Comprehensive survey of the IoT open‐source OSs
Bibliographic record
Abstract
The Internet of things (IoT) has attracted a great deal of research and industry attention recently and is envisaged to support diverse emerging domains including smart cities, health informatics, and smart sensory platforms. Operating system (OS) support for IoT plays a pivotal role in developing scalable and interoperable applications that are reliable and efficient. IoT is implemented by both high‐end and low‐end devices that require OSs. Recently, the authors have witnessed a diversity of OSs emerging into the IoT environment to facilitate IoT deployments and developments. In this study, they present a comprehensive overview of the common and existing open‐source OSs for IoT. Each OS is described in detail based on a set of designing and developmental aspects that they established. These aspects include architecture and kernel, programming model, scheduling, memory management, networking protocols support, simulator support, security, power consumption, and support for multimedia. They present a taxonomy of the current IoT open‐source OSs. The objective of this survey is to provide a well‐structured guide to developers and researchers to determine the most appropriate OS for each specific IoT devices/applications based on their functional and non‐functional requirements. They remark that this is the first such tutorial style paper on IoT OSs.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".