we conducted a comparative analysis with existing counterparts such as Blynk IoT and ThingSpeak IoT, evaluating their respective features and application services against standard requirements. Additionally, we subjected our platform to rigorous server load testing, assessing crucial performance parameters including throughput, response time, user capacity, and data sampling rates. Over an observation period spanning an average of 339 days, our platform successfully processed and stored a substantial volume of data, encompassing 817,633 sensor messages, averaging 2412 messages per day, with a cumulative storage size of 14,238.28 KB. Extrapolating from these results, it is noteworthy that an A0 instance with 20 GB of cloud space can adequately accommodate 200 users at a rate of 100 MB per user, which is adequate for the smallholder needs. Furthermore, the purposed platform was deployed inside a polyhouse to perform off-season grafting of citrus plants. The achieved success rate of 84% closely approached the success rate of 90–95% observed during on-season grafting. These empirical findings, coupled with the extensive data gathered during our research, underscore the reliability and performance of our proposed IoT platform for smallholder farmers.

Real-Time IoT-Based Air Quality Monitoring and Health Hazards Indicator System for Mines Regions: A Case Study of Bulyanhulu Gold Mine

Air quality in mining regions is a significant concern due to the release of pollutants from mining activities, posing health risks to nearby communities. However, limited information on air quality levels often leads to neglect of this issue. Inhaling pollutants like PM2.5/PM10, CO, CO2, SO2, and NO2 can result in chronic diseases such as respiratory issues, asthma, and cancer. To tackle this problem, a study suggests the implementation of a real-time Internet of Things (IoT)-based air quality monitoring and health hazards indicator system for mining regions. The proposed system utilizes a reliable wireless sensing system, incorporating sensors like MQ7, MQ135, MQ136, MiCS4514, PMS7003, and DHT22, along with ESP8266, STM32, ATmega328 microcontroller, LoRa shields, and the ThingSpeak IoT server. It ensures continuous operation with a self-contained design, including a solar charger shield connected to a photovoltaic solar panel and rechargeable battery. The smart sensing device continuously monitors air quality and uploads real-time data to the cloud through a coordinator node. The collected data is processed to calculate the Air Quality Index (AQI), which is analyzed to generate early warnings and indicate potential health hazards. The results are accessible through a web-based dashboard for easy visualization. This system simplifies monitoring and provides accurate pollutant data. It supports environmental stakeholders by aggregating and analyzing air quality data, generating reports, and facilitating public access to air quality information. Additionally, it helps identify health hazards, enabling informed decision-making, policy formulation, and mitigation strategies.

An IOT framework for detecting cardiac arrhythmias in real-time using deep learning resnet model

A cardiac arrhythmia poses a serious health risk to patients and can have serious consequences for their health. A clinical assessment of arrhythmia disorders could save a person's life. The Internet of Things (IoT) will revolutionize the healthcare sector by continuously monitoring cardiac arrhythmia diseases remotely and minimally invasively. We propose a frame-work that will facilitate the development of a practical diagnostic tool for the identification of cardiac arrhythmias in real-time in this work. An Electrocardiogram (ECG) signal is processed using the Pan Tompkins QRS (Quantum Resonance System) detection method in order to extract the dynamic properties of the signal. The inter beat (RR) intervals are derived from an ECG signal in order to determine the characteristics of heart rate variability. The electrocardiogram is primarily used to identify irregular heartbeats (cardiac arrhythmias). Therefore, in our study, we evaluated other factors such as the heartbeat of the individual. As part of our IoT deployment, we are storing and analyzing data collected by the Pulse Sensor on the ThingSpeak IoT platform. The designed circuit's real-time collection of heartbeat and beats per minute values was uploaded to Thingspeak. Over the course of more than a week, we collected a variety of heart data. We propose Multi Channel Residual Network (MCHResNet) a deep-learning based solution that relies on multi-channel convolutions to detect both spatial and frequency features from electrocardiograms to facilitate the classification process. Based on the well-known Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia (MIT-BIH-AR) database, we evaluate the proposed framework against MCH ResNet. Our IoT-based framework has been shown to be effective based on the results reported in this paper.

