Impact of route length on the performance of routing and flow admission control algorithms in wireless sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it