Congestion- and selfishness-aware social routing in delay tolerant networks
Bibliographic record
Abstract
Delay Tolerant Network (DTN) is a type of network that permanent connections between nodes are not always available. Routing in DTN uses store-carry-and-forward scheme, where nodes store and carry data until a suitable message carrier appears. Positive social characteristics such as centrality and friendship can be used to make a better routing decision in DTN. However, negative social characteristics such as selfishness may decrease the network performance. Selfish nodes for their individual or social benefits, do not contribute in message relaying when the device resources like battery level are low. Moreover, to achieve a better network performance we need to consider buffer congestion at the rely node. In this paper, we propose Congestion Aware and Selfishness Aware Social Routing protocol (CASASR) which by using social characteristics and awareness of buffer congestion and selfish behavior in the network selects a better relay. Using Poisson process, we find a utility value to select a relay node with a higher chance to deliver the message to the destination in the remained message TTL. In addition, we take into account the social and individual selfish behavior of nodes to adjust energy level available for message relaying. Comparison of our algorithm against several well-known algorithm shows that CASASR performs better in terms of delivery ratio and delivery overhead.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".