Design and Evaluation of Wireless Power Monitoring IoT System for AC Appliances

The paper is aimed to develop a wireless alternating current (AC) power monitoring module which features the advantage of cost-effectiveness and sufficient reliability for the proposed AC power monitoring Internet of Things (IoT) system. The novel wireless AC power monitoring module consists of both ZMPT101B voltage sensor and ACS712-20 current sensor; a 16-bit analog-to-digital (ADC) ADS1114 with I2C interface and WeMos D1 Mini Wi-Fi module were integrated for monitoring refrigerator and air conditioner appliances with the ratings of single-phase 110/220 VAC, respectively. First, both analog readings of V/I sensors for AC appliances are converted into data streams in compliance with I2C (inter-integrated circuit) protocol, and are forwarded to a WeMos D1 Mini Wi-Fi module for the corresponding values of instantaneous electric power and energy, power factor (pf), and frequency well programmed in the built-in ESP8266EX IoT-based microcontroller unit (MCU) based on the well-known AC power fundamentals. All of the important AC power parameters are sent to the ThingSpeak IoT platform through Wi-Fi network. The visualization of voltage, current, electric power and energy, pf, and frequency is illustrated in the ThingSpeak IoT platform. Both close agreement and confidence of the proposed AC power monitoring IoT system for both refrigerator and air conditioner are evaluated with two CM3286-1 AC Power Meters. Taking the commercialized CM3286-1 instrument as reference, the values of mean absolute percentage error (MAPE) for above six electrical parameter readings are all less than 2%. The evaluation results illustrate sufficient closeness of agreement and confidence for the proposed wireless AC power monitoring IoT system for in situ monitoring AC appliances with single-phase 110/220 VAC ratings. Furthermore, the cost of the proposed AC power monitoring module is less than 100 USD, which makes the novel module more cost-effective than commercialized AC power meters which generally cost over 1000 USD. The novelties of the work are the following: (1) the introduction of ADS1114 provides I2C interface directly for Wi-Fi module to reduce the capital cost of the proposed wireless AC power monitoring module; (2) the sufficient confidence of the proposed AC power monitoring IoT system has been validated with closeness of agreement as compared to the commercialized CM3286-1 AC Power Meters. These make the assessment action of environmental, social, and governance (EGS) for stakeholders much more feasible with the advantages of cost-effectiveness and sufficient confidence.

Device for remote and realtime monitoring of neonatal vital signs in neonatal intensive care unit using internet of things: proof-of-concept study.

Realtime and remote monitoring of neonatal vital signs is a crucial part of providing appropriate care in neonatal intensive care units (NICU) to reduce mortality and morbidity of newborns. In this study, a new approach, a device for remote and real-time monitoring of neonatal vital signs (DRRMNVS) in the neonatal intensive care unit using the internet of things (IoT), was proposed. The system integrates four vital signs: oxygen saturation, pulse rate, body temperature and respiration rate for continuous monitoring using the Blynk app and ThingSpeak IoT platforms. The Wemos D1 mini, a Wi-Fi microcontroller, was used to acquire the four biological biomarkers from sensors, process them and display the result on an OLED display for point of care monitoring and on the Blynk app and ThingSpeak for remote and continuous monitoring of vital signs. The Bland-Altman test was employed to test the agreement of DRRMNVS measurement with reference standards by taking measurements from ten healthy adults. The prototype of the proposed device was successfully developed and tested. Bias [limits of agreement] were: Oxygen saturation (SpO2): -0.1 [- 1.546 to + 1.346] %; pulse rate: -0.3 [- 2.159 to + 1.559] bpm; respiratory rate: -0.7 [- 0.247 to + 1.647] breaths/min; temperature: 0.21 [+ 0.015˚C to + 0.405˚C] ˚C. The proof-of-concept prototype was developed for $33.19. The developed DRRMNVS device was cheap and had acceptable measurement accuracy of vital signs in a controlled environment. The system has the potential to advance healthcare service delivery for neonates with further development from this proof-of-concept level.

Pengembangan Sistem Penyiraman Otomatis Tanaman Anthurium Berbasis IoT

Ornamental plants are types of plants that are cultivated to provide additional aesthetic value. One of the commercial ornamental plants known in Indonesia is the wave of love or Anthurium plowmanii. This type of Anthurium has a high selling value because of special care, one of which is regular and not excessive water supply. One of the technologies that can help humans in automatically watering anthurium plants is the Internet of Things (IoT). ThingSpeak IoT platform connected with NodeMCU ESP8266 is used as artificial intelligence to control and determine watering time, watering automatically takes place when plants need air. This research was conducted to design an IoT-based automatic anthurium plant watering system and collect data on soil moisture and anthurium temperature from the installed IoT sensors. The method in this research is prototyping which is used to produce certain products, and test the effectiveness of these products. This IoT-based automatic watering system for anthurium plants is calibrated by making comparisons with standard measuring instruments to determine the accuracy of the sensor measurement results. The results of this study are a prototype of an IoT-based automatic watering system for anthurium plants by achieving an average level of sensor accuracy in reading anthurium plant conditions reaching 98.1% for soil moisture sensors while for temperature sensors reaching 98.8%. This research is expected to help farmers and ornamental plants communities to facilitate automatic watering process according soil moisture and temperature.

