Interference‐aware clustering approach improving QoS for linear WSNs using a token‐based MAC protocol
Bibliographic record
Abstract
Summary Wireless sensor networks (WSNs) have received a lot of attention from both academia and industry due to the increasing need for ubiquitous computing for monitoring applications, the continuous advances in miniaturization of electronic devices, and the ultra‐low‐power wireless technologies. These innovations in technology have driven the curiosity to use sensor networks in a new kind of applications such as road track or railway monitoring, border monitoring, oil and gas, or even water pipeline monitoring. Due to the underlying linear topology of these applications, a new type of network, called a linear sensor network (LSN), has emerged. Because of the specific characteristics of this application and the resource constraints of sensors, some of the major challenges faced in LSNs are to reduce end‐to‐end delays, to maximize the packet delivery ratio to a sink, and an even distribution of the load between nodes. To achieve these objectives, it is necessary to control node‐to‐node packet traffic conditions and to manage radio interference created by simultaneously active nodes. This paper addresses these challenges and proposes a new method of clustering LSNs that reduces or controls radio interference risks in order to satisfy these objectives, application needs, and the resource limitations of sensor nodes in the best possible way. This method is applied for LSNs using a token‐passing mechanism to access the medium. The performance evaluation is conducted by using a realistic propagation model in the analytical evaluation and also a NS‐2 simulation process.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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".