Spatial and Temporal Granularity in Environmental Data Analysis for IoT Applications - Azevedo, Leonildo José de Melo de (2025)
The Internet of Things (IoT) comprises \"things\" such as tiny sensors and actuators capable of interacting with the environment. The integration of these devices with sensor networks and Internet access enables communication between the physical world and cyberspace, fostering solutions to many real-world problems. However, most existing applications focus on solving specific issues using private sensor networks, limiting the full potential of the IoT. Additionally, these applications often overlook the quality of service provided by the sensor networks and their constituent sensors, leading to the collection of inaccurate or irrelevant data that can significantly harm organizations. This doctoral thesis presents a metaheuristic-based solution for precise analysis of data quality and infrastructure in IoT environments, specifically focusing on environmental sensing. Using the U.S. Environmental Protection Agency (EPA) database as a case study, we demonstrate how spatial and temporal data granularity analysis can aid in optimizing the deployment and reordering of environmental sensors. This type of analysis helps identify blind spots and unmapped areas, suggesting changes in sensor locations to improve coverage and data quality. By clarifying the relationship between environmental sensing and IoT, this work contributes to the discussion on enhancing sensor networks effectiveness, ensuring more reliable environmental monitoring within the IoT framework.