Real-Time Implementation of Wastewater Monitoring System on the Communal Wastewater Treatment Plant using the IoT Technology

A Wastewater Treatment Plant (WWTP) is a plant used to reduce the pollutants contained in the wastewater. The optimal operation of the WWTP can be achieved when the performance of WWTP is monitored regularly. This paper presents an actual implementation of the wastewater monitoring system on the communal WWTP using the Internet of Things (IoT) technology. The proposed method employs the sensors and electronic modules available in the market and the popular ThingSpeak IoT platform. Four sensors, namely pH, temperature, turbidity, and Dissolved Oxygen (DO), monitor the wastewater’s parameters. The IoT hardware on the field communicates with the ThingSpeak cloud server using a GSM modem. The proposed monitoring system has been implemented on the Tirtarona WWTP, a communal WWTP located in Malang, Indonesia. The preliminary evaluation results show that the proposed method works properly and achieves the goal of the real-time wastewater monitoring system. Further, the solar panel is installed at the top of the sensor module for providing a self-powered and green energy monitoring system.

LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring

This study proposes a smart long-range (LoRa) sensing node to timely collect the air quality information and update it on the cloud. The developed long-range wide area network (LoRaWAN)-based Internet of Things (IoT) air quality monitoring system (AQMS), hereafter called LoRaWAN-IoT-AQMS, was deployed in an outdoor environment to validate its reliability and effectiveness. The system is composed of multiple sensors (NO2, SO2, CO2, CO, PM2.5, temperature, and humidity), Arduino microcontroller, LoRa shield, LoRaWAN gateway, and The Thing Network (TTN) IoT platform. The LoRaWAN-IoT-AQMS is a standalone system powered continuously by a rechargeable battery with a photovoltaic solar panel via a solar charger shield for sustainable operation. Our system simultaneously gathers the considered air quality information by using the smart sensing unit. Then, the system transmits the information through the gateway to the TTN platform, which is integrated with the ThingSpeak IoT server. This action updates the collected data and displays these data on a developed Web-based dashboard and a Graphical User Interface (GUI) that uses the Virtuino mobile application. Thus, the displayed information can be easily accessed by users via their smartphones. The results obtained by the developed LoRaWAN-IoT-AQMS are validated by comparing them with experimental results based on the high-technology Aeroqual air quality monitoring devices. Our system can reliably monitor various air quality indicators and efficiently transmit the information in real time over the Internet.

IoT Based Gas Leakage Detection and Alarming System using Blynk platforms

Gas or liquefied petroleum gas (LPG) is a chemical substance resultant from petroleum and could be dangerous in industrial places or those that deal with this substance. Gas leakage causes many health issues. So, to prevent such catastrophes and in order to maintain a clean air environment, the workspace atmosphere should be frequently monitored and controlled. The proposed monitoring gas leakage detector system is based on Internet of Things (IoT) technology. NodeMCU ESP8266 Wi-Fi is used to be the microcontroller for the whole system. The combustible gas sensor (MQ2) is used in order to detect the presence of methane (CH4) and carbon monoxide gas (CO). MQ2 sensor will detect the concentration of the gas according to the voltage output of the sensor and the ESP8266 will send the data reading from the gas sensor to Blynk IoT platform over an IOS phone; data visualization is done using Thingspeak IoT Platform. Besides, a fan will immediately work upon the leakage occurs along with an alarming buzzer.

