Energy aware routing for efficient green communication in opportunistic networks
Bibliographic record
Abstract
Opportunistic networks are characterised by intermittent connectivity, unstable topology, and no assurance of a fixed end‐to‐end path to transfer the messages because of nodes mobility. Due to this, the store‐carry and forward mechanism is used for routing the data in such networks. Considering that the energy of relaying nodes during the routing operation get depleted, there is a clear demand for power‐aware routing schemes for opportunistic networks. This article proposes an energy‐efficient altruism‐based trust‐dependent message forwarding routing protocol for opportunistic networks (called E‐ATDTN), where social matrices are exploited to determine the trustworthiness of a node in participating in the message forwarding procedure. Extensive simulations are conducted to assess the performance of the newly proposed E‐ATDTN protocol against that of the power‐aware PRoPHET, energy efficient PRoWait, and power‐aware EDR protocols showing that E‐ATDTN outperforms E‐PRoPHET, E‐ProWait, and E‐EDR in terms of average residual energy, overhead ratio, dropped messages, and number of dead nodes under varying time‐to‐live, and message generation interval.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".