Environmental Condition Measurement System Using a Mini Weather Station

Abstract

This paper outlines the development and execution of an Internet of Things (IoT)-enabled weather station with the goal of improving environmental surveillance and analysis. The system incorporates many sensors to quantify essential meteorological variables, such as temperature, humidity, air pressure, wind speed, and precipitation amounts. The gathered data is sent over wireless means to a central server, where it is stored and analyzed for immediate monitoring and historical examination. The architecture of the weather station is constructed using a modular design, which enables effortless customization and scalability. We utilize widely-used microcontroller platforms such as Arduino and various other microcontrollers to establish connections with sensors and oversee the transmission of data. To establish connectivity, we investigate different Internet of Things (IoT) communication protocols, such as MQTT and HTTP. This allows for versatility in transmitting data over Wi-Fi or cellular networks. One notable characteristic of the system is its ability to display data in real-time and be accessed remotely. Through the utilization of cloud-based storage and an intuitive web interface, individuals are able to retrieve meteorological data from any geographical region, enabling both personal and collective environmental study. In addition, the system is equipped with alarm mechanisms that inform users about severe weather conditions, hence enhancing safety and readiness. The efficacy of IoT in environmental monitoring is demonstrated by our deployment. We conduct field testing to verify the system's precision, dependability, and resilience under different weather situations. The collected data yields valuable insights into regional weather patterns, presenting possible uses in the fields of agriculture, disaster response, and urban development. This Internet of Things (IoT)-enabled weather station offers an affordable and adaptable alternative for thorough weather monitoring. Our future efforts will concentrate on incorporating cutting-edge data analytics, machine learning, and edge computing to augment the system's capabilities. This will enable predictive analysis and provide more advanced weather-related insights