Bibliographic record
Abstract
The dynamics of an ad hoc network are a challenge to protocol design because mobility inevitably leads to unstable routing, and consequently flows encounter fluctuations in resource availability on various paths during the lifetime of a session. This has become serious, especially for those protocols based on single-path reservation, as frequent reservation and restoration of reservation-based flows increase the instability of connections. Advances in wireless research are focusing more and more on the adaptation capability of routing protocols due to the interrelationship among various performance measures such as those related to topological changes (link breakages, node mobility, etc.) and quality of service (QoS) parameters (load, delay, etc). After giving a more detailed discussion of the existing work in adaptive routing, we summarize our work on Multipath Source Routing (MSR) in order to introduce our latest work on QoS-MSR.Multipath Source Routing (MSR) is an extension of DSR (Dynamic Source Routing) that incorporates the multipath mechanism into DSR. MSR is an adaptive routing for ad hoc networks. It considers the two fundamental issues in its design. MSR may adapt to topology changes by retaining the route discovery and route maintenance mechanism of DSR. In addition, MSR employs a probing-based load-balancing mechanism. Simulation results show that MSR can improve the packet delivery ratio and the throughput of TCP and UDP, and it reduces the end-to-end delay and the average queue size while adding little overhead. As a result, MSR decreases network congestion and increases the path fault tolerance quite well.As a multipath QoS routing protocol, QoS-MSR can collect QoS information through a route discovery mechanism and establish a QoS route with reserved bandwidth by using Multipath Bandwidth Splitting Reservation (MBSR). MBSR allows a bandwidth request to be split into several smaller bandwidth requests among multiple paths. According to our simulation results, QoS-MSR with MBSR decreases network congestion and improves the packet delivery ratio and end-to-end delay of all connections. In addition, the reserved packet ratio indicates that MBSR can improve QoS of reservation-based flows and can be made adaptive to ad hoc networks with high mobility.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".