Dynamic Reduced-Round TLS Extension for Energy-Saving Encryption in Wireless IoT Communications
Bibliographic record
Abstract
Securing the wireless Internet of Things (IoT) is a complex challenge due to devices’ computing capacity limitations, battery restrictions or insufficient power supply. Reaching 30 billion connected devices in 2020, the IoT sector is booming. According to marketing studies, by 2025, the global IoT market is expected to reach $34.4 billion and the global IoT battery market is estimated to growth to $15.8 billion. Nevertheless, the smart city, connected healthcare, Industry 4.0 and home security, representing over 75% of the IoT market, raise critical cybersecurity and energy consumption issues. The battery lifespan of specific devices such as Wireless Sensor Networks (WSNs), Wearable or Implantable Medical Devices (WMDs, IMDs) can then be drastically impacted. To meet emerging demands, new solution to provide both cybersecurity and energy efficiency must be developed. Hence, this thesis research tried to develop a dynamic and secure solution to balances communication security and power consumption according to the IoT device's current battery level and the reduced-round cryptography. The contributions are as follow: (1) the security and power consumption evaluation of reduced-round cryptography on different lightweight ciphers; (2) the design, and implementation of a dynamic mechanism to control the battery discharge by adjusting the communication encryption cipher reduced-round value; (3) the design, integration and evaluation of our dynamic reduced-round mechanism integrated within TLS protocol version 1.2 and 1.3. The results of the two experiments confirm the efficiency of the reduced-round cryptography and of our dynamic round-reduced TLS extension to achieve a trade-off between IoT's communications security level and energy savings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".