| dc.description.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 | en_US |
