Energy Efficient Routing for Multi Hop Ad Hoc Networks with Multiple Access and Adaptive Modulation to Maximise Throughput
Bibliographic record
Abstract
Abstract Wireless ad hoc networks are frequently deployed in strategic applications that require the use of battery powered nodes. A key requirement for these networks is to maximize the time span when all nodes have sufficient battery charge to participate in communication with other nodes. To meet this requirement, this paper describes a routing strategy that seeks to find the best balance between minimizing the power consumption and evenly using all nodes within the network to avoid early exhaustion of individual nodes. The proposed routing scheme is compared to reported schemes using minimum power routing and the results show that the proposed scheme gives a longer time until the first node’s battery energy is depleted with a lower network power consumption than schemes using just energy minimization. Multiple access techniques are discussed and a cost-effective scheme based on available wireless LAN channels and space division multiplexing is proposed. Each path can use one, two or three time slots according to the number of hops in the path. Adaptive modulation is used where the link power budget is sufficient to maintain the throughput per unit time regardless of the number of hops in the path. Simulation results show that the throughput can be significantly improved using adaptive modulation with a small reduction in the time until the first node’s battery energy is depleted.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".