Tinkertoy: Build Your Own Operating Systems for IoT Devices
Bibliographic record
Abstract
The Internet of Things (IoT) makes it possible for tiny devices with sensing and communication capabilities to be interconnected and interact with the cyber–physical world. However, these tiny devices have limited computing power and memory, so they often cannot run commodity operating systems, such as Windows and Linux. IoT devices are deployed everywhere, from smart home appliances to self-driving vehicles, and their applications impose ever-increasing and more heterogeneous demands on software architecture. There are many special-purpose and embedded operating systems built to satisfy these wildly different requirements, from early sensor network operating systems, such as TinyOS and Contiki, to more modern robot and real-time control systems, such as FreeRTOS and Zephyr. However, the rapid evolution and heterogeneity of IoT applications calls for a different solution. Specifically, this work introduces Tinkertoy, a collection of standard operating system modules from which developers can easily assemble customized operating systems. A customized operating system provides precisely the functionality needed by an application and consumes up to four times less memory than other IoT operating systems without sacrificing performance.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".