Joint routing and link rate allocation under bandwidth and energy constraints in sensor networks
Bibliographic record
Abstract
In sensor networks, both energy and bandwidth are scarce resources. In the past, many energy efficient routing algorithms have been devised in order to maximize network lifetime, in which wireless link bandwidth has been optimistically assumed to be sufficient. This article shows that ignoring the bandwidth constraint can lead to infeasible routing solutions. As energy constraint affects how data should be routed, link bandwidth also affects not only the routing topology but also the allowed data rate on each link. In this paper, we discuss the sufficient condition on link bandwidth that makes a routing solution feasible, then provide mathematical optimization models to tackle both energy and bandwidth constraints.We first present a basic mathematical model to address using uniform transmission power for routing without data aggregation, then extend it to handle nonuniform transmission power, and then routing with data aggregation. We propose two efficient heuristics to compute the routing topology and link data rate. Simulation results show that these heuristics provide more feasible routing solutions than previous work, and provide significant improvement on throughput and lifetime.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".