Bibliographic record
Abstract
We advocate for a novel paradigm in Wireless Sensor Networks (WSNs). As a technology, it has evolved to a scalable networking paradigm with minimalistic operational mandates. However, inherited design principles of static functionality, that are pre-determined at design stage, hinder WSN evolvement. More importantly, while we design WSNs to endure harsh environments and scale in both urban and remote settings, we neglect two major factors. The over-deployment of WSNs renders many sensing nodes redundant in functionality, and inflates the cost of running applications; not to mention the resulting medium contention. In this paper we present a novel approach to expanding the operational scale of WSNs by adapting to the environment in which it is deployed. That is, capitalizing on an organic approach in thriving on available resources in the region of interest to reduce deployment cost, and solicit incentivized interaction among communicating resources to deliver dynamic sensing. Not only does this span a new dimension of reliability, over garnered resources, but presents a novel approach to assigning sensing tasks to available resources in correlation to their abundance and serviceability. We present our performance evaluation of reduction in operational costs, and the uptake of sensing tasks by neighboring resources via extensive simulations. We aim to benchmark WSN operational versatility and present a rigorous basis for evaluating the ability of WSNs to resiliently scale to new applications as well as handle intermittent and permanent failures.
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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 | 0.000 | 0.000 |
| 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".