A pricing scheme for porter based delivery in integrated RFID-Sensor Networks
Bibliographic record
Abstract
RFID-Sensor Networks (RSNs) represent the pervasive components of the heterogeneous Internet of Things (IoT) paradigm. By incorporating both the identification and localization capabilities of Radio Frequency Identification (RFID) and the sensing and inter-communication of Wireless sensor networks (WSNs), RSNs are capable of realizing the IoT on a global scale. A pressing hindrance to cost-effectiveness lies in the deployment of high-end relay nodes that potentiate the backbone of the IoT. Paradigms that depend solely on deploying enough readers and relays to maintain network connectivity and coverage, often converge to infeasible costs. In this work we capitalize on pre-existing mobile nodes in the field, dubbed porters, to carry out the relaying task; utilizing their ubiquitous and dense presence and integrating multiple coexisting systems. One of the prominent challenges facing the integration across multiple systems is the trade factor governing their cooperation. That is, what would the porter gain in return for forwarding RSN packets? The pricing scheme governing the operation and trade-off functionality across RSNs is a hindering factor, seldom probed in current literature. In this paper, we introduce a pricing scheme for a delivery framework in RSNs for the IoT paradigm. Our framework incorporates porter nodes with variable mobility, buffering and transfer capacities; connecting relays and base-stations. Our pricing scheme incorporates major factors of impact, most prominently porter and relay density, network load and cost of service delivery. We present a formal model for this framework, elaborated upon with a use case.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".