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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".