Dynamic Reduced-Round TLS Extension for Secure and Energy-Saving Communication of IoT Devices
Bibliographic record
Abstract
The security of wireless Internet of Things (IoT) communication is a complex challenge due to not only growing attack surfaces and threats but also the limitations of energy consumption. As a significant portion of the IoT market is composed of both security- and energy-critical sectors, e.g., smart homes and e-health, there is a pressing demand for solutions to secure billions of IoT devices while minimizing energy footprint. To this end, this article proposes a transport layer security (TLS) extension to integrate a lightweight and self-monitored mechanism that dynamically balances communication security and power consumption according to the IoT device’s current battery level. Integrated within the TLSv1.3 protocol, the secure extension automatically adjusts the encryption round number of the negotiated cipher according to an operator-defined policy while ensuring the minimum required security level. A Proof-of-Concept (PoC) has been implemented on the wolfSSL library and a real-world IoT platform, on which the performance of the proposed mechanism has been reported for various lightweight ciphers. The results showed an energy reduction of encryptions by 57.1% and a battery saving of 9.4% when encrypting at 4 kBps with reduced-round encryption, demonstrating the potential of the proposed extension into the TLS protocol.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".