SimulateIoT-FIWARE: Domain Specific Language to Design, Code Generation and Execute IoT Simulation Environments on FIWARE

Systems based on the Internet of Things (IoT) are continuously growing in many areas such as smart cities, home environments, buildings, agriculture, industry, etc. This system integrates heterogeneous technologies into a complex architecture of interconnected devices capable of communicating, processing, analysing or storing data. There are several IoT platforms that offer several capabilities for the development of these systems. Some of these platforms are Google Cloud’s IoT Platform, Microsoft Azure IoT suite, ThingSpeak IoT Platform, Thingworx 8 IoT Platform or FIWARE. However, they are complex IoT platforms where each IoT solution has to be developed ad-hoc and implemented by developers by hand. Consequently, developing IoT solutions is a hard, error-prone and tedious task. Thus, increase the abstraction level from which the IoT systems are designed helps to tackle the underlying technology complexity. In this sense, model-driven development approaches can help to both reduce the IoT application time to market and tackle the technological complexity to develop IoT applications. In this paper, we propose a Domain-Specific Language based on SimulateIoT for the design, code generation and simulation of IoT systems which could be deployed on FIWARE infrastructure (an open-source IoT platform). This implies not only designing the IoT system for a high abstraction level and later on code generation, but also designing and deploying an additional simulation layer to simulate the system on the FIWARE infrastructure before final deployment. The FIWARE IoT environment generated includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics, which are deployed as microservices on Docker containers and composed suitability to obtain a service-oriented architecture. Finally, two case studies focused on a smart building and an agricultural IoT environment are presented to show the IoT solutions deployed using FIWARE.

Grey wolf optimization with fuzzy logic for energy‐efficient communication in wireless sensor network‐based Internet of Things scenario

SummaryWireless sensor network is an essential building block for Internet of Things (IoT) due to its usage for collecting and sensing data by the sensor nodes in a nearby environment. Energy efficiency is an important requirement for these networks as the sensors used are short on power and memory. In this paper, an enhanced energy‐efficient clustering protocol is proposed that works on three major phases under network setup: grid formation and grid head selection, clustering, and cluster head (CH) selection. Clustering is achieved using nature‐inspired grey wolf optimization (GWO) algorithm, and CH selection is accomplished using fuzzy inference‐based system. Simulation is performed in MATLAB software, and the protocol is evaluated in terms of network lifetime, the first node dead (FND), half node dead (HND), and last node dead (LND). Additionally, a prototype modeling of IoT using the Thingspeak IoT Platform provided by Mathworks is also integrated with the proposed protocol to demonstrate its usage in IoT applications. The simulation results indicated that the proposed scheme attained better performance in preserving energy and extending network lifetime.

IoT based Smart Helmet for Industrial Workers

Abstract: Safety is very important in every workplace, but very often we hear about accidents in factories industries causing loss of life. The labours and workers working in any factory, industries, construction site or mine is vulnerable to accidents and therefore they should be with safety guards properly. In most of the accidents, number of deaths or severe injuries is maximized because the labours and worker are not wearing safety equipmentor wearing low grade safety equipment. Working environment hazards include radiation leakage, fall due to suffocation, poisoning gas leakage and gas explosion. Hence air quality and hazardous event detection is very important factorin industry. In order to achieve those safety measures, the proposed system provides wireless sensors network for monitoring real time situation of working environment from monitoring station. Keywords: Industries; Helmet; Cloud Computing; ThingSpeak Internet of Things; Sensors; Ubiquitous Sensing

DEVELOPMENT OF A LOW-COST OPEN-SOURCE PLATFORM CONNECTED TO THE INTERNET FOR ACQUISITION OF ENVIRONMENTAL PARAMETERS AND SOIL MOISTURE

The development of the Internet and the technologies associated with it has allowed disseminating and cheapening of communication equipment, prototyping services, electronic sensors, and all types of devices. Agriculture has benefited from these technological advances to boost its productivity and profitability. This study presents the development of a data acquisition and device control platform to obtain, in real-time and remotely, information from the field for decision-making and process automation. All electronic components are low-cost and “open hardware”, and the software is “open-source”. The developed platform was validated during a development cycle of two lettuce varieties (Japanese and crisp), in which the soil water matric potential was monitored at two depths (10 and 25 cm), while solar irradiation, air temperature, and soil temperature were evaluated only to monitor the cycle. The platform automatically and satisfactorily controlled the applied irrigation depths using only the data of matric potential by activating a solenoid valve and made the information from the sensors available on the ThingSpeak Internet of Things (IoT) platform.

