Routing algorithms for ad hoc wireless networks with a realistic physical layer
Bibliographic record
Abstract
The design and evaluation of existing routing protocols for ad hoc wireless networks normally assume an ideal physical layer model, where the message is received if and only if two nodes are within the transmission range. Routing protocols designed for this model show poor performance in simulators implementing more realistic models. In this thesis, we introduce a model that simplifies the realistic log-normal shadowing model by approximating the probability of packet reception based on distance between nodes. We then design several localized, position based routing algorithms with and without acknowledgements, with fixed message sizes. An appropriate MAC protocol for acknowledging the message is described. Localized position and acknowledgement based protocols are based on calculating ideal hop count, and optimizing expected progress. Improved, iterative versions of these protocols are also presented. These algorithms strive to optimize the expected hop count (EHC) measure in delivering the message from the source to the destination, where EHC takes into account all acknowledgements and retransmissions. We also propose several localized non acknowledgement-based algorithms. These protocols aim to maximize the probability of delivery of a packet from the source node to the destination node. Our, performance evaluation shows that newly proposed localized protocols are competitive with global shortest weighted path based protocols, and superior to threshold based localized protocols that were also proposed in this thesis. We also studied the impact of imprecise location information on the performance of the suggested protocols.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".