Reciprocal public sensing for integrated RFID-Sensor Networks
Bibliographic record
Abstract
Public sensing is an application in which sensory systems embedded in smart devices, vehicles, residential and public spaces form a collective cloud of data sources from which multi-owned access points realize end-users' service requests. This conception can be further extended under the umbrella of integrated RFID-Sensor Networks (RSNs) to include RFID systems. Such a configuration is heterogeneous by nature and faces many challenges in terms of data delivery and resource management. In this paper, we represent a Reciprocal Public Sensing (RPS) scheme for integrated RSN architectures. Our scheme incorporates heuristic solutions for static sensors and mobile data collectors, in addition to a reciprocal agreement for data exchange over the tiers of the proposed architecture adhering to the social welfare of the network as a whole. We provide simulation results showing how RPS outperforms other data delivery schemes in terms of minimizing delay, packet loss, and energy consumption, in addition to prolonging the overall network lifetime.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".