An altruism‐based trust‐dependent message forwarding protocol for opportunistic networks
Bibliographic record
Abstract
Summary In opportunistic networks (OppNets), which are characterized by intermittent end‐to‐end connections, the messages are routed in a store‐carry‐and‐forward fashion using the locally inferred knowledge about the behavior of nodes. As such, most OppNets routing protocols use social metrics that are dependent on the nodes' past information. But the participation of nodes in the message forwarding process is not guaranteed without incentivizing them because most nodes are reluctant in sharing their private resources for public uses. In this paper, some socially derived psychological attributes of a node are introduced to ensure their trustworthy participation in the message forwarding process, leading to the design of an altruism‐dependent trust‐based data forwarding mechanism for OppNets (called ATDTN). In this protocol, each node is associated with a dynamically changing altruism value representing its trust in the network, which is used to determine its status with regard to its participation in message forwarding. Through trace‐driven simulations using the ONE simulator, it is shown that ATDTN outperforms IronMan and SimBet protocols for routing in OppNets (respectively, 18% and 48% improvement), in terms of delivery ratio, end‐to‐end delay, overhead count, and average number of hops, under varying buffer size and time‐to‐live.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".