Fault‐tolerance in wireless ad hoc networks: bi‐connectivity through movement of removable nodes
Bibliographic record
Abstract
ABSTRACT For a wireless ad hoc network to achieve fault‐tolerance, it is desired that the network is bi‐connected. This means that each pair of nodes in the network have at least two node‐disjoint paths between them, and thus, failure at any single node does not partition the network. In other words, in a bi‐connected network, there is no cut‐node (defined as a node such that the removal of it partitions the network). To make a connected but not bi‐connected network become bi‐connected, actions should be taken such that all cut‐nodes become non‐cut‐nodes. In this research, we propose to deal with cut‐nodes from a new perspective. Specifically, we first introduce a new concept of removable node , defined as a non‐cut‐node such that the removal of it does not generate any new cut‐node in the network. Then, we propose to move a removable node to a new location around a cut‐node. In this way, the cut‐node becomes a non‐cut‐node, that is, the failure of it does not partition the network anymore. Algorithms are provided (i) to identify removable nodes; (ii) to match cut‐nodes with a feasible set of removable nodes, in which all nodes can be simultaneously removed from the network without generating any new cut‐node in the network; and (iii) to derive the final location of a removable node such that its movement distance is the shortest and the associated cut‐node becomes a non‐cut‐node. The proposed algorithms do not guarantee the final bi‐connectivity but have the merits of a large success rate (almost 100% in the simulation), a small number of moved nodes, and a short total movement distance. In addition, the proposed algorithms are shown to be effective even when there are a large portion of fixed nodes in the network. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".