Full Text Article

IoT Weather Station

Received Date: May 06, 2022 Accepted Date: June 11, 2022 Published Date: June 13, 2022

doi: 10.17303/jcssd.2022.2.203

Citation: Bavesh Reddy R, Prabhu Kumar K, Naga Sai Anil K, Bavesh Reddy, Ganeshaperumal D, et al. (2022) Iot Weather Station. J Comput Sci Software Dev 2: 1-8

Today, intelligent technologies such as IoT and Machine Learning are being introduced in a big way. For IoT implementation, there are many IoT hardware platforms available. An example is the ESP8266 chip. This work develops an actual Weather forecasting system that can be utilized for a range of purposes, such as homes, companies, farms, venues, and so on. A temperature sensor (DHT11), a BMP180 sensor, and a rain sensor module are used in the system. Using Node MCU the detected data from the sensors is updated in the thing speak server. A model named logistic regression is used in ML (machine learning). For monitoring actual values, data is also shown on a custom webpage. This model is built using real time application that has been pre-recorded. The data is extracted from openweathermap.org.

Keywords: Weather Station, Humidity

A DHT11 humidity and temperature sensor, as well as a BMP180 sensor and a rain sensor module, are used in the system. Using Node MCU as well as the ESP8266-01 module, the detected data from the sensors is posted to ting speak server. As of late, new approaches to forecasting atmospheric conditions have emerged with the aim of improving accuracy. Data mining and machine learning applications are providing significant contributions of their own. There are many machine learning implementations, the most prominent of which are categorization and predictions. Predicting sequences from various types of unsupervised or supervised learning problems or instances is not the same as predicting sequences from other types of learning issues or situations. Despite the fact that there are different sorts of challenges depending on input and output sequences, most prediction problems involving sequence data are classed as sequence prediction problems. We'll need to build a time series analytical framework 2 to do sequence prediction. It is employed in the implementation of real-time prediction. Time series prediction, as the name implies, is based on a given number of data points accumulated at specific intervals. These are examined in order to identify the long-term trend in order to make a forecast or perform another calculation. System monitors the weather and updates the information on the web page. The data is sent to the web page in order to keep the meteorological conditions of the area up to date. Anywhere around the world, a specific location can be identified. Temperature and Humidity sensors are included in the system

Existing Systems

Weather stations are often used to detect weather and climatic changes using various sensors such as thermometers, barometers, wind vanes, rain gauges, and other devices. In existing weather monitoring systems. The majority of these devices use simple analogue technologies, which would then be directly recorded and stored in a database. The weather forecast is then broadcast on news programs and radio broadcasting using this information.

Existing weather monitoring systems typically consist of large machinery in several mechanical parts that require ongoing maintenance and must be physically inspected and updated on a regular basis. Power needs are one of the major constraints these devices are often positioned far from the primary power supply. The information received from the devices should be transferred to the computer or PC via a link from the logger.

Proposed System

The suggested system is a Weather monitoring technology that makes real-time information easily accessible all over a broad variety of devices by leveraging IoT. Temperature, humidity, pressure, and rain are among the weather and climate variations monitored by the system. Unlike traditional weather monitoring instruments, our recommended IoT weather is small and compact, making it easy to install on terraces. We can easily take it to a faraway location for installation because it is highly portable. Our systems (sensors and boards). Our system requires less power than the other systems which are already available in the market and also affordable which gives us more value to the design in terms of cost and quality.

System Design

DHT11, BMP180, Rain sensor module, these sensors are connected to the Node MCU. Program these sensors and connect to the thingspeak server. Hence, we can monitor the weather condition in thingspeak server from time to time.

Node MCU helps to collect information sent by the sensors. Then, pass it to the database. This information updates date to date. Collected information helps to predict the future weather conditions using pandas using linear regression algorithm. Later sends this information to the webpage to monitor.

Sensor Organization

Node MCU

As shown in Fig. 2, Node MCU is an upgraded form of Arduino with an embedded Wi-Fi chip. It is affordable than other modules that can work continuously.

Node MCU comes under the low cost microcontroller board with a Wi-Fi module. ESP 8266 powers the module. The Lua scripting language is used on the board. We can modify and develop the code in Arduino IDE.

DHT11

As illustrated in Fig. 3, it is a module for sensing humidity and temperature It senses the humidity and climate of the region air using a piezoelectric humidity sensor and a thermistor.

