Dynamic resource reuse towards participatory sensing networks
Bibliographic record
Abstract
In recent years the demand and density of Wireless Sensor Network (WSN) deployments has generated overwhelming underutilization of resources across multiple deployments. More recently, the proliferation of smartphone usage has augmented sensing architectures with readily available resources to enable data collection in real-time; aiding the adoption of participatory sensing network (PSN) paradigms. Unfortunately both literature domains remain disparate, and their operational mandates dictate significant variance despite their apparent common goals. In this paper we present a formal paradigm for resource representation across ubiquitous platforms, and present dynamic heuristics for utilizing WSNs and participatory-based transient resources towards serving multiple applications in concurrency. The presented paradigm, namely Dynamic Resource Reuse (DRR) WSN, supports multiple owners of resources and incentivizes their collaboration via token-reward systems. We capitalize on dynamic incentive mechanisms to solicit the contribution of resources from WSNs and PSNs. The core contribution of this work lies in operational synergy, facilitating cross-network functional utilization of all readily available resources, despite their network ownership.
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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 | 0.005 | 0.008 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".