Impact of lossy links on performance of multihop wireless networks
Bibliographic record
Abstract
Multihop wireless networks have unique features such as lossy links and interference. Both interference and lossy links affect the maximum achievable throughput of a network. Some wireless networks have energy constraints. Lossy links also affect energy efficiency due to retransmissions and broadcasting. We investigate the impact of lossy links on maximum achievable throughput and minimax energy utilization. These can be modeled as linear programming optimization problems. We give optimal solutions for both flow-based and destination-based routing. Experiments show that lossy links do have significant impact on the maximum achievable throughput. There are cases where a network can only achieve half of the throughput of the corresponding lossless network. The results show less significant impact of loss on energy efficiency. In some cases, the loss may be advantageous for energy efficiency, since the energy consumption may be reduced due to the loss of broadcasting messages. Experiments also show the significant impact of overhearing on energy efficiency.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".