A performance comparison of delay-tolerant network routing protocols
Bibliographic record
Abstract
Networks that lack continuous end-to-end connections among their nodes due to node mobility, constrained power sources, or limited data storage space are called DTNs. To overcome the intermittent connectivity, DTN nodes store and carry the data packets they receive until they come into communication range of each other. In addition, they spread multiple copies of the same packet on the network to increase the delivery probability. In recent years, several routing protocols have been developed specifically for DTNs. These protocols vary in the number of copies they spread and the information they use to guide the packets to their destinations. There have been some reviews of those protocols, but no performance comparison has been conducted. In this article, we study four well-known DTN routing protocols: EPIDEMIC, Spray-and-Wait, PROPHET, and MAXPROP. We introduce a procedural form to present the protocols. We measure the performance of the protocols in terms of packet delivery, delivery cost, and average packet delay. We compare the protocols' performance together with the results of optimal routing using real-life scenarios of vehicles and pedestrians roaming in a city. We conduct several simulation experiments to show the impact of changing buffer capacity, packet lifetime, packet generation rate, and number of nodes on the performance metrics. The article is concluded by providing guidelines to develop an efficient DTN routing protocol. To the best of our knowledge, this work is the first to provide a detailed performance comparison among the diverse collection of DTN routing protocols.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".