Towards a global IoT: Resource re-utilization in WSNs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Internet of Things (IoT) is envisioned as a paradigm shift, with a plethora of applications, on the premise of well-established enabling technologies; prominently Wireless Sensor Networks (WSNs) and RFIDs. The former has evolved to improve energy efficiency and resilient operation, yet true scalability has only been recently probed and quite sparsely advanced. Moreover, the traditional approach, whereby most WSN platforms are tailored for a single-application, imposes significant rigidity in re-utilizing platforms for new applications, and limitations on re-using previously deployed ones. In remedy, we present a novel paradigm in WSNs to efficiently utilize network resources, and extend it to a platform for multiple applications to cross-utilize resources over multiple WSNs. We present the approach in three phases; the first calibers resources in the network and their usability. Then applications are represented as finite sets of functional requirements. Finally, we present an optimization approach to find an optimal mapping between applications and resources. This paradigm presents a leap in scalability, not only in a WSN but across multiple ones, dynamically accommodating varying resources being introduced and removed; in addition to utilizing transient resources in their vicinity. To this end, we present an architecture to efficiently adopt WSNs in IoT with changing demands and scale. Our approach is further explained and demonstrated via a detailed use case depicting the premise of IoT applications.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 it