Utilizing transient resources in dynamic wireless sensor networks
Bibliographic record
Abstract
In a technology where multiple networks are often deployed in concurrency, significant resource underutilization is witnessed in Wireless Sensor Networks (WSNs). As the manufacturing and deployment costs drop, multiple networks are introduced in overlapping vicinities to satisfy new functional requirements. Mostly with dedicated goals and deterministic operation schemes, practitioners seldom investigate the usability of visible resources already deployed in the region of interest, their utilization and the accommodation for transient resources that “pass-by” with a set of functional capacities. This paper presents a framework for classifying resources that contribute to the set of functional capacities of WSNs deployed in a given region, and the mapping of functional requirements set by multiple applications on these resources. We present a utility function to cater for successful utilization of transient resources; highlighting their importance in WSN longevity as well as dynamicity. An optimal formulation is presented for this mapping, with tunable rounds that cater for the temporal behavior of the network and its constituting resources. A use case further explains this paradigm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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".