Hybrid Community-Based Forwarding: A complete energy efficient algorithm for pocket switched networks
Bibliographic record
Abstract
Sensor devices and the emergent networks that they enable are capable of transmitting information between data sources and a permanent data sink. Since these devices have low-power and intermittent connectivity, latency of delivery for certain classes of data may be tolerated in an effort to save energy. Several previously developed algorithms employ models which considers the popularity of individual nodes within communities and forward messages to nodes with higher probability of delivery according to some heuristic. In previous work, we developed Community-Based-Forwarding (CBF) that considers the interactions between communities as a factor in message forwarding. Using this information, CBF is able to exploit intermediate connections between clusters to route messages with more balanced node participation and higher levels of reliability and efficiency. One disadvantage of CBF was an increased delivery latency for some subset of messages that could not be delivered using other algorithms. In this paper, we extend the semantics of CBF with the Hybrid CBF algorithm (HCBF) by optimizing forwarding inside communities by considering the social diversity (measured by Unique Interactions). We find that all performance metrics are improved with this heuristic on a representative set of human mobility traces, but most significantly the message delivery latency is substantially improved over the other algorithms studied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".