Techniques for Exploiting Mobility in Wireless Sensor Networks
Bibliographic record
Abstract
A sensor network consists of tiny, low-powered and multifunctional sensor devices and is able to perform complex tasks through the collaborative efforts of a large number of sensor nodes that are densely deployed within the sensing field. Maintaining connectivity and maximizing the network lifetime are among the critical considerations in designing sensor networks and its protocols. Conservation of limited energy reserves at each sensor node is one of the greatest challenges in a sensor network. It has been suggested that mobility of some nodes/entities in a sensor network can be exploited to improve network performance in a number of areas, including coverage, lifetime, connectivityy, and fault-tolerance. In this context, techniques for effectively utilizing the unique capabilities of mobile nodes have been attracting increasing research attention in the past few years. In this chapter, the authors focus on some of the new and innovative techniques that have been recently proposed to handle a number of important problems in this field. It also presents a number of open problems and some developing trends and directions for future work in this emerging research area.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".