Wireless Sensor Network Reliability and Security in Factory Automation: A Survey
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
Industries can benefit a lot from integrating sensors in industrial plants, structures, machinery, shop floors, and other critical places and utilizing their sensing and monitoring power, communicating and processing abilities to deliver sensed information. Proper use of wireless sensor networks (WSNs) can lower the rate of catastrophic failures, and improve the efficiency and productivity of factory operations. Ensuring reliability and providing adequate security in these crucial services provided by WSNs will reinforce their acceptability as a viable and dependable technology in the factory and industrial domain. In this paper, we examine the reliability and security challenges of WSNs and survey their practicality for industrial adoption. We discuss the unique characteristics that distinguish the factory environment from the rest, elaborate on security and reliability issues with their respective solution measures, and analyze the existing WSN architectures and standards. A number of challenges and interesting research issues have emerged from this study and have been reported for further investigation.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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