Comparative Performance Analysis of Lightweight Cryptography Algorithms for IoT Sensor Nodes
Bibliographic record
Abstract
The Internet of Things (IoT) has become an integral part of future solutions, ranging from industrial to everyday human life applications. Adding a new level of intelligence to objects and automating decisions make this new technology appealing to everyone. However, applications that involve data are more vulnerable to various types of attacks. As a result, researchers are constantly exploring secure connections between IoT edge nodes. On one hand, suitable IoT nodes should be cheap and require low power, which means lower computational performance. On the other hand, a secure connection layer is power hungry and requires powerful hardware resources. Lightweight cryptography (LWC) algorithms are a promising solution to reduce computation complexity while maintaining a desired level of security. In the presented work, we attempt to address the issue of adding security to the IoT network layer by comparing the performance of 32 LWC algorithms with currently well-known algorithms on multiple IoT platforms (Raspberry Pi 3, Raspberry Pi Zero W, and iMX233). These 32 authenticated encryption with associated data algorithms have been selected from the second round of the LWC standardization process conducted by the National Institute of Standards and Technology. Power consumption, random access memory usage, and execution time are measured for these algorithms using the targeted embedded platforms that are used as IoT sensor nodes. The results of this study will assist researchers in choosing a suitable platform and optimal LWC algorithm for IoT applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".