The Impact of IoT and Sensor Integration on Real-Time Weather Monitoring Systems: A Systematic Review

Abstract

The observation of weather and its impact on human life is undeniable. Weather observations are essential for various sectors, including agriculture, transportation, aviation, and disaster management. Therefore, the collection of data on weather variations is crucial. However, the transient nature of weather variations makes it difficult to collect accurate and real-time data. To overcome this challenge, researchers have developed smart real-time weather monitoring systems integrated with mobile applications. These systems utilize various frameworks, technologies, and techniques, including the Internet of Things (IoT), Arduino, machine learning algorithms, deep learning algorithms, and sensors that measure temperature, humidity, wind speed, and wind direction. These systems provide real-time data on weather conditions, enabling individuals to make informed decisions. The primary objective of this review paper is to critically examine and elaborate on multiple research studies that focus on integrating smart real-time weather monitoring systems with mobile applications. The aim is to understand the techniques, methodologies, and technologies employed in designing and developing such systems. One of the key findings of this review is that integrating smart real-time weather monitoring systems with mobile applications has significant potential to improve weather forecasting and monitoring. For instance, machine learning algorithms can analyze sensor data to identify patterns and accurately predict future weather conditions. Additionally, integrating cloud computing technology in these systems allows for secure data storage, making it accessible to validated individuals from anywhere. In conclusion, integrating smart real-time weather monitoring systems with mobile applications has enormous potential in improving weather forecasting and monitoring. Various frameworks, technologies, and techniques have enabled researchers to develop sophisticated systems that collect accurate and real-time data on weather conditions. These systems significantly affect various sectors, including agriculture, transportation, aviation, and disaster management.

IoT and Machine Learning in Smart Kitchen Monitoring for Enhanced Worker Health

“…The architecture's foundation is structured around three principal segments: the IoT module (comprising Arduino UNO and ESP8266), an array of sensors (LM35 for temperature, DHT11 for humidity, TGS2601 and MQ2 for air quality monitoring), and actuators (including a FAN and an AC unit). At the heart of the operation, the Arduino functions as the main control unit, interfacing with the ESP8266 for seamless wireless communication, thereby facilitating the relay of sensor data to the Thingspeak database [22]. The data collated in the Thingspeak database is then formatted into a CSV file, prepped for preprocessing and data labeling, which will delineate the datasets for training and testing [23].…”