METHOD FOR REDUCING ENERGY CONSUMPTION BY IOT ELEMENTS
DOI:
https://doi.org/10.31891/2307-5732-2024-345-6-7Keywords:
IoT, energy efficiency, data prediction, data transmission reduction, battery life, LoRa, BLE, error threshold, simplified window method, data transmission optimizationAbstract
The article proposes an effective approach to reduce the amount of data transmitted from IoT nodes to the server, with an emphasis on saving energy resources and extending battery life. The main concept of the method is based on a prediction mechanism that allows IoT nodes to send data only when the absolute difference between the measured and predicted values exceeds a set error threshold. This solution allows to significantly reduce the amount of transmitted traffic, while maintaining a sufficient level of data accuracy for most applications. A simplified window method was used to implement the prediction, which provides a balance between approximate accuracy and low computational resource consumption, which is critically important for energy-efficient devices. During the experiments, Lightbug LoRa GPS trackers with data transmission via LoRa and BLE networks were used. Experimental results showed that reducing the amount of transmitted data by up to 50% allowed to significantly increase the duration of the devices, especially in conditions of high traffic intensity. For example, for LoRa technology, the battery life increased by 300% with intensive data traffic.
A detailed analysis of the results showed that at low values of the error threshold, the accuracy of the predicted data practically does not differ from the measured ones. However, with increasing the threshold, the accuracy decreases significantly, which may be unacceptable for some applications. The optimal value of the error threshold was determined experimentally, taking into account the balance between minimizing energy consumption and ensuring an acceptable level of accuracy.
The proposed method is universal and promising for use in energy-efficient IoT systems, especially in those scenarios where energy saving is crucial, and high data accuracy remains an important condition. This approach opens up opportunities for improving intelligent monitoring, management and data collection systems in various areas, including smart cities, agriculture, environmental monitoring and logistics.