IoT Based Real-Time Monitoring of Phytoremediation of Wastewater using the Mathematical Model Implemented on the Embedded Systems

This paper presents the Internet of things (IoT) technology for real-time monitoring of wastewater phytoremediation. Phytoremediation is a technique to remove pollutants from the wastewater using the plants. A conventional method to monitor the phytoremediation performance is by taking the samples of the contaminants from a site and measuring them at the laboratory. This method needs many works for data preparation and analysis. A recent development on the IoT technology may eliminate such tasks by a real-time monitoring system. In the proposed realtime monitoring system, several phytoremediation models are implemented on embedded hardware and connected to the Thingspeak IoT platform. The proposed system aims to provide a real-time monitoring system to better model the phytoremediation by examining the monitoring data time interval and fitting techniques. From the experiments, the proposed monitoring system achieves a data transfer reliability of 81.4% when the period of the data transmission is one minute, which is suitable for one-day interval real-time monitoring system. The proposed monitoring system can build a phytoremediation model using a polynomial fitting with a higher fit than the existing methods using an exponential fitting. Further, the proposed method promises a better solution in terms of the best model, the low cost, and the acceptable accuracy.

Monitoring System for Early Detection of Fire in Wetlands based Internet of Things (IoT) using Fuzzy Methods

In Indonesia, especially the province of South Kalimantan is a province with a high frequency of forest and wetland fires. These fires seem to be a routine annual natural disaster. BNPB (National Disaster Management Agency) and BPBD (Regional Disaster Management Agency) Central Kalimantan recorded that during 2014 to early 2019, the largest area of forest and wetlands fires in Central Kalimantan occurred in 2015 with a total burned area of 583,833.44 ha. The fire early detection monitoring system that will be made based on Arduino Uno uses the MQ-2, DHT-22 sensor, and flame detector sensor and is integrated with the Thingspeak IoT, which functions to receive data sent from the Arduino Uno microcontroller using the ESP8266 module, then Thingspeak will send the data to an Android smartphone in the form of a real-time display and also a pop-up notification when a potential fire is detected. Finally, the fuzzy method is also added so that later the reading data from the MQ-2, DHT-22, and flame detector sensors will be more accurate before being sent to Thingspeak. The level of accuracy obtained from the readings of the three sensors against the tests that have been carried out is 90%.

Petroleum pipeline monitoring using an internet of things (IoT) platform

In this study, we present the use of an internet of things (IoT) analytics platform service to mimic real-time pipeline monitoring and determine the location of damage on a pipeline. Pressure pulses, based on the principle of vibration in pipes are used for pipeline monitoring in this study. The principle of time delay between pulse arrivals at sensor positions is also adopted in this study. An Arduino and a Wi-Fi module were combined, programmed and used to produce a wireless communication device which communicates with the ThingSpeak internet of things (IoT) analytics platform. A total of five channels were created on the platform to collect data from the five sensors that were used in the experimental test rig that made use of wireless communication device. Signal data was collected once every 15 s and all the channels were updated every 2 min. ThingSpeak provided instant visualizations of data posted by the wireless communication device. Online analysis and processing of the data was performed as it came in. A second test rig was built that made use of a data logger for processing of data. The measured velocity of pulse propagation using the data logger and air as transport fluid was 355 m/s. The computed estimates of event location for the 50 measurements taken ranged between 4.243 m and 4.246 m. This had a scatter of just 3 mm against the actual measured event location of 4.23 m. The experimental results obtained showed that the performance of the wireless communication device compared satisfactorily with the data logger and is capable of detecting the location of damage on real pipelines when used for real time monitoring.Using this communication device and an analytics platform, real-time monitoring of pipelines can be carried out from any location in the world on any internet-enabled device.

Apply Matlab in Thingspeak Server to build the system measure and analyze data using IoT Gateway technology

ThingSpeak is an open Internet of Things (IoT) platform with MATLAB® analytics that enables the collection and storage of sensor data in the cloud and development of IoT applications. The ThingSpeak IoT platform provides applications that allow data analysis and visualization in MATLAB. With MATLAB® analysis in ThingSpeak, MATLAB code can be executed to perform preprocessing, visualization, filtering, data analysis, and for object modeling applications. This paper presents researches on Matlab application in Thingspeak Server to build data measurement and analysis system using IoT LoRa Gateway technology. The research contents include suggestions on device configuration for the system, programming the Arduino board and LoRa Shield to collect measurement data from sensor nodes and communicate by LoRa waves to the LoRa Gateway. The LoRa Gateway will send data to Web Server based on Thingspeak's Cloud Service platform using MQTT (Message Queing Telemetry Transport). Thingspeak's Matlab interface will display online and store values from the sensor nodes. The system is integrated and tested on temperature and humidity monitoring model, evaluated for the results with the required accuracy. The research results allow the deployment of IoT Gateway system in practice for online measurement, analysis and data processing applications that require the use of algorithms and code generation in Matlab using Web Server.