Bibliographic record
Abstract
Recent advances in electronic miniaturization, software engineering and wireless communication technologies have enabled the deployment of low-power sensor nodes that are equipped with an embedded processing unit, memory, power-supply, on-board sensor, radio communication facilities (I.F. Akyildiz, W. Su et al. 2002).An important characteristic of sensor nodes is their ability to sense specific phenomena in a target field and send their data to a central node, called the Base Station/sink, possibly through multihop wireless communication links.Since most data gathering applications are concerned with collection of physical data that is generated in the target area monitored by sensor nodes, therefore coverage becomes a core meaure of performance.A fundamental issue in coverage is the quality of monitoring provided by the network.This quality is usually measured by how well deployed sensors cover a target area.In its simplest form, 1-coverage means that every point inthe target area is monitored at least one sensor.In recent years, the problem of providing sensor coverage has received extensive attention from the research community in the context of 2D sensor networks (Xing, Wang et al. 2005;Zhang and Hou 2005;Bai, Kumar et al. 2006).However, most of the real world sensor network deployments often a follow 3D model.Examples of such deployments are environmental monitoring in forests (Mainwaring, Culler et al. 2002;Szewczyk, Osterweil et al. 2004) where sensor nodes are deployed on trees of different heights in a forest, structural health monitoring of multistorey buildings (Kim, Pakzad et al. 2006;Lynch and Loh 2006) and underwater surveillance networks (Akyildiz, Pompili et al. 2005).In most cases such deployments follow a model where sensor nodes are placed in large quantities over a target region.Excessive deployment of sensor nodes is often desirable to protect the network from individual node failures.However keeping in mind the energy and bandwidth constraints for most applications, the coverage control problem translates to choosing a set of active nodes that ensure that the target region is sufficiently monitored.Considering the fact that sensors are deployed to interact with the physical phenomenon to gather data, coverage becomes one of the fundamental measures to gauge the service quality provided by the network to the application.Different applications may have 11 www.intechopen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".