Multicommodity Lifetime Routing for Wireless Sensor Networks with Multiple Sinks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) have recently received increasing attention from research and development communities. In a WSN, the field information (e.g., temperature, humidity, airflow) is acquired via several battery-equipped wireless devices and is relayed towards a sink node. As the size of the WSNs increases, it becomes inefficient to gather all information in one sink. To tackle this problem, the number of sinks can be increased. The data information flow towards each of the sinks is called a commodity. In this paper, we formulate a lexicographically optimal commodity lifetime (LOCL) routing problem. A stepwise algorithm is proposed to obtain the optimal routing solution which can lead to lexicographical fairness among commodity lifetimes. Simulation results show that our proposed algorithm increases the normalized commodity lifetime compared to MLMS [1] and LMM [2] routing algorithms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".