A compact and energy-efficient semi-systolic field multiplier for secure IoT edge devices in smart city applications
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
The accelerated evolution of IoT applications within smart cities is imperative for the advancement of digital economies, as these technologies significantly optimize urban management, enhance service delivery, and promote sustainable growth, thereby aligning with various Sustainable Development Goals (SDGs). Nonetheless, the extensive deployment of IoT devices introduces substantial security concerns due to the sensitive nature of the data they collect and the heightened risk of cyberattacks on critical infrastructure. Regularly, IoT devices are susceptible to vulnerabilities arising from limited processing capabilities and the lack of industry-standard security protocols. Addressing these security challenges is essential to ensure that the benefits of IoT can be fully harnessed without compromising safety. The limited resources of IoT edge nodes complicate the implementation of cryptographic protocols, which primarily rely on finite-field multiplication as a key operation. Consequently, an efficient implementation of this operation is essential for deploying cryptographic protocols on these compact nodes. Therefore, this research focuses on developing a low-area and low-energy parallel semi-systolic structure for an effective field multiplication algorithm, specifically the Montgomery multiplication algorithm. A comprehensive analysis of the proposed multiplier construction reveals significant reductions in both area and power consumption compared to existing efficient systolic and semi-systolic multipliers. These findings suggest that the proposed multiplier architecture is ideally suited for integration into cryptographic processors in resource-constrained IoT devices utilized in smart city applications. By optimizing space and power consumption, this design not only improves the performance of IoT edge nodes but also facilitates secure communication, thereby supporting urban developmental initiatives.
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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