A Reliable Transmission on Cluster Based Wireless Ad Hoc Network with Adaptive Negotiations Using Vector Assisted Energy Efficient Dynamic Opportunistic Routing Mechanism
Bibliographic record
Abstract
Reliable data transmission within wireless ad hoc networks is a formidable challenge, owing to the distinct attributes of mobile data communication.Existing routing protocols struggle to accommodate the dynamic nature of data traffic, leading to energy losses and substantial propagation delays.In response to these challenges, we introduce a novel Vector Assisted Energy Efficient Dynamic Opportunistic Routing model, implemented through an Adaptive negotiation-based cluster routing protocol.The approach involves strategically partitioning the network into clusters, each facilitated by an optimal cluster head.These clusters optimize data transmission and network longevity by gathering mobile nodes through negotiations.Our model leverages a reactive opportunistic routing protocol and organizes packet forwarding into vectors.Through extensive simulations using NS2, we evaluate the proposed scheme against conventional routing protocols.Key metrics, including Throughput, Packet delivery ratio, Average end-to-end delay, Network lifetime, and packet drop, demonstrate the efficacy of our model.Notably, our approach outperforms conventional methods, particularly in energy savings during data collection and transmission within high-traffic contexts.And our study contributes a novel solution to the challenges of wireless ad hoc networks.The Vector Assisted Energy Efficient Dynamic Opportunistic Routing model showcases superior reliability, efficiency, and network longevity.This work not only advances data communication in such networks but also provides a template for future research in enhancing wireless communication systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".