Dynamic Reduced-Round Cryptography for Energy-Efficient Wireless Communication of Smart 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
Securing the wireless Internet of Things (IoT) is a challenging issue do to their technological constraints: limited computing power, restricted batteries or inconsistent energy supply. With more than 26 billion devices connected in 2019, the expected 75 billion things by 2025 will require an even higher energy supply. Meanwhile, as smarts cities, industry and healthcare represent more than 75% of the IoT market share, these devices must be secured while limiting the impact on energy consumption. The lifetime of specific devices such as Wearable or Implantable Medical Devices (WMDs, IMDs) can then be significantly impacted. In this paper, we propose a generic design that dynamically reduces the energy consumption required by the addition of security within the IoT networks, according to the local level of battery use. This self-monitored, fully-automated, low-cost and remotely configurable mechanism adjusts the number of encryption rounds of the cryptographic primitive while guaranteeing the minimum level of security required. This method has been integrated into the Constrained Application Protocol (CoAP) with the Datagram Transport Layer Security (DTLS) using the AES-128 encryption standard, with 10 rounds (full) to 7, and can be implemented on other protocol stacks. We show a reduction in CPU power consumption of a Raspberry Pi of 19.67%. Finally, we estimate its efficiency by simulating the discharge of multiple batteries with different capacities. Our mechanism increases operating time up to 33 minutes and 15 seconds for a 10,000mAh Raspberry Pi battery when 150 messages of 4Kb per second are exchanged with an operator.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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