The DHT11 is a simple humidity and temperature sensor. It just uses a capacitive humidity sensor and thermistor to examine the ambient air, and it outputs a digital signal pin that can also read relative humidity between 20 and 80 percent with 5 percent accuracy.

A BMP180 is a pressure sensor that measures the pressure in the environment. The atmospheric and biometric are estimated using the BMP180 sensor. The operating mechanism of an air pressure sensor is relatively basic; it is based on the weight of air. Because the air we breathe has a specific weight and a specific pressure

D. Rain Sensor Module

The rain sensor module is an excellent tool for detecting rain. A raindrop sensor can be used as a switch or to track rainfall intensity when it falls through the wet board. Then it will turn on when it is attached to the induction board does not have a raindrop, and DO output is high. The information gathered will be sent to the Thingspeak server.

Software Used

The Arduino IDE is virtualization technology for writing, compiling, and transferring code to Arduino. Thingspeak is a virtualized Internet of Things platform that allows users to collect and store sensor data. An unencrypted document with file extensions.html is an HTML web page. It usually includes multimedia elements as well.

Weather Prediction

In openweathermap.org it is mandatory to create an account and produce a key for the account. This key helps in real time extraction of data. We collect temperature, humidity, rain, and pressure using (openweathermap.org) for real time analysis. Train a model using machine learning techniques and grab information from the application using a key that is generated in the openweathermap.org. Predict the feature weather forecasting. We use the Python programming language for extraction of data from time to time and the predicted output that will be sent back to the website into structured data. In the Web page we are going to take prediction data and make a different framework that will be correlated to precisely the efficiency of the weather that will be monitored.

The Thingspeak server is used as a communication server for our sensors. First, we need to create an account

In Thingspeak server API Keys are generated for separate accounts the key is written in Arduino ide It is used to write the data in server. And channel ID is also separate for each channel.

Open weather web site is used to get data for real time to train data for our forecast. We need to create an account on the website.

separate API keys are created for users which are used to get data

The sensors are connected to Node MCU and programmed using Arduino ide successfully and information was sent to thingspeak server. And We collected temperature, humidity, rain, and pressure using (openweathermap.org) for real time analysis. Train a model using machine learning techniques and grab information from the application using a key that is generated in the (openweathermap.org). predict the future weather forecasting and displayed in website

The sensors are connected to Node MCU and programmed using Arduino ide successfully. Generates the graphical information.

Information was sent to the thingspeak server.

predicted the future weather and displayed in website successfully

Data can be collected from Thingspeak server

First and foremost, I wish to thank the Almighty God for his grace and benediction to complete this Project work successfully. I would like to convey my special thanks from the bottom of my heart to my dear Parents and affectionate Family members for their honest support for the completion of this Project work. I express deep sense of gratitude to “Kalvivallal” Thiru. T. Kalasalingam B.com., Founder Chairman, “Ilayavallal” Dr.K.Sridharan Ph.D., Chancellor, Dr.S.Shasi Anand, Ph.D., Vice President (Academic), Mr.S.Arjun Kalasalingam M.S., Vice President (Administration) , Dr.R.Nagaraj, ViceChancellor, Dr.V.Vasudevan Ph.D., Registrar , Dr.P.Deepalakshmi M.E., Ph.D., Dean (School of Computing) . And also, a special thanks to Dr. A. Francis Saviour Devaraj. Head, Department of CSE, Kalasalingam Academy of Research and Education for granting the permission and providing necessary facilities to carry out Project work. I would like to express my special appreciation and profound thanks to my enthusiastic Project Supervisor Dr. D. Ganeshaperumal, Associate Professor/CSE of Kalasalingam Academy of Research and Education [KARE] for his inspiring guidance, constant encouragement with my work during all stages. I am extremely glad that I had a chance to do my Project under my Guide, who truly practices and appreciates deep thinking. I will be forever indebted to my Guide for all the time he has spent with me in discussions. And during the He gave me the emotional support and flexibility I needed to keep going through the most difficult periods of writing my report. Apart from my Project guide, I’d like to thank the rest of the Class committee members, as well as all academics and non-teaching staff, for their encouraging comments. Last but not least, I would want to convey my thanks. to all of my professors, well-wishers, and friends from high school and college for their unwavering support.

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