Energy-Efficient Check-and-Spray Geocast Routing Protocol for Opportunistic Networks
Bibliographic record
Abstract
Opportunistic networks (OppNets) are a type of challenged network where there is no guaranteed of end-to-path between the nodes for data delivery because of intermittent connectivity, node mobility and frequent topology changes. In such an environment, the routing of data is a challenge since the battery power of the mobile nodes drains out quickly because of multi-routing activities such as scanning, transmitting, receiving, and computational processing, effecting the overall network performance. In this paper, a novel routing protocol for OppNets called Energy-Efficient Check-and-Spray Geocast Routing (EECSG) is proposed, which introduces an effective way of message distribution in the geocasting region to all residing nodes while saving the energy consumption by restricting the unnecessary packet transmission in that region. A Check-and-Spray technique is also introduced to eliminate the overhead of packets in the geocast region. The proposed EECSG is evaluated by simulations and compared against the Efficient and Flexible Geocasting for Opportunistic Networks (GSAF) and the Centrality- Based Geocasting for Opportunistic networks (CGOPP) routing protocols in terms of average latency, delivery ratio, number of messages forwarded, number of dead nodes, overhead ratio, and hop count, showing superior performance.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".