Modeling Routing Overhead of Reactive Protocols at Link Layer and Network Layer in Wireless Multihop Networks
Bibliographic record
Abstract
To keep information recent between two nodes, two types of link sensing feed-back mechanisms are used: link layer (LL) and network layer (NL). In this paper, we model and evaluate these link sensing mechanisms in three widely used reactive routing protocols: ad hoc on-demand distance vector (AODV), dynamic source routing (DSR), and dynamic MANET on-demand (DYMO). Total cost paid by a routing protocol is the sum of cost paid in the form of energy consumed (in terms of packet reception/transmission) and time spent (in terms of processing route information). Routing operations are divided into two phases: route discovery (RD) and route maintenance (RM). These protocols majorly focus on broadcast cost optimization performed by expanding ring search (ERS) algorithm to control blind flooding. Hence, our model relates link sensing mechanisms in RD and RM for the selected routing protocols to compute consumed energy and processing time. The proposed framework is evaluated via NS-2, where the selected protocols are tested with different nodes' mobilities and densities.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".