Towards Reliable IoT: Fog-Based AI Sensor Validation
Bibliographic record
Abstract
Trust, reliability, and validation of data collected in distributed edge sensor systems is an increasingly relevant issue. Though the obvious solution of deploying redundant identical systems can provide validation, real-world modification constraints can sometimes make this difficult, or even prevent this. However, many distributed sensors exist for other purposes, that may be available to be used. Introducing validation with existing sensors may impose too high a requirement for bandwidth to use cloud-based validation, while edge-based validation may require too much computing power. A fog-based validation layer using sensory substitution is presented. With the rise of cyber-physical attacks on cloud, fog, and edge computing systems, validation is important, and lack of correct validation has been seen in some high impact cases where incorrect sensor data can be thought of as as true. A playback cyber-attack is discussed, and an algorithm for increasing reliability of IoT systems in the case of typical sensor errors or more serious incidents like cyber-physical attacks is presented. Given the need for dependable autonomy and reliability in IoT systems, this paper presents a method of sensor validation to increase robustness, resilience and dependability of sensed data by detecting false positives and negatives, and corroboration of true positives and negatives, using sensory substitution. Perhaps sometimes sensor data is trusted without ongoing validation. Using the example of cameras and artificial intelligence-based human presence detection, as well as using ambient distributed magnetometers and luminosity sensors, examples of a fog-based corroboration and validation methodology for human detection is presented. Results show the technique is an effective vector for sensor validation using available sensors, and scenarios where sensory substitution corrects false positives and false negatives from an artificial intelligence visual model are shown.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".