LoRa Power Model for Energy Optimization in IoT Applications.

Area of Science:

• Electronics
• Computer Engineering
• Wireless Communication

Background:

• Energy efficiency is critical for Internet of Things (IoT) nodes, especially those using energy harvesting without batteries.
• Developing accurate power consumption models is essential for optimizing the design of low-power IoT devices.

Purpose of the Study:

• To create a parametric power model for the Semtech SX1276 LoRa transceiver.
• To enable accurate prediction of average power consumption in ultra-low power remote sensing scenarios.

Main Methods:

• Characterization of the LoRa transceiver in various states: startup, transmission, reception, and sleep.
• Development of a state-based model predicting power consumption based on transmission power, sleep strategy, packetization, and data rate.
• Implementation of the model in a Python simulator for mean, best-case, and worst-case power consumption estimates.

Main Results:

• Experimental validation showed a cubic fit for transmission peaks with a determination coefficient of 0.99.
• Reception power consumption was modeled as a constant.
• The model's predictions were validated on an ASIC-based sensor node, achieving accuracy within 10% of measured values.

Conclusions:

• The developed power model effectively predicts LoRa transceiver energy consumption for IoT applications.
• The framework aids in understanding energy efficiency vs. robustness trade-offs, supporting custom controller design for ultra-low power IoT nodes.