Optimizing Power Consumption in IoT Sensor Systems through Adaptive Duty Cycling with Intelligent Automated Switching

Abstract

With the increasing demand for energy-efficient and automated sensing systems, the operation of DHT11 temperature and humidity sensors in different modes to optimize energy consumption has been explored. The experimental setup consists of a DHT11 sensor, INA219 current sensor, and IRLZ43N MOSFET relay, interfaced with a Raspberry Pi 4 to collect and upload data to the Adafruit IO cloud platform. Additionally, Google Assistant and IFTTT are used for voice-controlled sensor activation. The study evaluates three operational modes: continuous operation, code-controlled operation, and voice-controlled operation. In continuous operation, the sensor remains active for three hours, consuming 91.39 J of energy. In code-controlled mode, the sensor operates for a fixed time and is pushed in an idle state for the rest of the time. There is a significant reduction in energy consumption demonstrating a 99.8% energy saving as compared to continuous operation. The voice-controlled mode, which activates the sensor using voice commands, further optimizes power consumption, requiring only 0.147 J over three hours. However, a 20-30 second activation delay was observed due to the cloud-based processing of voice commands. The findings of this study highlight the potential of optimized duty cycling and intelligent automation in reducing energy consumption and improving sensor efficiency in IoT-based applications.