STEMM Institute Press
Science, Technology, Engineering, Management and Medicine
A Study on the Energy Consumption of Encryption Algorithms for Drones in Extreme Environments
DOI: https://doi.org/10.62517/jike.202604310
Author(s)
Yanfang Li
Affiliation(s)
Hebei University of Technology, Tianjin, China
Abstract
With the widespread application of unmanned aerial vehicles (UAVs) in extreme environments such as the polar regions, high plateaus and the deep sea, issues relating to communication security and energy consumption have become increasingly prominent. This paper investigates the energy consumption characteristics of encryption algorithms used in UAVs operating under extreme conditions. Mathematical models of mainstream encryption algorithms, including AES, RSA and ECC, were established using MATLAB. These were combined with CMOS circuit power consumption theory to construct a hardware energy consumption estimation model, and energy consumption predictions were carried out using the Gem5 architecture simulator and the McPAT tool. The study focuses on analysing the impact of temperature on leakage current and the effect of atmospheric pressure variations on circuit performance, simulating typical extreme environments ranging from -40°C to 85°C and from 0.1 to 2 atmosphere. The results indicate that under low-temperature conditions, encryption power consumption decreases significantly but computation time increases; under high-temperature conditions, increased leakage current leads to an exponential rise in power consumption; and changes in atmospheric pressure primarily alter power consumption indirectly by affecting heat dissipation. Comparative analysis reveals that elliptic curve encryption algorithms exhibit the best power consumption stability under extreme conditions. This study provides a theoretical basis and technical support for the selection and power consumption optimisation of encryption algorithms for UAVs operating in extreme environments.
Keywords
UAVs; Extreme Environments; Encryption Algorithms; Energy Consumption Optimisation; MATLAB Simulation
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