Designing a Energy Efficient Node Disjoint Multipath Routing technique to achieve energy efficiency in Mobile Ad Hoc Networks
Bibliographic record
Abstract
Mobile Ad Hoc Networks (MANETs) are wireless networks which comprise of mobile nodes with limited energy resources. Every node cooperates to perform routing and expends energy on a frequent basis. Nodes are mobile thus link breakages are common and new routes need to be established quickly. Traditional routing protocols tend to find shortest routes to destination providing best-effort delivery service. However due to limited energy and bandwidth resources shortest path routes may not suffice and may usually degrade the performance of the network. In this paper an Energy aware Node Disjoint routing technique END-AODV is proposed that is based on the traditional AODV protocol. The technique designed is based on the argument that node disjoint multipath routing can conserve energy more efficiently as compared to link disjoint routing. Link Disjoint routing leads to overuse of a subset of nodes thus decreasing the overall network lifetime. The technique proposed incorporates energy drain rate metric to establish energy aware routes which are node disjoint in nature. Simulation results with ns2.34 simulator show efficiency of the proposed technique in terms of packet delivery ratio, average energy consumption per data bit delivered, network lifetime and average end to end delay.
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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 | 0.000 | 0.001 |
| 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".