| dc.description.abstract | The increasing demand for energy-efficient and automated systems has prompted the development of smart power management techniques. For this project, we therefore propose a “Smart Power
Management System for an IoT device and the case study selected is based on Raspberry
Pi”.
The power management system works in such a way that it will enable the raspberry pi to be on
for 30 minutes and the go off for the next 30 minutes. This happens for every one hour between a
certain time range for example between 6AM and 6PM which effectively reduces the power
consumption of the raspberry pi during its idle times while ensuring availability of power during
the active times. However the time for active and inactive states can be modified by preference.
The major components used include the Arduino board, Real Time Clock (RTC), relay, ESP 8266
pin-out, buck converter,DHT11 sensor,coulomb counter and battery.
1.The Arduino board acts as the intermediary between the different components and
raspberry pi for effective control and communication.
2.The RTC provides the real time for accurate timekeeping and scheduling of the raspberry
pi.
3.The relay is for controlling power supply to the raspberry pi thereby enabling switching
between the ON and OFF states.
4.ESP 8266which is a Wi-Fi module is for cloud data transfer to the Blynk monitoring
Platform which provides realtime environmental data, for example the sensor readings and
State of Charge of the battery, to the user in a remote location.
5.The buck converter steps down the voltage of 12 volts provided by the battery to 5 volts
used by the different components.
6.The DHT11 sensor provides the temperature and humidity readings in the vicinity of the
entire system.
7.The coulomb sensor is used to provide the current, voltage and charge readings from the
battery to obtain the power consumed and State of Charge of the battery.
8.The battery which is 12 volts, 7 amperes provides power to the system.
The proposed solution offers an efficient and user-friendly solution to power optimization of the raspberry pi-based applications. By intelligently controlling the power cycles and providing monitoring, power is optimized while maintaining power availability. Future recommendations include integrating additional sensors and machine learning algorithms to make the system more
proactive.
Therefore this project contributes to an advancement of smart power management in IoT systems thereby promoting energy efficiency and sustainability and this can further be used or improved by project developers and researchers in energy conscious IoT applications. | en_US |
