Using the Cloud to Improve Sensor Availability and Reliability in Remote Monitoring
Bibliographic record
Abstract
Although there have been significant advancements in low-power remote sensors in recent years, the challenge of sensor availability and data reliability in remote monitoring applications still persists. The fault and failure of sensors will affect the reliability of the monitored data and subsequently the adverse effect will inevitably propagate itself to the data analytics stage. There are many existing solutions focusing on improving sensor nodes to enhance data reliability and couple it with various energy harvesting techniques to prolong the availability of sensor nodes. This paper presents a complementary solution to these existing solutions by analyzing the correlation between data from different sensor nodes using cloud computing resources. The discovered relationship between the sensor nodes can then be used to improve data reliability and availability of sensor nodes. Performance evaluations using real data sets show that there are indeed relationships between the collected data, and through these discovered relationships the fault detection and fault masking methods outperform conventional approaches such as autoregressive-integrated moving average. In addition, this paper also proposes an approach to extend operation of sensor nodes duration through the discovered relationships, with experiments showing promising results.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".