Efficient and Density-Aware Routing for Wireless Sensor Networks
Bibliographic record
Abstract
Point-to-point routing is central to communication networks. In this paper, we present a novel addressing and routing scheme for wireless sensor networks. We base our approach on the observation that in real applications, sensors are usually deployed in groups; while it is impractical to predict the landing location for each individual sensor, the locations of sensors from the same group tend to follow certain probabilistic model. By taking advantage of this deployment knowledge, we design a Monte Carlo sampling algorithm that distributedly discovers group-level topology of the sensor field, and represents it as a compact atlas. Meanwhile we assign each node an address comprised of its group ID and local coordinates within the group. Efficient point-to-point routing is achieved as two sub-procedures, proactive path planning on the high-level atlas and reactive actual routing using local coordinates information. In addition, our approach takes account of node density information, and prolongs the network lifetime by conserving the energy of sensors in sparse areas, which is especially important for non-evenly dense sensor networks. Experimental results show that our approach enables efficient and density-aware routing even in environment with complex topology structures.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".