A Review of Temperature-Dependent Encryption Scaling in IoT Chipsets: Intelligent Modeling, Electronics Integration, and Real-World Applications
Bibliographic record
Abstract
The rapid expansion of the Internet of Things (IoT) has led to billions of interconnected devices operating under varying environmental conditions, where temperature fluctuations significantly affect the performance, reliability, and security of IoT chipsets. Encryption mechanisms, essential for ensuring data confidentiality, integrity, and authentication, are highly sensitive to hardware constraints such as power consumption, processing capability, and environmental stress. Temperature-induced variations in semiconductor behavior can influence encryption latency, energy efficiency, and error rates, thereby impacting overall system performance. This review presents a comprehensive analysis of temperature-dependent encryption scaling in IoT chipsets, focusing on intelligent modeling, hardware–electronics integration, and real-world applications. It highlights the limitations of traditional encryption methods in resource-constrained environments and emphasizes recent advancements such as adaptive encryption scaling, temperature-aware cryptographic design, lightweight algorithms, and hybrid approaches. Additionally, hardware-based primitives and emerging frameworks enhance security. However, challenges persist in balancing energy efficiency with robust security and in developing standardized, scalable solutions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".