Performance Evaluation of Routing Protocols for Opportunistic Networks: An Energy Efficiency Perspective
Bibliographic record
Abstract
In most computer networks, a path between a sending node and the destination node is requiredto transfer data. There are instances where such a path for communication may not exist, suchas in space communications, and the standard message routing techniques will not work. OpportunisticNetworks were developed to solve this problem. Infrastructure-less Opportunistic Networks(OppNets) are a type of Delay Tolerant Networks where nodes in the network are responsible forforwarding messages to the destination nodes under the constraints of intermittent connectivity, dynamictopology changes, and non-guarantee of an end-to-end path. Various routing protocols weredeveloped for OppNets; however, these routing protocols are energy-inefficient, and this is a problembecause devices that are used in OppNets are usually battery-powered. In this project, newenergy-efficient routing protocols for OppNets, namely, E FirstContactRouter, E WaveRouter, EFloatingContentRouter and E LifeRouter were developed from their energy-inefficient base routingprotocols, namely FirstContactRouter, WaveRouter, FloatingContentRouter and LifeRouter. Theenergy-efficient routing protocols were compared against their energy-inefficient base protocols andother developed energy-efficient routing protocols, namely E-Prophet, E-Epidemic, E-MaxProp andE-Spray&Wait routing protocols. The energy metrics that we used to do the tests were the numberof dead nodes and the average remaining energy. Simulations were carried out using the OpportunisticNetwork Environment (ONE) simulator and it was discovered that the proposed energy-awarerouting protocols outperform their non-energy-aware counterparts in terms of the above-mentionedperformance metrics.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".