Impact of route length on the performance of routing and flow admission control algorithms in wireless sensor networks
Bibliographic record
Abstract
In this study, the impact of route length on the performance of a routing protocol and flow admission control is analysed. First, the authors present an end‐to‐end available‐bandwidth‐based proactive routing protocol for ad‐hoc wireless sensor networks. The routing protocol maintains the best data forwarding path in terms of the end‐to‐end available bandwidth. Second, to determine the impact of route length on a routing protocol's performance, they modify the routing protocol. The modified available‐bandwidth‐based protocol trades‐off the end‐to‐end available bandwidth against the route length. Third, they integrate a state‐of‐the‐art flow admission control algorithm with the proposed protocols and a shortest hop‐count‐based protocol. Through simulations they evaluate the following: (i) performance of the proposed protocols and a state‐of‐the‐art available‐bandwidth‐based opportunistic protocol and (ii) the effectiveness of a state‐of‐the‐art flow admission control algorithm over proposed protocols and a shortest hop‐count‐based protocol. The simulation results demonstrate the following drawbacks of not considering the hop‐count metric: longer data forwarding paths, higher number of retransmissions, and reduced effectiveness of the admission control algorithm. The modified available‐bandwidth‐based proactive protocol provides the best overall performance. Therefore, using their results they conclude that route length impacts the performance of routing and flow admission control algorithms, but is not a singularly decisive factor.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".