APPLIED RESEARCH

Optimization of energy consumption of computing devices of the Internet of Things using adaptive data processing methods

Authors

  • Andrey V. Ivashchenko Moscow Technical University of Communications and Informatics, 111024, Russia, Moscow, Aviamotornaya street, 8a
DOI: 10.25726/l2655-8014-1564-l EDN: AHRPDE

How to cite

GOST Ivashchenko A. V. Optimization of energy consumption of computing devices of the Internet of Things using adaptive data processing methods // STROITEL'NYE I DOROZHNYE MASHINY. 2026. Vol. 70. No. 7. P. 209-219. DOI: 10.25726/l2655-8014-1564-l
APA Ivashchenko, A. V. (2026). Optimization of energy consumption of computing devices of the Internet of Things using adaptive data processing methods. STROITEL'NYE I DOROZHNYE MASHINY, 70(7), 209-219. https://doi.org/10.25726/l2655-8014-1564-l

Abstract

Autonomous computing nodes of the Internet of Things are powered by primary chemical cells of limited capacity, and the radio channel remains the dominant item of charge expenditure. Periodic data collection with a fixed step generates information-redundant traffic under a strictly limited battery resource. The aim is to quantitatively assess the gain in average current and calendar service life of a node obtained by moving data processing to the node through adaptive mechanisms: send-on-delta sampling, payload compression, and local aggregation of events. An author energy model of the node is built on a LoRa transceiver and a low-power microcontroller; for four scenarios the daily charge balance by subsystem and the service life are calculated for a Li-SOCl2 cell of 2600 mAh. The average current decreases from 141.5 to 40.6 uA, which corresponds to a 71,3% reduction of energy consumption and an extension of the service life from 1,94 to 6,75 years. The main contribution comes from reducing the number and duration of transmissions rather than from compression, after which the radio channel still holds about 88% of the daily charge.

Keywords

Internet of Things energy consumption adaptive data processing wireless sensor network send-on-delta sampling data compression battery life LoRa

References

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Issue

Vol. 70 No. 7 (2026): Stroitel'nye i dorozhnye mashiny

Section

APPLIED RESEARCH

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