Optimum Neighbors for Resource-Constrained Mobile Ad Hoc Networks
Bibliographic record
Abstract
This paper presents an investigation on the optimum number of neighbors for mobile ad hoc networks (MANETs). The MANETs are self-configuring and self-organizing networks. In such a network, energyconstrained mobile nodes share limited bandwidth to send their packets to the destinations. The mobile nodes have a limited transmission range and they rely on their neighbors to deliver their packets. Hence, the mobile nodes must be associated with the required (i.e., optimum) number of neighbors. As the number of neighbors is varied, a trade-off exists between the network connectivity and available bandwidth per mobile node. To investigate this issue, we consider Dynamic Source Routing (DSR) as the routing protocol and IEEE 802.11 as the MAC layer protocol in this work. We consider both static and dynamic scenarios in this work. We simulated the ad hoc networks via network simulator (NS-2) and the simulation results show that there exists an optimum number of neighbors for the static case. We also show that mobility has a grave impact on the performance of the MANETs in terms of network throughput, end-to-end delay, energy consumption, and packet loss. Hence, we need to increase the number of neighbors under mobility conditions. However, there is no global optimum number of neighbors for the mobility case.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".