A Comparative Systematic Review of PRESENT and SIMON Algorithms for IoT devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the Internet of Things (IoT) continues to expand across industries such as healthcare, smart cities, and industrial automation, securing data on small, resource-constrained devices becomes increasingly important. Standard encryption algorithms such as AES typically require more processing resources than such devices can tolerate. This systematic review of the literature compares two of the most recognized lightweight encryption algorithms-PRESENT and SIMON-to determine which is better suited for IoT applications. Ten peer-reviewed publications from 2015 to 2025 were selected following PRISMA guidelines. In this review, we examine how these algorithms perform in terms of energy consumption and security. The findings indicate that SIMON generally consumes less energy and occupies a smaller hardware footprint, making it more suitable for battery-operated or ultra-low-power systems. PRESENT, though slightly more resource-intensive, is easier to implement and benefits from international standardization, which makes it preferable in contexts requiring regulatory compliance or auditability. Overall, there is no universally superior algorithm; the choice depends on the specific goals and constraints of the implementation context. The review also highlights future research directions, including standardizing benchmarking practices and evaluating resistance to side-channel attacks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it