A Comparative Systematic Review of PRESENT and SIMON Algorithms for IoT devices
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".