Exploring Innovative Strategies for Energy Efficiency and Energy Harvesting in Mobile Sensor Platforms for Urban Monitoring

This thesis presents a comprehensive approach to enhancing the energy efficiency and self-sufficiency of the Urbanist mobile sensor platform, an Internet of Things (IoT) device designed to monitor emissions and movement data of vehicles in urban areas. Green IoT principles guide this work, aiming to reduce the power consumption of connected devices through energy efficient design and renewable energy harvesting. Given the Urbanist module's sustainability mission, this research explores and implements a range of strategies to align its operation with environmental goals. To minimize energy consumption, the study introduces several techniques, including the selection of low-power microcontrollers, sleep modes for power conservation, adaptive sampling, and passive wakeup mechanisms that respond to environmental triggers, thus reducing the need for constant active monitoring. Motion detection algorithms differentiate between actual movement and false positives, ensuring that the device only activates when necessary. Additionally, this thesis explores parallel processing on dual-core microcontrollers, which enables concurrent data acquisition and transmission, improving data handling efficiency. To further optimize power use, efficient data transmission techniques were developed. Methods such as data frame stacking and periodic batching were introduced to reduce communication frequency, significantly lowering transmission power.
In addition to energy efficiency, this research emphasizes renewable energy harvesting as a pathway toward greater device self-sufficiency. This thesis explores solar and wind energy harvesting across diverse conditions, intending to demonstrate the capacity of renewable energy to supplement the device’s power needs. Field trials and controlled lab testing validate these strategies, showing that they collectively enhance energy efficiency and support the goal of sustainable, autonomous operation. Overall, this work contributes to the advancement of sustainable IoT, providing practical approaches to energy efficient data acquisition and transmission for mobile monitoring platforms.