Empirical Evaluation and Reclassification of Cryptographic Algorithms for Energy-Efficient Secure Communication in Medical IoT Devices
Bibliographic record
Abstract
Internet of Medical Things (IoMT) devices demand cryptographic solutions that balance robust security with extreme resource efficiency, given strict constraints on power, memory, and processing. This study presents a detailed empirical evaluation of ten cryptographic algorithms—including symmetric ciphers and hash functions —across two representative microcontroller platforms: TM4C123GXL (ARM Cortex-M4F) and PIC32MX440F256H (MIPS32). Algorithms tested include AES-256-ECB, Tiny-AES (AES-128), XTEA, ChaCha20, Poly1305, NORX, HMAC, SHA-256, AEAD (AES-256-EAX), and Ascon-AEAD.Comprehensive measurements of energy, execution time, and memory footprint reveal substantial discrepancies between prior classifications and practical performance. Lightweight algorithms such as ChaCha20 and Ascon-AEAD consistently achieved superior energy and speed efficiency, while certain “lightweight” candidates like Poly1305 and Tiny-AES demonstrated higher resource demands. The study introduces a performance-based reclassification of cryptographic algorithms grounded in empirical results. It also evaluates end-to-end session behavior by integrating ECDH key exchange with ChaCha20-Poly1305 encryption in a real-world IoMT setup. Results confirm the viability of these algorithms in real-time telemetry pipelines and battery-sensitive wearables. The findings offer hardware-aware, use-case-driven guidelines for cryptographic algorithm selection in secure and sustainable IoMT systems aligned with emerging NIST standards.
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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".