An energy‐efficient history‐based routing scheme for opportunistic networks
Bibliographic record
Abstract
Summary In opportunistic networks (Oppnets), nodes rely on contact opportunities between them to exchange information with each other. Routing and forwarding in Oppnets remains a challenging task because of the limited energy and bandwidth constraints. Various routing protocols for Oppnets have been proposed in the literature, but only few of them have explicitly investigated the energy issue. In this paper, some improvements in the already existing history‐based prediction for routing protocol for infrastructure‐less Oppnets (so‐called HBPR) is suggested so as to make it energy efficient. The proposed energy‐efficient HBPR protocol (EHBPR) addresses the energy constraints in HBPR and reduces the number of packets transferred in the network, which in turn results to a reduction in the nodes' energy consumption. Through simulations, the performance of EHBPR in terms of energy consumption is compared against the HBPR and the energy‐efficient n‐epidemic routing protocol. The results show that (1) EHBPR consumes 14.66% less energy than HBPR (respectively 13.14% less energy than n‐epidemic); (2) EHBPR generates 67.4% less dead nodes compared with HBPR (resp. 66.33% less dead nodes compared to n‐epidemic); and (3) EHBPR yields 77.86% less overhead ratio compared with HBPR (resp. 84.49% less overhead ratio compared with n‐epidemic). Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".