Energy-aware ant colony optimization based routing for mobile ad hoc networks
Bibliographic record
Abstract
In mobile ad hoc networks, nodes are mobile and have limited energy resource that can quickly deplete due to the multi-hop routing activities, which may gradually lead to an un-operational network. In the past decades, the hunt for a reliable and energy-efficient MANET routing protocol has been extensively researched. In this thesis, a novel routing scheme for MANETs (so-called MAntNet) has been proposed, which is based on the AntNet approach. Precisely, the AntNet algorithm is modified in such a way that the routing decisions are facilitated based on the available nodes energy. Additionally, some energy-aware conditions are introduced in MAntNet and replicated in the conventional AODV routing protocol for MANETs. The resulting energy-aware M-AntNet (E-MAntNet) and energy-aware AODV(E-AODV) are analyzed using NS2 simulations. The results show that E-MAntNet performs significantly better than MAntNet and E-AODV both in terms of network residual energy and number of established connections in the network.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".