A controlled flooding approach to efficient routing in ad-hoc wireless networks
Bibliographic record
Abstract
The focus of this dissertation is ad hoc wireless networks. A correct efficient operation of such networks depends on the interaction of several protocols dealing with routing, medium access control, power control and many other issues. This dissertation is primarily concerned with the development and evaluation of such protocols. In this dissertation, we show how flooding can be adopted as a reliable and efficient routing scheme in ad-hoc wireless mobile networks. It turns out that, with the assistance of some tunable heuristics, flooding is not necessarily inferior to sophisticated point-to-point forwarding schemes. We have developed a reactive broadcast-based ad-hoc routing protocol in which flooding exhibits a tendency to converge on a narrow strip of nodes along the shortest path between source and destination. The width of this strip can be adjusted automatically or by the user. We also point out a certain deficiency inherent in the IEEE 802.11 family of collision avoidance schemes in handling broadcast packets, and show how to fix it to provide better service to broadcast-based routing schemes represented by our variant of controlled flooding. Next, we consider the topology control problem whose objective is to minimize the amount of power needed to maintain connectivity. The issue boils down to selecting the optimum transmission power level at each node, based on the position information of reachable nodes. Local decisions regarding the transmission power level induce a subgraph of the maximum powered graph. We propose a new algorithm for constructing minimum-energy path-preserving subgraphs of the maximum powered graph. Routing protocols use another important service called 'broadcasting' for different purposes. The primary goal of any broadcast scheme is to reduce the total number of retransmissions needed to reach all nodes in the network. Another performance measure, which has not received as much attention, is the broadcast latency. We demonstrate that these two objectives, i.e., reducing the number of retransmissions, and reducing the latency, are contradictory and result in a trade off. We also show how to adjust the stochastic component of the popular class of contention resolution schemes based on IEEE 802.11 to significantly reduce the broadcast latency.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".