Hierarchical Geographic Multicast Routing for Wireless Sensor Networks
Bibliographic record
Abstract
Multicast is a fundamental routing service for efficient data dissemination required for activities such as code updates, task assignment and targeted queries in large-scale wireless sensor networks. Recently, two protocols were proposed to optimize two orthogonal aspects of location-based multicast protocols: GMR [1] improves the forwarding efficiency by exploiting the wireless multicast advantage but it suffers from scalability issues when dealing with large sensor networks; HRPM [2] reduces the encoding overhead by constructing a hierarchy at virtually no maintenance cost via the use of geographic hashing but it is energy-inefficient due to inefficacies in forwarding data packets. In this paper, we present HGMR (Hierarchical Geographic Multicast Routing), a new location-based multicast protocol that seamlessly incorporates the key design concepts of GMR and HRPM and optimizes them for wireless sensor networks by providing both forwarding efficiency (energy efficiency) as well as scalability to large networks. Our simulation studies in an ideal environment and in a realistic environment confirm that HGMR exhibits the strength of both GMR and HRPM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".