Infected female mosquitoes can be brutal to detect when they transmit diseases such as Dengue, Malaria, and other harmful diseases. One of the primary reasons that can cause these unpredictable infections is those female mosquitoes can become infected with illnesses when they come in contact with vertebrate blood. The rate of getting infected depends on the breeding site of the mosquitoes. The infected mosquitoes can lay hundreds of eggs in the breeding locations. The mosquitoes that are born from the infected mosquitoes can also be infected, allowing them to continue transmitting diseases to other victims. Hence, gathering and monitoring climate data and environmental conditions for mosquito research can be valuable in preventing mosquitoes from spreading diseases. Users like mosquito researchers may need weather stations in various locations to obtain microclimate data. They may need an inexpensive and effective monitoring system for multiple sites. Researchers can achieve it by placing numerous remote data collection stations in various areas. Also, they can send the microclimate data (temperature, humidity, pressure, etc.) collected by the data collection stations to a cluster such as a customized Raspberry Pi-based cluster. This study aims to develop multiple clusters based on Raspberry Pi 3 B+ and Pi 4 B devices using Micro-Kubernetes as the distribution system. Each cluster will store data in a database, and the data will be able to be accessed in various ways, such as through an Android application and a website. Android and web applications were developed and presented in this thesis for accessing the microclimate data. Also, microclimate data were compared between the specific locations and nearby weather stations. Lastly, the resource usage of the two proposed clusters was compared under a similar scenario. The results of this study show that the measurement data of the specific locations are more accurate than those from nearby weather stations. Also, cluster 2, with four Raspberry Pi 4 B boards, uses fewer resources in CPU and memory than cluster 1, with one Raspberry Pi 4 B and three Raspberry Pi 3 B+ boards.
