Application of Green IoT Based on Green Computing Algorithms for Energy Efficiency in Logistics Automation Devices
Keywords:
Green Computing, Energy Efficiency, Logistics Automation, Sleep Wake Scheduling., Green IoTAbstract
ABSTRACT
The rapid implementation of automation in the digital start-up logistics sector has led to a massive surge in Internet of Things (IoT) energy consumption, primarily driven by continuous wireless radio transmissions. This study aims to design and evaluate the effectiveness of Green IoT architecture integrated with a green computing algorithm, utilizing adaptive sleep-wake scheduling and data aggregation mechanisms to minimize power consumption in warehouse automation devices. The research method employed a quasi-laboratory experiment comparing conventional (always-on) IoT architecture with the proposed green computing-based IoT architecture using ESP32 microcontrollers and the LoRaWAN protocol. Evaluation parameters were measured based on total current consumption (mAh), packet delivery ratio (PDR), and estimated sensor node battery lifespan. The results demonstrate that the implementation of the green computing algorithm successfully reduces IoT device energy consumption by up to 73.4% compared to conventional methods without significantly degrading data transmission performance, as evidenced by a sustained PDR value above 98.2%. Implicatively, this research contributes to the theoretical framework of green networking and offers a practical solution for the logistics industry to sustainably cut operational costs and reduce battery electronic waste.
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