AVN-AHH-VBF: Avoiding Void Node with Adaptive Hop-by-Hop Vector Based Forwarding for Underwater Wireless Sensor Networks
Bibliographic record
Abstract
In this paper, we propose a routing protocol AVN-AHH-VBF for underwater wireless sensor networks (UWSNs). Sensor nodes forward data packets in multi-hop fashion within a virtual pipeline. As nodes outside pipeline do not forward data packets, thus, flooding is avoided in the network. Mitigating flooding in network, in turn, decreases energy consumption and improves delivery ratio. Moreover, on each hop, forwarding towards void region of the network is avoided with the help of two hop information. Avoiding void hole on each hop reduces number of transmissions for a data packet, for which route to sink does not exist. Thus, energy wastage by each dropped packet is reduced. In addition, taking the benefit of broadcast nature of the network, best forwarder (non-void) is selected from the transmission range of a node. Best forwarder selection results in increased delivery ratio. Furthermore, we incorporate two new factors in holding time calculation. First, the number of hops a data packet has already traversed along its journey that is started from source node. Second, the number of neighbors of a node which is calculating the holding time, is incorporated. A sensor node with less number of neighbors holds data packet for long duration of time than the node with more neighbors. By doing so, holding time is reduced on each hop throughout the journey of a data packet. Extensive simulations verify that AVN-AHH-VBF minimizes end-to-end delay upto 57% as compared to existing AHH-VBF. Also, in the proposed technique, energy wasted by a dropped packet is about 54% less than counterpart technique. At the same time, compared with AHH-VBF, delivery ratio of AVN-AHH-VBF is approximately 8% improved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".