Re-Usable Resources in Wireless Sensor Networks: A Linear Optimization for a Novel Application Overlay Paradigm over Multiple Networks
Bibliographic record
Abstract
Today's abundance of sensors and their wireless/wired networks, coupled with a growing plethora of applications, necessitate a dynamic approach to the assignment of tasks to a network. The current practice in WSN design is almost always application specific, due to functional and resource tradeoffs that have justified much of the tailored research done so far. Identifying this as a major bottleneck in WSN advancement, this paper presents a new paradigm which decouples applications from WSN architectures and protocols. This paradigm views the network as an abundance of connected resources (hence functionalities) to match requirements of applications (old and new) based on utilization and feasibility factors. We present an elaborate abstraction of network resources, with detailed description of its governing utility attributes. Then we describe the view of applications as an aggregation of functional requirements based on a given set of resources. The intermediate mapping between applications and resources is then solved by a reduced linear optimization formulation, to realize the system as a whole. The paradigm is further explained via a multiple-application scenario and its representation and operation under our 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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".