Power optimization of IoT devices scanning wireless fingerprint

• Main Author: Tan, Sing Yew
• Other Authors: Chau Yuen
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering
• Online Access: https://hdl.handle.net/10356/181754
• Institution: Nanyang Technological University

The Internet of Things (IoT) though has changed the way devices are interacted with, its use with battery powered devices raises an energy efficiency problem. This project deals with the energy optimization of an ESP32 based self-localizing and data transmitting device that uses Wi-Fi access points and a motion sensor to locate itself and transmit data to a remote sever over an ESP32 microcontroller. The microcontroller has various low power features like deep sleep which can dramatically lower power usage. The introduction of techniques like deep sleep timers, optimization of Wi-Fi and LTE communication and optimization of dynamic frequency scaling are meant to conserve battery life. The main purpose of the device is to create a device that works to minimize energy usage and deliver reasonable performance without having to sacrifice on functionality. When the device is not in use, the self-localizing function is put under deep sleep mode and is programmed to periodically wake to search for Wi-Fi access points, take readings from the motion sensor and transmit the data. Compared to the baseline, the self-localizing device is estimated to operate with over 60% lower power thereof reducing the operating expenses. By engaging in hardware analysis and performing software changes, this device still showcases operational capabilities whilst saving significant energy as evidenced by this research. The data suggests that the approaches used will in the future enhance the battery longevity of the device, thereby making it highly ideal for prolonged IoT applications in remote areas or places with limited power supply.