Integrating wireless sensor networks and mobile ad hoc networks for an enhanced end-user experience
Bibliographic record
Abstract
Wireless sensor networks (WSNs) sense and aggregate ambient information (e.g. space, environment or physiological data). Ambient information can enhance enduser experience and is made available to end-user applications (which may reside in another network) via gateways. Gateways are usually centralized and fixed. Mobile ad hoc networks (MANETs) are networks that can be deployed “on the fly”. They are useful in situations such as emergency response operations. When the ambient information collected by WSNs is intended for applications residing in a MANET, centralized and fixed gateways are not practicably feasible. This paper proposes an overall two-level overlay architecture to integrate WSNs (with mobile and distributed gateways) and MANETs, for an enhanced end-user experience. It also proposes a new architecture to interconnect the two overlays of the overall architecture. Motivating scenarios are presented, requirements are derived, and the two-level overlay is discussed along with the proposed interconnection architecture. The prototype is also presented.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".