Big Sensed Data: Evolution, Challenges, and a Progressive Framework
Bibliographic record
Abstract
The rise of sensor deployments, uptake of the Internet of Things (IoT), and new manifestations of sensing systems (e.g., crowd sensing, M2M-driven sensing, cloud sensing) has resulted in a tide of sensed data that is potentially drowning our communication resources and hindering big data analytics with superfluous data. We argue that efficient management of IoT systems in smart communities and cities lies not in sensing systems alone, but in the expedited funneling and processing of data as we attempt to prune the unnecessary and build on the valuable. The quest for energy efficiency that dominated sensor networks for so long is now matched with a more pressing demand for access ubiquity and real-time operation. We highlight how big data became a challenge in sensing systems, then elaborate on the status quo in managing this challenge under different research umbrellas. We draw upon three planes that encompass current and future developments for the management of big sensed data (BSD), namely resources, data, and information planes, detailing their pertinent challenges and how evolving solutions can streamline their contributions in light of others. We conclude by highlighting core challenges rising across these three planes, and potential solutions to addressing synergy in coping and scaling with BSD.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".