Improvement in Network Lifetime for On-Demand Routing in Mobile Ad hoc Networks using either On-Demand Recharging or Transmission Power Control or Both
Bibliographic record
Abstract
Given a fixed energy budget for the operation of a mobile ad hoc network (MANET), on-demand recharging is the technique of charging the nodes initially with identical, but reduced energy level called the recharge quantum, and then recharging the nodes with the recharge quantum of energy whenever the energy level at a node goes below a threshold level. Transmission power control is the technique of adjusting the transmission power at a sender node depending on the distance to the receiver node. The high-level contribution of this paper is a simulation-based analysis of the network lifetime obtained for each of the following four scenarios: [a] No power control, No on-demand recharging; [b] Power control, but no on-demand recharging; [c] On-demand recharging, but no power control and [d] Both power control and on-demand recharging. Network lifetime is defined as the time of first node failure due to the exhaustion of energy level at the node and the inability to further charge the node. The on-demand routing protocols studied are: Dynamic Source Routing (DSR), Flow-Oriented Routing Protocol (FORP) and the Min-Max Battery Cost Routing (MMBCR) algorithm run on the top of DSR. We illustrate the improvement obtained in network lifetime as we transition from scenarios [a] through [d]. Simulation results illustrate that scenarios involving on-demand recharging ([c] and [d]) yield a higher network lifetime than scenarios [a] and [b]. When we operate the network with both on-demand recharging and power control, we obtain the maximum improvement in network lifetime. The percentage of the supplied energy that has been consumed in the network at the time of first node failure for each of the four scenarios and the three routing protocols is also measured to illustrate the effectiveness of on-demand recharging in maximizing the usage of the available energy budget.
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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 | 0.003 |
| 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 | 0.000 | 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".