Standards for Internet of Things ( <scp>IoT</scp> )
Bibliographic record
Abstract
The Internet of Things (IoT) links physical objects via sensors and software, allowing data exchange over the internet. Advances in sensor technology and communication protocols boost device connectivity. Key features of IoT include interoperability, edge and cloud computing, real-time operations, remote access, automation, security, and integration with other advanced technologies. IoT faces many challenges such as security risks, privacy concerns, energy consumption, data management issues, and system complexity. These drawbacks significantly impact various sectors such as smart grid, smart cities, healthcare, agriculture, consumer devices, and transportation. Standardization is crucial for ensuring interoperability, security, and scalability, with international and Indian standards driving innovation and adoption. This chapter presents an extensive review of international standards (namely, IEEE, IEC, ANSI, ISO, etc.) and national standards (namely, the Bureau of Indian Standards [BIS], etc.) of IoT applicable to various domains such as healthcare, agriculture, food industry, smart home, industrial automation, disaster management, cybersecurity, and data science. Standards presented in this chapter would be helpful for all the research and development aspirants in this domain from both academic and industrial perspectives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.031 |
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".